Podcasts about Next Big Sound

Here we are with Natalie Del Carmen. Natalie is a musician, songwriter, and artist from Los Angeles. She has carved a niche in the Folk-Americana genre with touches of Pop. A Berklee College of Music graduate at 20, she debuted with her 11-track album “Bloodline” in Spring 2023, exploring themes of love, diligence, and time, and recorded in Nashville with Brunjo. Her EP “Tandem Songs,” produced by Zack Burke and Brunjo, marks her shift towards Americana. Featured in Medium.com's “The Five Things You Need To Shine In The Music Industry,” Holler Country's “10 Upcoming Country & Americana Artists, Nov 2023,” and KCSN 88.5's “SoCal's Next Big Sound” competition, Del Carmen is making waves in the country music scene. In 2024, she delves into self-acceptance with “Good Morning From Magnolia” her latest single release. We will get to hear her advice in the music industry and her upcoming goals on touring.

David Hoffman built and sold big-data and data analytics company Next Big Sound to Pandora in 2015. He's now building Beam, which helps people create shoppable mood boards for DTC brands. David reflects on his experience with mentorship and the long arc of the conversation that is being a co-founder and being in community. We unpack the Techstars motto "give first" and discuss the power of the Techstars community and the importance of community relationships in entrepreneurship. We talk through the complex evolution that is founding and scaling a startup, his experience doing just that with Next Big Sound, and the challenges of becoming a leader inside a growing company. One challenge is always scaling culture as a company scales, and David outlines some of the routines and structures that helped in defining his startup's culture. David also shares some insights on the post-startup-sale emotional roller coaster and the decision to build another company. Some of my other favorite insights from David: Living the “Give First” motto requires approaching everything with curiosity. “Grown ups” is a construct: When it is your idea and your company, you can make the decisions you need to make. Your Culture is made of your routines, whether it's Friday bagels or snap-clapping after people share wins. Your MVP product can be much, much more simple than you think if it creates value for your customers. David's nuanced reflections are a gift, and I'm so glad he sat down for this conversation. Head over to theconversationfactory.com/listen for full episode transcripts, links, show notes  and more key quotes and ideas. You can also head over there and become a monthly supporter of the show for as little as $8 a month. You'll get complimentary access to exclusive workshops and resources that I only share with this circle of facilitators and leaders. Links Beam

No matter where you are in your career, you'll benefit from listening to 3Q. 3Q provides a window into the careers of some of the best in the music business. Every episode is an insider's view of the realities of life as a music executive. Topics include issues of empowerment, uncertainty, trust, finances, etc; issues that will impact you both personally and professionally. The executives we interview represent every aspect of the industry including but not limited to A&R, Marketing, Music Supervision, Artist Management, Promotion, and more. About Britnee: Britnee's known for being a multi-hyphenate music executive and data strategist with a passion for helping artists and creators make a living doing what they love. She currently serves as the Head of Data Strategy and Digital Operations at Excelration Music where she scales labels and music catalogs using data, as well as royalty collection, marketing, development, and discovery tactics. She's held positions at top companies like Downtown Music Holdings, Pandora, Next Big Sound, Universal Music Republic Group, SPIN Magazine, and many others. She's also worked as a sound engineer, artist manager, and public speaker. Last but certainly not least, she's the Global Co-Chair for Membership at Women in Music, an organization committed to advancing equality, visibility, and opportunities for women in the music industry.

David Hoffman is the CEO and Co-Founder of Beam, the easiest way to design and remodel your home. From the DIY'er, to the hands-off-I-need-to-hire, Beam provides you with all of the tools you need to remodel better. Previously, David co-founded Next Big Sound, which was named one of the 10 best music startups of 2010 by Billboard Magazine before the company was acquired by Pandora in 2015. When David isn't growing Beam, he's likely working on his own remodeling projects or making memories with his wife and two daughters. To learn more about Beam or to start your next remodeling project with ease, visit www.buildwithbeam.com Connect with Behind Company Lines and HireOtter Website Facebook Twitter LinkedIn:Behind Company LinesHireOtter Instagram Buzzsprout

Here at Idea to Startup we're obsessed with the process of filtering through and choosing ideas. Today we're joined by Alex White, previously the founder of The Next Big Sound (acquired by Pandora) and current founder of Subcity, and someone with as thoughtful and rigorous an approach around ideation as you'll find. We dig in on how to think through an idea, how to pursue it, and how to decide if it's worth your time. SubcityTackleboxFour Steps to the Epiphany

We continue along on our HHN Recap month interviews with Cody Carson from Set It Off. The LA based pop-punk reaches around 2.5 million monthly listeners and has appeared all over the Billboard Heatseakers, Next Big Sound and Top 200 list! Originally from FL, Cody has been a passionate HHN fan since he was young. Listen along as we dive into their band history a bit, past and upcoming tours, new releases and of course plenty of HHN talk! 

From Harvard to Broadway.  Today's friend of the podcast is LITERALLY teaching me the music game. Graduating from Harvard with a degree in Computer Science, she went on to work at Next Big Sound – a platform acquired by Pandora to provide social media, streaming service, and radio analytics.    Hailing from the Bay Area she started playing piano when she was four years old. Next? Did nothing short of taking the music world by storm. Some highlights along the way include HADESTOWN on Broadway to a recent streaming production of THE LAST FIVE YEARS from Out of the Box. Next up: a secret movie project.

In light of our exciting new Pandora integration, we chat with Pandora's Dan Wissinger and Jay Troop about why Pandora matters to the music industry and to artists’ careers, how artists can get their music on Pandora, and what strategies you can use to make sense of your Pandora data — whether it's on Next Big Sound, AMP, or Chartmetric.

Today, we are talking about traveling the world with babies in tow! I am so fortunate that David Hoffman joined me as a guest a few weeks ago to discuss his journey into parenthood. David is a former classmate of mine from Northwestern and before the pandemic set in, he and his wife Liv spent a lot of their time traveling while working and raising their two daughters. The goal of the show today is to share David’s perspective of traveling and living in different cities around the world with a toddler and a baby!In this episode we discuss: David’s background: co-founded Next Big Sound and after it was sold to Pandora, he met his wife, LivDavid and Liv’s adventures traveling with their newbornWhat it was like to live in Air BnBs with a babyTraveling and pregnancy during the time of ZikaLiving in Quito, Ecuador at the beginning of their second pregnancyHow to find a babysitter in a new city!How the pandemic has informed their decision for where they are living now.David and Liv’s baby gear recommendations: Books: Home Game by Michael Lewis for the big pictureMom's On Call for schedulesStroller: Bugaboo Cameleon for 1 kidUppababy Vista for 2 kids if you're in a city Thule Chariot for towing behind our bikes, taking on walks and hikesTravel Bassinet: Babyhome Bassinet Travel Crib: Guava Family Lotus Travel Crib White noise: Spotify White Noise RainBag: The KAOS Diaper Bag (my wife sells it on our store and it's our go-everywhere bag)Thermos: a good Thermos for milk! (clean it ASAP or it gets nasty)Travel Diaper Pails: Munchkin Toss Portable Disposable Diaper PailHow to get in touch with David: http://david-hoffman.com Blue House: Seriously, Liv’s shop is beautiful. Check it out. InstagramThank you so much for listening to the show today! To hear past episodes of the podcast, go to www.findingyourvillagepod.com or subscribe anywhere you listen to podcasts! Support the show (https://www.patreon.com/findingyourvillagepod?fan_landing=true)

In this live webinar about data in the music industry, Zak is joined by Alex White (founder of Next Big Sound and current VP, Content & Programming at Pandora), Britnee Foreman (Data analyst at Tempo Music) and Jason Joven (Content and Insights manager at Chartmetric).

Why are five thousand music industry pros from eighty countries descending on Cannes? Midem, the leading international event for the global music community, kicks off soon! We chat with Midem director Alexandre Deniot about Midem’s role in music business history, and how it’s shaping the industry’s future. Get a sneak peek at this year’s Midemlab, the music startup pitch competition that springboarded SoundCloud, Soundcharts, The Echo Nest, and Next Big Sound. Who will be 2019’s big success story? What are the music tech trends making waves at Midem this year, and what’s about to break out? Listen today to get Alexandre’s international perspective, and stay tuned for more upcoming Midem coverage! Podcast host Dmitri Vietze is in Cannes recording podcast interviews with Midemlab competitors and international music tech superstars. The Music Tectonics Podcast goes beneath the surface of the music industry to explore how technology is changing the way business gets done. The podcast includes news roundups, interviews, and more.

Over the years, a #GiveFirst network will reward its members time and time again. Troy Henikoff looks back at 2009, when he first encountered Techstars and was so inspired he started his own accelerator—with some help from the Techstars Network. A decade later, that accelerator is now Techstars Chicago, and Troy is Managing Director of MATH Ventures. You never know where #GiveFirst will take you—but you do know you’ll never have to go it alone.

We’re back with a special music-related analytics episode! Following Next Big Sound’s acquisition by Pandora, Julien Benatar moved from engineering into product management and is now responsible for the company’s analytics applications in the Creator Tools division. He and his team of engineers, data scientists and designers provide insights on how artists are performing on Pandora and how they can effectively grow their audience. This was a particularly fun interview for me since I have music playing on Pandora and occasionally use Next Big Sound’s analytics myself. Julien and I discussed: How Julien’s team accounts for designing for a huge range of customers (artists) that have wildly different popularity, song plays, and followers How the service generates benchmark values in order to make analytics more useful to artists How email notifications can be useful or counter-productive in analytics services How Julien thinks about the Data Pyramid when building out their platform Having a “North Star” and driving analytics toward customer action The types of predictive analytics Next Big Sound is doing Resources and Links: Julien Benatar on Twitter Next Big Sound website Next Big Sound blog The Data Pyramid model Quotes from Julien Benatar "I really hope we get to a point where people don’t need to be data analysts to look at data." "People don’t just want to look at numbers anymore, they want to be able to use numbers to make decisions." "One of our goals was to basically check every artist in the world and give them access to these tools and by checking millions of artists, it allows us to do some very good and very specific benchmarks" “The way it works is you can thumb up or thumb down songs. If you thumb up a song, you’re giving us a signal that this is something that you like and something you want to listen to more. That’s data that we give back to artists.” “I think the great thing today is that, compared to when Next Big Sound started in 2009, we don’t need to make a point for people to care about data. Everyone cares about data today.” Episode Transcript Brian: I’m really excited today for this episode. We have Julien Benatar on the show and he’s from a company that I’m sure a lot of people here know. You probably have had headphones on at your desk, at home, or wherever you are listening to Pandora for music. Julien , correct me if I’m wrong, you were the product manager for artist tools and insights at Next Big Sound, which is a type of data product that provides information on music listening stats to, I assume, artists’ labels as well to help them understand where their fans are and social media engagement. I love this topic. I’m also a musician, I have a profile on Next Big Sound and I feel music’s a fun way to talk about analytics and design as well because everybody can relate to the content and the domain. Welcome to the show. Did I get all that correct? Julien: Yeah, it was perfect. Brian: Cool. Tell us a little about your background. You’re from France originally? Julien: Yes, exactly. I grew up next to Paris, in Versailles more specifically, and moved to New York in 2014 to join Next Big Sound. Brian: Cool, nice. You’ve been there for about four years, something like that. You have a software engineering background and then now you’re on the product side, is that right? Julien: Exactly yes. I joined the company back when we were a startup. Software engineering was perfect, there was so much to do. To our move to Pandora, I moved to a product manager role around a year ago. Brian: Next Big Sound was independent and then they were acquired by Pandora. I assume there is good stuff about your data. Why did Pandora acquire you and how did they see you guys improving their service? Julien: We got acquired in 2015. The thing is, Next Big Sound was already really involved in the music industry. We already had clients like the three major labels and a lot of artists were using us to get access to their social data. I think it was a very natural move for Pandora as they wanted to get closer to creators and provide better analytics tools. Brian: For people that aren’t on the service, I always like to know who are the actual end users, the people logging in, not necessarily the management, but who sits down and what are some of the things that they would do? Who would log in to Next Big Sound and why? Julien: Honestly, it’s really anyone having any involvement into the music industry, so that can be an artist, obviously, try looking to try their socials and their audience on Pandora. But you can also be a booker trying to book artists in their town. We have a product that can really be used by many different user personas. But our core right now is really artists and labels, having contents on Pandora and trying to tell them the most compelling story about what they’re doing on the platform. Brian: When you think about designs, it’s hard to design and we talk about this on the mailing list sometimes but it’s really hard to design one great thing that’s perfect for everybody so usually you have to make some choices. Do you guys favor the artist, or the label, or as you call them,the bookers or whom I know as presenters,in the performing arts industry? Do you have a sweet spot, like you favor one of those in terms of experience? Julien: I think it’s something we’re moving towards, but it hasn’t always been this way. Like I told you, we used to be a startup or grow us to make a product that could work for as many people as possible. What is funny is we used to have an entity on Next Big Sound called Next Big Book where we used to provide the same type of service for the book industry. If anything, it’s been great to join Pandora because then we could really refocus on creators and it really allowed us to, I believe, create much better and more targeted analytics tools to really fulfill needs for specific people like artists and labels. Brian: I would assume individual artists are your biggest audience or is it really heavily used by the labels or who tends to... Julien: I think it’s pretty much the same honestly. I think the great thing today is that, compared to when Next Big Sound started in 2009, we don’t need to make a point for people to care about data. Everyone cares about data today. I think that everyone has reasons to look at their dashboards and especially for a platform like Pandora with millions of users every month. Our goal is really just telling them a story about what does it mean to be spinning on the platform and the opportunities it opens. Brian: You talked about opportunities, do you have any stories about a particular artist or a label that may have learned something from your data and maybe they wrote to you or you found out like in an interview how they reacted like, “Hey, we changed our tool routing,” or, “Hey, we decided to focus on this area instead of that area.” Do you know anything about how it’s been put into use in the wild? Julien: Yeah, it’s used for so many different reasons. For the people who don’t use Pandora, something I really like about the platform is it’s really about quality. As you use Pandora, you have the opportunity to thumb up or thumb down songs and as you do, you’re going to get recommended more songs like the ones you like. It’s really about making sure that you get the best songs at all times. The reality then is that for artists, their top songs on Pandora can be pretty different than their top songs on other platforms because sometimes their friends are going to be just reacting more to some part of their catalog than another one. I’ve heard many times of artists changing their playlists in looking at which songs where their fans thumbing up the most on Pandora. Brian: Could you go through that again? How would they adjust their playlist? Julien: Usually, people use Pandora as a radio service. While we already have internet today, most people are listening to the radio because they’re usually are very targeted and it just works really well. The way it works is you can thumb up or thumb down songs. If you thumb up a song, you’re giving us a signal that this is something that you like and something you want to listen to more. That’s data that we give back to artists. We tell them, “This are your most thumbed songs on Pandora. These are the songs that people engage with the most on the platform.” Looking at this data, you can actually inform them songs that they believe they should be playing more on the store. Brian: I see. A lot of it has to do with the favoriting aspect to give them idea what’s resonating with their audiences. Julien: Qualitative feedback, yes. Brian: Got it. Actually, it’s funny you mentioned the qualitative feedback. In preparation for this, I was reading an article that you guys put out back in March about a new feature called weekly performance insights, which is really cool and this actually reminds me of something that I talked about in the Designing for Analytics mailing list, which is the act of providing qualitative guides with your analytics. A lot of times they analyze for turnout quantitative data and whenever there’s an opportunity to put stuff into context or provide qualifiers, I think that’s a really good thing and you guys look like you’ve have done some really nice things here. I’ll paraphrase it and then you can jump in and maybe give us some backstory on it. One of the things that I think is really cool is there're concepts of normalcy in here so that, if I’m an artist and I look at my numbers, I have an idea. For your Twitter mentions, for example, you say, “For artists with 26,000 followers, we expect you to get around 44 mentions.” When you show me that I have 146 mentions, I can tell that I’m substantially higher than what my social group would be. I think that’s a really fantastic concept that people not in music could try to apply as well which is, are there normalcy bans where you’d want to sit? Is there some other type of group, maybe, an industry, or apparent group, or another business unit, whatever it may be to provide some context for what these out of the blue numbers mean that don’t have any context? How did you guys come up with that and can you tell us a bit about the design process of going from maybe just showing, “You’re at 826 apples,” as compared to what? How did you move from just a number into this these kind of logical groupings where you provide the comparisons? Julien: I think what’s really fascinating is, we really live in an age of data. As an artist, you need to be on social media for the most part. There still a lot of artists I listen to but just decide not to. It’s part of things but at the same time, real big success in the music industry didn’t change. It’s still being on the Billboard chart, getting a Grammy and all these things. But as we see this, we have millions of artists looking at their data every day and just are not able to understand, like is it good or is it not good. Everyone starts at zero. We have a strong belief that data can only be useful when put in context. Looking at the number on its own can give you a sense of how things are doing but that can also be dismissive. An example is, a very common way to look at data is to look at a number and look at the percent changing comparison to the previous week. You’ve got a bunch of tables and you look at, am I growing or am I not growing. The reality is it’s actually impossible to always have a positive percent change. There’s no artist in the world that always does better week by week. Even Beyonce, I can assure you that the week she released Lemonade, she had more engagement on Twitter than the week after. With that in mind, we really try to give a way for artists to understand how are they doing for who they are and where they are currently in their career. Next Big Sound started in 2009. One of our goals was to basically check every artist in the world and give them access to these tools and by checking millions of artists, it allows us to do some very good and very specific benchmarks. For an artist, like the example you said, for instance an artist with a thousand Twitter mentions in a week, is it good or bad in comparison to their audience size? This feature comes because that’s just the question we’re asked. Artists want to know is it any good? What does this number actually mean for me? That’s why we really wanted to, in some ways, get out of being a content aggregator platform and really be a data analytics platform. How can we actually give information that can help artist make better decisions? Brian: I remember the first time I got what I would call an anomaly detection email from your service and it was about some spike in YouTube views or something like that. I thought it’s fantastic in two reasons. First of all, you identify an anomalous change and I think in this case it’s a positive anomalous change. That tells me that I should log in the tool. Secondly, you proactively delivered that to me. On the Designing for Analytics mailing list, we talk about is that user experience does not necessarily live inside your web browser interface or your hard client or whatever you’re using to show your analytics. Email and notifications are a big part of that. Can you tell me about how you guys also arrived at when you pushed these things out and maybe talk about this little anomaly detection service that you have? Julien: It all started when we got acquired by Pandora. We decided to just invite a bunch of users and just talk to them, understand how to use our product and what did they think about it. We had artists, managers, and label people come over and we just talk to them and basically they all said, “We love it.” But then, by looking at their actual usage, they don’t use it that much. I guess one of their questions was when should I be looking at my data? Everyone is very busy. As you’re an artist, you need to perform, you need to write music, you need to engage with your fans and same goes with everyone. When should I look at data? The reality is by being a data company, we do get all the data, we have all the numbers. We have ways to know when things are supposed to be known, when artists should be acting on something. We just turn this into this email notifications. Anytime we notice that an artist is doing better than expected, we just let them know right away. Brian: That’s great. Do you do it on the opposite end too? If there’s an unexpected drop or maybe like, “Oh, you put a new track out and your socials dropped,” or something like that, do you look at the negative side too or do you tend to only promote the positive changes? Julien: As far as pushes, we decided to only do push for positive. But as you mentioned weekly performance, weekly performance can give you some negative insights, like, “You’re not doing as well as artists with the same size of audience as yours.” The reason we didn’t do it for our notification is, anomalies are really hard to completely control. A reason, for instance, is Twitter removing bots. Basically, every single artist would have had an email telling them, “You lost Twitter followers this week.” It was a lot of work to really tune our anomaly factor to actually only send emails when something legitimate happens. That’s the reason we only decided so far to do it for positive but we actually have been thinking about doing the same for negative but that’s another type of work. Brian: Yeah, you’re right. You have to mature these things over time. You don’t want to be a noise generator. Julien: Exactly. Brian: Too many, then people start to ignore you. I’ve seen that with other data products I’ve worked on which just have really dumb alerting mechanisms that are very binary or they’re set at a hard threshold and just shootout noise and people just tune it out. Julien: I’m glad you mentioned this because this feature was in beta for a year for that specific reason. Brian: Got it. Julien: We had to learn the hard way. We had like a hundred beta users. We’ve got way too many emails because anytime there were an anomaly anywhere, they would just get an email. For the most part, it was things that were supposed to help them. If a notification becomes noise, then that’s absolutely against its purpose. Brian: I don’t know if everybody knows how the music business works, at least from the popular music side, but just to summarize. You have individual artists that are actually performers. They may or may not have an artist manager which takes care of their business affairs, represents them like negotiations with people that book shows. Then you have labels which are sort of like an artist manager except they’re really focused on the recording assets that the artist makes and they actually tend to own the recordings outright at the beginning and then over time, the artist may recoup through sales they make it the ownership act and the sound recordings they make. Of those kinds of three major groups, is there a one that’s particularly hungry or you’re the squeaky wheel that is most interested in what you’re doing? Julien: I really think that into these three groups, we have a subset of users that are really into the data and into the actionability of it. I don’t think it’s one specific group of user. It could be all around the industry like we have the data-savvy, they really want to know. We have some users that actually would rather get more notifications even if they need to on their end to figure what is right from what is wrong. But since we have such a wide user base of different type of people, we decided to go on the conservative side and make sure to only share things that we thoroughly validated through all of our filters. Brian: I assume that your group reports into some division of Pandora, I’m not sure of that. Are you reporting into a technology, like an IT, or a business unit, or marketing? Where do you guys fit in the Pandora world? Julien: We’re part of the creator’s tools. I don’t really have a perfect answer to this. Brian: Okay. I guess my main question being, because when we talk about designing services, we talk about both user experience, which is the end user thing and about business success or organization success. I’m curious, how does Pandora measure that Next Big Sound as delivering value? I can understand, I’m sure our artist can understand how the artists value it through understanding how is my music moving my audiences, et cetera. Is there a way that Pandora looks at it? Are they interested in just time spent? The analytics on the analytics, so to speak, is what I’m asking about. How do you guys look at it like, “Hey, this is really doing a good job,” or whatever? Do you know how that’s looked at? Julien: To be honest, I think you said it right. Our goal is to help artists make their decisions through data and having artists use the platform is currently the way Pandora sees us doing a good job. Actually, it hasn’t changed that much since our acquisition. One of our main KPI for the past and couple of years is something I would call insights consumes. Just making sure that our users, artists, anyone using Next Big Sound are consuming data. That can be them logging into the website or that can be them opening one of our notifications. But so far that was our main KPI. We’re trying to work on some more targeted KPI, potentially like actions taken, that would be the North Star, but we're still working on how to do that right. Brian: Do you guys facilitate actions, so to speak, directly in the tool or are there things people can do with those actions really take place outside of the context of Next Big Sound? Julien: There are actions that artists can take to the other creator’s tools provided by Pandora. For instance, artists have the ability to send audio messages to anyone listening to them. If they go on tour into the US, they can have targeted messages in every single song they’re going to play. If anyone listens to them there, they can just click and buy a ticket. We’re working to make sure that artists are aware of these tools because they are free and they’re generally helping them grow at their careers. But regarding external actions, so far we don’t have any one-click way to tweet at the right time to the right people or with the right content or anything like this. Brian: Sure and that’s understood. Not every analytics product is going to have a direct actionable insight that comes right out of it. You guys may be feeling a longer term picture about trending and maybe for a certain artist to get an idea if they’re releasing music fairly frequently, what stuff is working and resonating, and what stuff is not. I can understand that. There may not be a button to click as a result immediately. Julien: That’s the goal though. Everything we do right now is going towards this objective. Maybe I can tell you a little about the way we think about data and that can give more sense to it. In order to work on any new feature, we follow this concept called the data pyramid. It’s something that you can Google. There’s a Wikipedia page for it. Let me explain to you how it works. The data pyramid, it’s a pyramid formed of four layers. It could be upon each other and each representing an exquisitely useful application of data. At the bottom of the pyramid we have the data layer. Any sort of data that we may have. For our case, Android data, Twitter, Facebook just getting the numbers, getting the raw data. On top of it, we have the information layer. The information layer is going to be ways you have to visualize this data. I guess it’s like the very broad sense of analytics. We’re going to give you tables, graphs, pie charts, you name it. We’re giving you ways to craft stories about this data but it’s on you to figure it out. Then on top of it we have what we call the knowledge layer. That’s where things start to get interesting. The knowledge layer is the contextual part of it. It’s like, “What do this number actually mean?” It has industry expertise. For instance, the way we’re going to work about it for musicians and their true data may be different than any other industry. The knowledge layer goes like a weekly performance. It’s a perfect answer to it. It’s what does it mean for me as a musician with a hundred fans to get two mentions this week. Same for notifications. It’s telling you that you should be looking at your data right now because something is happening. That’s how we get to the North Star and the last part of the data pyramid which is intelligence. The goal of intelligence is actionability. Now that I get to understand what does this number mean to the specific context, what should I be doing? Following your question, everything we’re trying to do here is to get to a point where we can just send an email to an artist and tell them, “Hey, you should be doing this right now because, with all the data that we have, we believe that this is going to have the highest impact for you.” Brian: It‘s really fascinating that you just outlined this data pyramid. I actually haven’t heard of this before. It made me think of one of the kind of, it’s not a joke but in the music community, I’m also a composer and when we write stuff, the kind of running joke is like nothing is new. Your ideas for this new song or this new melody I’m composing, it probably came before you. You heard it there before. I wrote a post on my list that was pretty much exactly the same thing except the knowledge layer. I was calling that insight. Data have been this raw format and information being the first human-readable format that’s like say going from raw data to a chart, a histogram. Now I have a line on a chart and then the insight layer being, I have a line on the chart and another line comparing it to like you said, average, or my social group, or a parent group, or some taxonomy, or an index. Then the action or the prescription for what to do or the prediction those that kind of lead you in about action which would be that fourth state. You’re like, “Oh, is this really a new concept?” It’s like, “Nope. Someone else already thought of that.” I totally want to go read about this data pyramid. Julien: That’s amazing. Brian: I’ll find that link to the data pyramid and I’ll put that in the show notes for sure. I thought that was really funny. Julien: It’s funny that you called it insight because that’s the way we call a lot of our features are working out. The way we define insight is bite-size, noteworthy, sharable content. How can we get into the noise of all of the data that only gives you exactly what you should be looking at. That’s how we got into notification and weekly performances. This is the one thing you should be looking at. Brian: I understand what you’re getting at there. The insights are, like you said, bite-size chunks of interesting stats that someone can put some kind of context around. That’s great and it’s good. One of the things I liked, too, that you talked about was you said, “Oh we got like a hundred users, like a beta group and that kind of inspired some of this.” Your product response to how do we help people know when to come and look at our service. I think this is really good because one of the problems that I see with clients and people on the list, I think is low engagement. This is especially true for internal analytics companies. Low engagement can be a symptom of a difficult product, it doesn’t provide the right information at the right time, it may not have a lot of utility, or it’s a resistance to change. People have done something the old way and they don't want to do it the new way. One of the recipes you can follow if you’re trying to do a redesign or increase engagement is to involve the people that are going to use the service in the design process, both the stakeholders as well as the end customers. This is especially true again for the internal analytics people. Your customers or other employees and your colleagues. By engaging them in the design process, they’re much more likely to want to change whatever they’re doing now. I loved how you guys did some research. Now I want to ask, do you frequently do either usability testing or interviews? Is that an ongoing thing at your company or is it really just in front of a big feature release or something like that? How do you guys do this research? Can you tell me about that? Julien: Of course. It’s consent. We haven’t released any major feature without doing some heavy user testing. I’m very lucky to be working with two designers, Justin and Anabelle who are very user-focused. Honestly, if you come to our office, at least every week we’re going to have some user interview and just talking to them, showing them prototypes, and just see how do they play with it. Brian: So you’re doing a lot of testing it sounds like. That’s fantastic. Julien: At the same time it’s always to find the right balance because you could be overtesting things too. We really are focusing on user testing for new things and make sure that the future that we are working on actually answers their user story that we intended. Brian: I don’t know how involved you get participating in these, but do you have any interesting stories or anecdotes that you got from one of those that you could share? Julien: Let me think. I do participate into a lot of them but I’m not sure I have an example right now. Brian: Are most of the people you interview, are they current users of Next Big Sound or do you tend to focus on maybe artists that haven’t experienced the service yet or you mix it up? Julien: We mix it up. We mostly engage with users that we already have but then we can decide to go with users that haven’t used the platform for a while, or more active users if you want to understand how we’re useful into their day to day. What I would say is that, surprisingly, it’s very easy to get users to chat about their experience with the product. I didn’t assume that we would get so many responses when we tried to have people come over or just hop on the zoom to check a new feature. Brian: I’m glad you actually mentioned that because I think in some places, recruiting is perceived to be difficult and it probably isn’t. Maybe you haven’t done it before but as I tell a lot of my clients, a lot of people love to have someone listen to them talk, tell them all about their life and what’s wrong with it, and how it could be better with their tools. They love having someone listen to them and especially if they know that their feedback is going to influence a tool or a service that they’re using. They tend to be pretty engaged with it. I find it’s really rare that I do an interview with a client’s customer and they don’t want to be included in the future round like, “Hey, when we redesign the service, can we come back to you and show you what we’ve done?” “Oh, I love to do that!” Everybody wants to get engaged with it. There are places where recruiting can be difficult when it’s hard to access the users, some of the enterprise software space that can be an issue sometimes. But generally, if you can get access to them, they tend to be pretty willing to participate. I’m glad you mentioned that. Julien: I think the great part about testing with current users on the platform is to actually show them prototypes with real data, not just show them an abstract idea that we want to work on. As soon as they can see what we’re working on apply to their own career as musicians, for instance, that can lead to fascinating discussions. Brian: You made a really good point on the real data thing. I remember as far back as 10 years ago or whenever, I use to work at Fidelity Investments, we would see this issue when we’re working on the retail site for investors. When you show a portfolio that, for example, has Apple stock trading at $22 in it, you’re not really there to test what is the price of Apple stock but you might be testing something entirely different and the customer cannot bear what is going on? They’re so stuck on this thing. It’s all fake seed data in the prototype. The story here being if you’re a listener, when you test it’s important to have at least realistic data. You don’t want to have noise in the test or whatever your studying or else you can end up on this tangent. Try to make the numbers looks somewhat realistic if you’re using quantitative data. In some cases, people can be taught to roleplay. Pretend you’re Drake or pretend you’re some big artist and then they can get their head around why they have billions of streams instead of thousands which they’re used to. Julien: Absolutely. That also helps us just build better products because the reality is we have a lot of artists with maybe 10 plays in a month. As we build visualizations like something that we built a line of looking at Drake’s data, it’s not going to work as intended for a smaller artist sometimes. Having real data involved as soon as possible into the design process has been such a game changer for us. We really have a multidisciplinary team involved into the research and design of everything we do. I’m working with a data scientist, data engineer, a web engineer, and designer on a daily basis. Obviously, we all have our things to do. But as we get into creating something new, we just make sure to have someone helping us get the real data, interview the right user, and just create prototypes as soon as possible. Working with prototypes is essential into building useful data analytics tools. Brian: Yes, you do learn a lot more with a working prototype. It’s not to say you can’t test with lower fidelity goods, especially early on but for a service like yours when the range of possible use both the personas and also you’ve got the Drakes of the world, big major label artist and then down to really small independents, it’s really important to have an idea how your charts are going to scale, and what’s going to happen with data. Even just small stuff like how many decimal points should you be showing on a mobile device, some of the numbers might cram up. Julien: Exactly. Brian: All this stuff that you never think, if you only look at one version of everything, you can end up with a mess. I’m glad that you brought that up. Julien: I couldn’t say better. The decimal is actually something that we’ve had to discover through real data. Brian: To all of you in the technical people out there, I will say this. If I’ve seen one trend with engineers, is they love precision and there’s a lot of times when there’s very unnecessary precision being added to numbers. Such as charts and histograms. Histograms are usually about the trend, they’re not about identifying what was the precise value on this date at this time. It’s about the change over time. Showing what’s my portfolio worth down to three digits of micro-cents or something like that is just unnecessary detail. You can probably just round up to the dollar or even hundreds of dollars or even thousands of dollars in some cases. It actually is worse. The reason it’s worse is that adds unnecessary noise to the interface, you’re providing all these inks that someone has to mentally process, and it’s actually not really meaningful ink because the change is what’s important. Think about precision when you’re printing values. Julien: This concept of noise is so essential today for any data analytics tools. There is so much data today. There is data for everything. I think it’s our responsibility as a data analytics company to make sure what are we actually trying to help our user with this data set is not just about adding new metrics. Adding new metrics usually is just going to add noise and not be helpful in comparison to fairing what do they need to make the right decision. Brian: Right. Complexity obviously goes up. The single verb, ‘add,’ as soon as you do that, you’re generally adding complexity. One of the design tools that is not used a lot, and this is something I try to help clients with is, what can we take away? If we're not going to cut it out entirely, can we move this feature, maybe this comparison to a different level of detail? Maybe it’s hidden behind a button click, or it’s not the default. But removing some stuff is a way to obviously simplify as well, especially if you do need to add new things. Your only weapon is not the pencil, you’ve got the eraser as well in the battle so to speak. Julien: I couldn’t agree more. On Next Big Sound we have this concept of artist stages. It’s a way for us to put artist into buckets and by looking at their social instrument data. It goes from undiscovered to epic. We do that by looking at all of the data we have and looking at it in context. I don’t have the numbers right now because they update on a daily basis but every artist starts undiscovered. For instance, as they get 1000 Facebook likes, maybe they’re going to get to a promising stage. We have all of these thresholds moving everyday looking at trends among social services. But what is interesting is that for instance, for a booker, a booker doesn’t need to look at the exact number of Twitter followers for an artist. He needs to know that he’s booking for a midsized venue in the city he’s in and he’s probably going to be looking for promising to established artists and not looking for the mainstream to epic artists. It’s always about figuring a way to use the numbers to tell the story. Brian: I’m totally selfishly asking for myself here, but I was immediately curious. I live in Cambridge which is in the Boston area, and I am curious who are the big artists in our area and what is the concentration? I’m in a niche. I’m more in the performing arts market, in the jazz, in world music, and classical music but I’m just curious. Is there a way to look at it by the city and know what your artist community looks like? You guys do anything like that? Julien: We don’t currently. But I think YouTube has actually a C-level chart available. It’s not part of something we do because I think the users it would benefit are not the users we specifically try to work on new features. It’s more something for bookers than artists ,specifically ,but it’s exactly the type of thing that we need to think about when we prioritize new features. Brian: I’m curious just because the topic’s fairly hot. Everybody is trying to do machine learning projects these days. I don’t like the term AI because it tends to be a little bit overloaded but are you guys using machine learning to accomplish any particular problems or add any new value to your service right now? Is that on your horizon? Julien: How do you think about machine learning? Brian: A lot of times I associate it with predictive analytics or understanding where you might be running instead of just using statistics. I don’t know what kind of data you might have for your learning that you can feed in but maybe there’s aspects about artists that can predict. Especially, I would think like in the pop music world where there tends to be more commercialization of the music, I would say, where it’s like we need a two-minute dance track at this tempo specifically because DJs are going to play it. It’s a very commercial thing. It’s very different than what I’m used to. So I’m curious if there’s a way to predict out how an artist may do or what kinds of tracks are performing well. Like these tempo songs, we predict over the next six months that tech house music at 160 beats for a minute is going to do really well based on the trending. I don’t know. I’m throwing stuff out there. The goal, obviously, is not to try to use like, “Oh Home Depot has this new hammer, let’s run out and get it. We don’t even know what it’s for but everyone else is buying it.” That’s how I joke about machine learning. It’s like you need to have a problem that necessitates that particular tool. I don’t ask such that, “Oh there should be some.” I’m more curious as to whether or not it’s a tool that you guys are leveraging at this time. Julien: The Next Big Sound team doesn’t worked on features following the musical aspects of things. We really are focused on the user data. Brian: Engagement and social. Julien: Engagement data mostly, yes. But at the same time, I’m sure teams have worked on this because of the way that genome works. We have a lot of data about the way songs are made. Regarding machine learning, on the Next Big Song team, we actually have something that is called the prediction chart. You said predictions. We have this chart that is available every week. Basically, it really goes back to having data for a long time. The fact that we’ve had data since 2009, we’ve been able to see artists actually get from starting to charting on the Billboard 200. By having all of these data, we’ve been able to see some trends, some things that usually happen for artists at specific times in their career up until they get into the Billboard 200. We actually do have some algorithms that allow us to apply this learning to all of the artists on Next Big Sound right now and have a list every week of artist that we believe are most likely to appear on the Billboard 200 chart next year. Brian: I see. Got it. Do you track your accuracy rate on that internally and change it over time? Do you adjust the model? Julien: Yeah, we do. Brian: Cool That’s really neat. Tell me, this chat has been super fun. I’ve selfishly got a little indulgent because being a musician, it’s fun to talk about these two worlds that I’m really passionate about so I could go on forever with you about this. But I’m curious. Do you have any advice for other product managers or analytics practitioners about how to design good data products and services? How to make either your own organization happy or your customers happy? Do you have any advice to them? Julien: Yeah, of course. I guess it’s all about asking questions, honestly. What is very good with working at Next Big Sound is that it all started in 2009. Maybe actually I can go back and tell you the story about how it started and why it’s so different today. It started in 2009. It was actually a project, a university project by the three co-founders. Basically, they were wondering about one thing. How many plays does a major artist get on the biggest music platform in the world? At that time, it was MySpace. The artist they picked was Akon. Basically, they just built a crawler, went to bed, woke up, and discovered that an artist like Akon was getting 500,000 plays on MySpace in one night in 2009. The challenge in 2009 was to get the data. That’s why for the most part in Next Big Sound as it started was, I really think a data aggregation tool. Our goal was to get as many sources as possible and just make them easily accessible into the same place. We really are much into the information layer here. We’re giving you all the numbers and you can compare Tumblr to Vimeo, to YouTube, to Twitter, to Facebook, to Vine, to you name it into a table or a graph that you want to. The reality is, today things change. We don't need to fight to get data anymore. We don’t need to hike our way into getting the numbers. Now, data is accessible to everyone in a very easy way. It’s kind of a contract. You, by being an artist, you know you’re going to get access to your Spotify, YouTube, Pandora, Apple Music or any other platform data very easily just by signing up and authenticating as an artist. That’s where our goal changes. Thankfully, we don’t need to convince people to care about data, we know they do already. But now the challenge is different. Now, the challenge is to make them understand what does their data mean and how can they turn it into getting even more data, getting into having even more engagement, and having even more plays. I think that’s something that is very interesting because it really resonates into the question we’ve been asked in the past few years like, “What does my data mean and when should I be looking at my data?” If anything, these two things correlated pretty well. People don’t just want to look at numbers anymore, they want to be able to use numbers to make decisions. That’s the core of what we’re trying to achieve today. We couldn’t be there if we didn’t have users that ask us the right questions. Brian: Cool that’s really insightful. Just to maybe tie it off at the end and maybe you can’t share this but what’s your home run? What is your holy grail look like? Is there a place you guys know you want to get? Maybe it’s the lack of data or you don’t have access to the data in order to provide that service. Do you guys have kind of a picture of where it is you want to take the service? Julien: What is very noble about our goal at Next Big Sound specifically is we’re here to help artists. The North Star would be to make sure that any artist at any time in their career is doing everything they can do to play more shows, to reach to more people, and to make sure their music is heard. Brian: Nice. I guess it’s like you’re already there, just maybe the level of quality and improving that experience over time, that’s your goal. It’s not so much that there’s so much unobtainable thing at this moment. Is that kind of how you see it? Julien: I think the more we don’t feel just a data analytics tool, the more we’re getting to that goal. I really hope we get to a point where people don’t need to be data analysts to look at data. We’re always going to provide a very customizable tool for the data-savvy because they know what they need more than we can ever do it for them. We want to make sure that for everyone else, we can just make it very easy and as simple as a click for them to do something that’s going to impact them positively. Brian: Cool, man. This has been really exciting to have you on the show. Julien, can you tell the listeners where can they find you on the interwebs? Are you on Twitter or LinkedIn? How do they find you? Julien: For sure. @julienbenatar on Twitter, nextbigsound.com is free for everyone. Actually, we made our data public recently, so if you ever want to learn more about what we do, please check it out. We try to post on our blog about what we learn through data science, through design, and share more about why we build what we build. I recommend to just check blog and do some commitment to learn more about what we do. Brian: I definitely recommend people check out the site. The fun thing is again, as you said, it’s public. If there’s a band you like or whatever, you can type in any group that you like to listen to and you can get access to those insights. Just kind of get a flavor of what the service does. I’ll put those links in the show notes as well as the data pyramid. Julien, cool. Thanks for coming on. Is there anything else do you like to add before we wrap it up? Julien: No, thank you so much. I love reading your newsletters and I’m very happy to be here. Brian: Cool. Thank you so much. Let’s do it again. Julien: Cool. Brian: Cool. Thank you. We hope you enjoyed this episode of Experiencing Data with Brian O’Neill. If you did enjoy it, please consider sharing it with #experiencingdata. To get future podcast updates or to subscribe to Brian’s mailing list where he shares his insights on designing valuable enterprise data products and applications, visit designingforanalytics.com/podcast. 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We’re back with a special music-related analytics episode! Following Next Big Sound’s acquisition by Pandora, Julien Benatar moved from engineering into product management and is now responsible for the company’s analytics applications in the Creator Tools division. He and his team of engineers, data scientists and designers provide insights on how artists are performing on Pandora and how they can effectively grow their audience. This was a particularly fun interview for me since I have music playing on Pandora and occasionally use Next Big Sound’s analytics myself. Julien and I discussed: How Julien’s team accounts for designing for a huge range of customers (artists) that have wildly different popularity, song plays, and followers How the service generates benchmark values in order to make analytics more useful to artists How email notifications can be useful or counter-productive in analytics services How Julien thinks about the Data Pyramid when building out their platform Having a “North Star” and driving analytics toward customer action The types of predictive analytics Next Big Sound is doing Resources and Links: Julien Benatar on Twitter Next Big Sound website Next Big Sound blog The Data Pyramid model Quotes from Julien Benatar “I really hope we get to a point where people don’t need to be data analysts to look at data.” “People don’t just want to look at numbers anymore, they want to be able to use numbers to make decisions.” “One of our goals was to basically check every artist in the world and give them access to these tools and by checking millions of artists, it allows us to do some very good and very specific benchmarks” “The way it works is you can thumb up or thumb down songs. If you thumb up a song, you’re giving us a signal that this is something that you like and something you want to listen to more. That’s data that we give back to artists.” “I think the great thing today is that, compared to when Next Big Sound started in 2009, we don’t need to make a point for people to care about data. Everyone cares about data today.” Episode Transcript Brian: I’m really excited today for this episode. We have Julien Benatar on the show and he’s from a company that I’m sure a lot of people here know. You probably have had headphones on at your desk, at home, or wherever you are listening to Pandora for music. Julien , correct me if I’m wrong, you were the product manager for artist tools and insights at Next Big Sound, which is a type of data product that provides information on music listening stats to, I assume, artists’ labels as well to help them understand where their fans are and social media engagement. I love this topic. I’m also a musician, I have a profile on Next Big Sound and I feel music’s a fun way to talk about analytics and design as well because everybody can relate to the content and the domain. Welcome to the show. Did I get all that correct? Julien: Yeah, it was perfect. Brian: Cool. Tell us a little about your background. You’re from France originally? Julien: Yes, exactly. I grew up next to Paris, in Versailles more specifically, and moved to New York in 2014 to join Next Big Sound. Brian: Cool, nice. You’ve been there for about four years, something like that. You have a software engineering background and then now you’re on the product side, is that right? Julien: Exactly yes. I joined the company back when we were a startup. Software engineering was perfect, there was so much to do. To our move to Pandora, I moved to a product manager role around a year ago. Brian: Next Big Sound was independent and then they were acquired by Pandora. I assume there is good stuff about your data. Why did Pandora acquire you and how did they see you guys improving their service? Julien: We got acquired in 2015. The thing is, Next Big Sound was already really involved in the music industry. We already had clients like the three major labels and a lot of artists were using us to get access to their social data. I think it was a very natural move for Pandora as they wanted to get closer to creators and provide better analytics tools. Brian: For people that aren’t on the service, I always like to know who are the actual end users, the people logging in, not necessarily the management, but who sits down and what are some of the things that they would do? Who would log in to Next Big Sound and why? Julien: Honestly, it’s really anyone having any involvement into the music industry, so that can be an artist, obviously, try looking to try their socials and their audience on Pandora. But you can also be a booker trying to book artists in their town. We have a product that can really be used by many different user personas. But our core right now is really artists and labels, having contents on Pandora and trying to tell them the most compelling story about what they’re doing on the platform. Brian: When you think about designs, it’s hard to design and we talk about this on the mailing list sometimes but it’s really hard to design one great thing that’s perfect for everybody so usually you have to make some choices. Do you guys favor the artist, or the label, or as you call them,the bookers or whom I know as presenters,in the performing arts industry? Do you have a sweet spot, like you favor one of those in terms of experience? Julien: I think it’s something we’re moving towards, but it hasn’t always been this way. Like I told you, we used to be a startup or grow us to make a product that could work for as many people as possible. What is funny is we used to have an entity on Next Big Sound called Next Big Book where we used to provide the same type of service for the book industry. If anything, it’s been great to join Pandora because then we could really refocus on creators and it really allowed us to, I believe, create much better and more targeted analytics tools to really fulfill needs for specific people like artists and labels. Brian: I would assume individual artists are your biggest audience or is it really heavily used by the labels or who tends to… Julien: I think it’s pretty much the same honestly. I think the great thing today is that, compared to when Next Big Sound started in 2009, we don’t need to make a point for people to care about data. Everyone cares about data today. I think that everyone has reasons to look at their dashboards and especially for a platform like Pandora with millions of users every month. Our goal is really just telling them a story about what does it mean to be spinning on the platform and the opportunities it opens. Brian: You talked about opportunities, do you have any stories about a particular artist or a label that may have learned something from your data and maybe they wrote to you or you found out like in an interview how they reacted like, “Hey, we changed our tool routing,” or, “Hey, we decided to focus on this area instead of that area.” Do you know anything about how it’s been put into use in the wild? Julien: Yeah, it’s used for so many different reasons. For the people who don’t use Pandora, something I really like about the platform is it’s really about quality. As you use Pandora, you have the opportunity to thumb up or thumb down songs and as you do, you’re going to get recommended more songs like the ones you like. It’s really about making sure that you get the best songs at all times. The reality then is that for artists, their top songs on Pandora can be pretty different than their top songs on other platforms because sometimes their friends are going to be just reacting more to some part of their catalog than another one. I’ve heard many times of artists changing their playlists in looking at which songs where their fans thumbing up the most on Pandora. Brian: Could you go through that again? How would they adjust their playlist? Julien: Usually, people use Pandora as a radio service. While we already have internet today, most people are listening to the radio because they’re usually are very targeted and it just works really well. The way it works is you can thumb up or thumb down songs. If you thumb up a song, you’re giving us a signal that this is something that you like and something you want to listen to more. That’s data that we give back to artists. We tell them, “This are your most thumbed songs on Pandora. These are the songs that people engage with the most on the platform.” Looking at this data, you can actually inform them songs that they believe they should be playing more on the store. Brian: I see. A lot of it has to do with the favoriting aspect to give them idea what’s resonating with their audiences. Julien: Qualitative feedback, yes. Brian: Got it. Actually, it’s funny you mentioned the qualitative feedback. In preparation for this, I was reading an article that you guys put out back in March about a new feature called weekly performance insights, which is really cool and this actually reminds me of something that I talked about in the Designing for Analytics mailing list, which is the act of providing qualitative guides with your analytics. A lot of times they analyze for turnout quantitative data and whenever there’s an opportunity to put stuff into context or provide qualifiers, I think that’s a really good thing and you guys look like you’ve have done some really nice things here. I’ll paraphrase it and then you can jump in and maybe give us some backstory on it. One of the things that I think is really cool is there’re concepts of normalcy in here so that, if I’m an artist and I look at my numbers, I have an idea. For your Twitter mentions, for example, you say, “For artists with 26,000 followers, we expect you to get around 44 mentions.” When you show me that I have 146 mentions, I can tell that I’m substantially higher than what my social group would be. I think that’s a really fantastic concept that people not in music could try to apply as well which is, are there normalcy bans where you’d want to sit? Is there some other type of group, maybe, an industry, or apparent group, or another business unit, whatever it may be to provide some context for what these out of the blue numbers mean that don’t have any context? How did you guys come up with that and can you tell us a bit about the design process of going from maybe just showing, “You’re at 826 apples,” as compared to what? How did you move from just a number into this these kind of logical groupings where you provide the comparisons? Julien: I think what’s really fascinating is, we really live in an age of data. As an artist, you need to be on social media for the most part. There still a lot of artists I listen to but just decide not to. It’s part of things but at the same time, real big success in the music industry didn’t change. It’s still being on the Billboard chart, getting a Grammy and all these things. But as we see this, we have millions of artists looking at their data every day and just are not able to understand, like is it good or is it not good. Everyone starts at zero. We have a strong belief that data can only be useful when put in context. Looking at the number on its own can give you a sense of how things are doing but that can also be dismissive. An example is, a very common way to look at data is to look at a number and look at the percent changing comparison to the previous week. You’ve got a bunch of tables and you look at, am I growing or am I not growing. The reality is it’s actually impossible to always have a positive percent change. There’s no artist in the world that always does better week by week. Even Beyonce, I can assure you that the week she released Lemonade, she had more engagement on Twitter than the week after. With that in mind, we really try to give a way for artists to understand how are they doing for who they are and where they are currently in their career. Next Big Sound started in 2009. One of our goals was to basically check every artist in the world and give them access to these tools and by checking millions of artists, it allows us to do some very good and very specific benchmarks. For an artist, like the example you said, for instance an artist with a thousand Twitter mentions in a week, is it good or bad in comparison to their audience size? This feature comes because that’s just the question we’re asked. Artists want to know is it any good? What does this number actually mean for me? That’s why we really wanted to, in some ways, get out of being a content aggregator platform and really be a data analytics platform. How can we actually give information that can help artist make better decisions? Brian: I remember the first time I got what I would call an anomaly detection email from your service and it was about some spike in YouTube views or something like that. I thought it’s fantastic in two reasons. First of all, you identify an anomalous change and I think in this case it’s a positive anomalous change. That tells me that I should log in the tool. Secondly, you proactively delivered that to me. On the Designing for Analytics mailing list, we talk about is that user experience does not necessarily live inside your web browser interface or your hard client or whatever you’re using to show your analytics. Email and notifications are a big part of that. Can you tell me about how you guys also arrived at when you pushed these things out and maybe talk about this little anomaly detection service that you have? Julien: It all started when we got acquired by Pandora. We decided to just invite a bunch of users and just talk to them, understand how to use our product and what did they think about it. We had artists, managers, and label people come over and we just talk to them and basically they all said, “We love it.” But then, by looking at their actual usage, they don’t use it that much. I guess one of their questions was when should I be looking at my data? Everyone is very busy. As you’re an artist, you need to perform, you need to write music, you need to engage with your fans and same goes with everyone. When should I look at data? The reality is by being a data company, we do get all the data, we have all the numbers. We have ways to know when things are supposed to be known, when artists should be acting on something. We just turn this into this email notifications. Anytime we notice that an artist is doing better than expected, we just let them know right away. Brian: That’s great. Do you do it on the opposite end too? If there’s an unexpected drop or maybe like, “Oh, you put a new track out and your socials dropped,” or something like that, do you look at the negative side too or do you tend to only promote the positive changes? Julien: As far as pushes, we decided to only do push for positive. But as you mentioned weekly performance, weekly performance can give you some negative insights, like, “You’re not doing as well as artists with the same size of audience as yours.” The reason we didn’t do it for our notification is, anomalies are really hard to completely control. A reason, for instance, is Twitter removing bots. Basically, every single artist would have had an email telling them, “You lost Twitter followers this week.” It was a lot of work to really tune our anomaly factor to actually only send emails when something legitimate happens. That’s the reason we only decided so far to do it for positive but we actually have been thinking about doing the same for negative but that’s another type of work. Brian: Yeah, you’re right. You have to mature these things over time. You don’t want to be a noise generator. Julien: Exactly. Brian: Too many, then people start to ignore you. I’ve seen that with other data products I’ve worked on which just have really dumb alerting mechanisms that are very binary or they’re set at a hard threshold and just shootout noise and people just tune it out. Julien: I’m glad you mentioned this because this feature was in beta for a year for that specific reason. Brian: Got it. Julien: We had to learn the hard way. We had like a hundred beta users. We’ve got way too many emails because anytime there were an anomaly anywhere, they would just get an email. For the most part, it was things that were supposed to help them. If a notification becomes noise, then that’s absolutely against its purpose. Brian: I don’t know if everybody knows how the music business works, at least from the popular music side, but just to summarize. You have individual artists that are actually performers. They may or may not have an artist manager which takes care of their business affairs, represents them like negotiations with people that book shows. Then you have labels which are sort of like an artist manager except they’re really focused on the recording assets that the artist makes and they actually tend to own the recordings outright at the beginning and then over time, the artist may recoup through sales they make it the ownership act and the sound recordings they make. Of those kinds of three major groups, is there a one that’s particularly hungry or you’re the squeaky wheel that is most interested in what you’re doing? Julien: I really think that into these three groups, we have a subset of users that are really into the data and into the actionability of it. I don’t think it’s one specific group of user. It could be all around the industry like we have the data-savvy, they really want to know. We have some users that actually would rather get more notifications even if they need to on their end to figure what is right from what is wrong. But since we have such a wide user base of different type of people, we decided to go on the conservative side and make sure to only share things that we thoroughly validated through all of our filters. Brian: I assume that your group reports into some division of Pandora, I’m not sure of that. Are you reporting into a technology, like an IT, or a business unit, or marketing? Where do you guys fit in the Pandora world? Julien: We’re part of the creator’s tools. I don’t really have a perfect answer to this. Brian: Okay. I guess my main question being, because when we talk about designing services, we talk about both user experience, which is the end user thing and about business success or organization success. I’m curious, how does Pandora measure that Next Big Sound as delivering value? I can understand, I’m sure our artist can understand how the artists value it through understanding how is my music moving my audiences, et cetera. Is there a way that Pandora looks at it? Are they interested in just time spent? The analytics on the analytics, so to speak, is what I’m asking about. How do you guys look at it like, “Hey, this is really doing a good job,” or whatever? Do you know how that’s looked at? Julien: To be honest, I think you said it right. Our goal is to help artists make their decisions through data and having artists use the platform is currently the way Pandora sees us doing a good job. Actually, it hasn’t changed that much since our acquisition. One of our main KPI for the past and couple of years is something I would call insights consumes. Just making sure that our users, artists, anyone using Next Big Sound are consuming data. That can be them logging into the website or that can be them opening one of our notifications. But so far that was our main KPI. We’re trying to work on some more targeted KPI, potentially like actions taken, that would be the North Star, but we’re still working on how to do that right. Brian: Do you guys facilitate actions, so to speak, directly in the tool or are there things people can do with those actions really take place outside of the context of Next Big Sound? Julien: There are actions that artists can take to the other creator’s tools provided by Pandora. For instance, artists have the ability to send audio messages to anyone listening to them. If they go on tour into the US, they can have targeted messages in every single song they’re going to play. If anyone listens to them there, they can just click and buy a ticket. We’re working to make sure that artists are aware of these tools because they are free and they’re generally helping them grow at their careers. But regarding external actions, so far we don’t have any one-click way to tweet at the right time to the right people or with the right content or anything like this. Brian: Sure and that’s understood. Not every analytics product is going to have a direct actionable insight that comes right out of it. You guys may be feeling a longer term picture about trending and maybe for a certain artist to get an idea if they’re releasing music fairly frequently, what stuff is working and resonating, and what stuff is not. I can understand that. There may not be a button to click as a result immediately. Julien: That’s the goal though. Everything we do right now is going towards this objective. Maybe I can tell you a little about the way we think about data and that can give more sense to it. In order to work on any new feature, we follow this concept called the data pyramid. It’s something that you can Google. There’s a Wikipedia page for it. Let me explain to you how it works. The data pyramid, it’s a pyramid formed of four layers. It could be upon each other and each representing an exquisitely useful application of data. At the bottom of the pyramid we have the data layer. Any sort of data that we may have. For our case, Android data, Twitter, Facebook just getting the numbers, getting the raw data. On top of it, we have the information layer. The information layer is going to be ways you have to visualize this data. I guess it’s like the very broad sense of analytics. We’re going to give you tables, graphs, pie charts, you name it. We’re giving you ways to craft stories about this data but it’s on you to figure it out. Then on top of it we have what we call the knowledge layer. That’s where things start to get interesting. The knowledge layer is the contextual part of it. It’s like, “What do this number actually mean?” It has industry expertise. For instance, the way we’re going to work about it for musicians and their true data may be different than any other industry. The knowledge layer goes like a weekly performance. It’s a perfect answer to it. It’s what does it mean for me as a musician with a hundred fans to get two mentions this week. Same for notifications. It’s telling you that you should be looking at your data right now because something is happening. That’s how we get to the North Star and the last part of the data pyramid which is intelligence. The goal of intelligence is actionability. Now that I get to understand what does this number mean to the specific context, what should I be doing? Following your question, everything we’re trying to do here is to get to a point where we can just send an email to an artist and tell them, “Hey, you should be doing this right now because, with all the data that we have, we believe that this is going to have the highest impact for you.” Brian: It‘s really fascinating that you just outlined this data pyramid. I actually haven’t heard of this before. It made me think of one of the kind of, it’s not a joke but in the music community, I’m also a composer and when we write stuff, the kind of running joke is like nothing is new. Your ideas for this new song or this new melody I’m composing, it probably came before you. You heard it there before. I wrote a post on my list that was pretty much exactly the same thing except the knowledge layer. I was calling that insight. Data have been this raw format and information being the first human-readable format that’s like say going from raw data to a chart, a histogram. Now I have a line on a chart and then the insight layer being, I have a line on the chart and another line comparing it to like you said, average, or my social group, or a parent group, or some taxonomy, or an index. Then the action or the prescription for what to do or the prediction those that kind of lead you in about action which would be that fourth state. You’re like, “Oh, is this really a new concept?” It’s like, “Nope. Someone else already thought of that.” I totally want to go read about this data pyramid. Julien: That’s amazing. Brian: I’ll find that link to the data pyramid and I’ll put that in the show notes for sure. I thought that was really funny. Julien: It’s funny that you called it insight because that’s the way we call a lot of our features are working out. The way we define insight is bite-size, noteworthy, sharable content. How can we get into the noise of all of the data that only gives you exactly what you should be looking at. That’s how we got into notification and weekly performances. This is the one thing you should be looking at. Brian: I understand what you’re getting at there. The insights are, like you said, bite-size chunks of interesting stats that someone can put some kind of context around. That’s great and it’s good. One of the things I liked, too, that you talked about was you said, “Oh we got like a hundred users, like a beta group and that kind of inspired some of this.” Your product response to how do we help people know when to come and look at our service. I think this is really good because one of the problems that I see with clients and people on the list, I think is low engagement. This is especially true for internal analytics companies. Low engagement can be a symptom of a difficult product, it doesn’t provide the right information at the right time, it may not have a lot of utility, or it’s a resistance to change. People have done something the old way and they don’t want to do it the new way. One of the recipes you can follow if you’re trying to do a redesign or increase engagement is to involve the people that are going to use the service in the design process, both the stakeholders as well as the end customers. This is especially true again for the internal analytics people. Your customers or other employees and your colleagues. By engaging them in the design process, they’re much more likely to want to change whatever they’re doing now. I loved how you guys did some research. Now I want to ask, do you frequently do either usability testing or interviews? Is that an ongoing thing at your company or is it really just in front of a big feature release or something like that? How do you guys do this research? Can you tell me about that? Julien: Of course. It’s consent. We haven’t released any major feature without doing some heavy user testing. I’m very lucky to be working with two designers, Justin and Anabelle who are very user-focused. Honestly, if you come to our office, at least every week we’re going to have some user interview and just talking to them, showing them prototypes, and just see how do they play with it. Brian: So you’re doing a lot of testing it sounds like. That’s fantastic. Julien: At the same time it’s always to find the right balance because you could be overtesting things too. We really are focusing on user testing for new things and make sure that the future that we are working on actually answers their user story that we intended. Brian: I don’t know how involved you get participating in these, but do you have any interesting stories or anecdotes that you got from one of those that you could share? Julien: Let me think. I do participate into a lot of them but I’m not sure I have an example right now. Brian: Are most of the people you interview, are they current users of Next Big Sound or do you tend to focus on maybe artists that haven’t experienced the service yet or you mix it up? Julien: We mix it up. We mostly engage with users that we already have but then we can decide to go with users that haven’t used the platform for a while, or more active users if you want to understand how we’re useful into their day to day. What I would say is that, surprisingly, it’s very easy to get users to chat about their experience with the product. I didn’t assume that we would get so many responses when we tried to have people come over or just hop on the zoom to check a new feature. Brian: I’m glad you actually mentioned that because I think in some places, recruiting is perceived to be difficult and it probably isn’t. Maybe you haven’t done it before but as I tell a lot of my clients, a lot of people love to have someone listen to them talk, tell them all about their life and what’s wrong with it, and how it could be better with their tools. They love having someone listen to them and especially if they know that their feedback is going to influence a tool or a service that they’re using. They tend to be pretty engaged with it. I find it’s really rare that I do an interview with a client’s customer and they don’t want to be included in the future round like, “Hey, when we redesign the service, can we come back to you and show you what we’ve done?” “Oh, I love to do that!” Everybody wants to get engaged with it. There are places where recruiting can be difficult when it’s hard to access the users, some of the enterprise software space that can be an issue sometimes. But generally, if you can get access to them, they tend to be pretty willing to participate. I’m glad you mentioned that. Julien: I think the great part about testing with current users on the platform is to actually show them prototypes with real data, not just show them an abstract idea that we want to work on. As soon as they can see what we’re working on apply to their own career as musicians, for instance, that can lead to fascinating discussions. Brian: You made a really good point on the real data thing. I remember as far back as 10 years ago or whenever, I use to work at Fidelity Investments, we would see this issue when we’re working on the retail site for investors. When you show a portfolio that, for example, has Apple stock trading at $22 in it, you’re not really there to test what is the price of Apple stock but you might be testing something entirely different and the customer cannot bear what is going on? They’re so stuck on this thing. It’s all fake seed data in the prototype. The story here being if you’re a listener, when you test it’s important to have at least realistic data. You don’t want to have noise in the test or whatever your studying or else you can end up on this tangent. Try to make the numbers looks somewhat realistic if you’re using quantitative data. In some cases, people can be taught to roleplay. Pretend you’re Drake or pretend you’re some big artist and then they can get their head around why they have billions of streams instead of thousands which they’re used to. Julien: Absolutely. That also helps us just build better products because the reality is we have a lot of artists with maybe 10 plays in a month. As we build visualizations like something that we built a line of looking at Drake’s data, it’s not going to work as intended for a smaller artist sometimes. Having real data involved as soon as possible into the design process has been such a game changer for us. We really have a multidisciplinary team involved into the research and design of everything we do. I’m working with a data scientist, data engineer, a web engineer, and designer on a daily basis. Obviously, we all have our things to do. But as we get into creating something new, we just make sure to have someone helping us get the real data, interview the right user, and just create prototypes as soon as possible. Working with prototypes is essential into building useful data analytics tools. Brian: Yes, you do learn a lot more with a working prototype. It’s not to say you can’t test with lower fidelity goods, especially early on but for a service like yours when the range of possible use both the personas and also you’ve got the Drakes of the world, big major label artist and then down to really small independents, it’s really important to have an idea how your charts are going to scale, and what’s going to happen with data. Even just small stuff like how many decimal points should you be showing on a mobile device, some of the numbers might cram up. Julien: Exactly. Brian: All this stuff that you never think, if you only look at one version of everything, you can end up with a mess. I’m glad that you brought that up. Julien: I couldn’t say better. The decimal is actually something that we’ve had to discover through real data. Brian: To all of you in the technical people out there, I will say this. If I’ve seen one trend with engineers, is they love precision and there’s a lot of times when there’s very unnecessary precision being added to numbers. Such as charts and histograms. Histograms are usually about the trend, they’re not about identifying what was the precise value on this date at this time. It’s about the change over time. Showing what’s my portfolio worth down to three digits of micro-cents or something like that is just unnecessary detail. You can probably just round up to the dollar or even hundreds of dollars or even thousands of dollars in some cases. It actually is worse. The reason it’s worse is that adds unnecessary noise to the interface, you’re providing all these inks that someone has to mentally process, and it’s actually not really meaningful ink because the change is what’s important. Think about precision when you’re printing values. Julien: This concept of noise is so essential today for any data analytics tools. There is so much data today. There is data for everything. I think it’s our responsibility as a data analytics company to make sure what are we actually trying to help our user with this data set is not just about adding new metrics. Adding new metrics usually is just going to add noise and not be helpful in comparison to fairing what do they need to make the right decision. Brian: Right. Complexity obviously goes up. The single verb, ‘add,’ as soon as you do that, you’re generally adding complexity. One of the design tools that is not used a lot, and this is something I try to help clients with is, what can we take away? If we’re not going to cut it out entirely, can we move this feature, maybe this comparison to a different level of detail? Maybe it’s hidden behind a button click, or it’s not the default. But removing some stuff is a way to obviously simplify as well, especially if you do need to add new things. Your only weapon is not the pencil, you’ve got the eraser as well in the battle so to speak. Julien: I couldn’t agree more. On Next Big Sound we have this concept of artist stages. It’s a way for us to put artist into buckets and by looking at their social instrument data. It goes from undiscovered to epic. We do that by looking at all of the data we have and looking at it in context. I don’t have the numbers right now because they update on a daily basis but every artist starts undiscovered. For instance, as they get 1000 Facebook likes, maybe they’re going to get to a promising stage. We have all of these thresholds moving everyday looking at trends among social services. But what is interesting is that for instance, for a booker, a booker doesn’t need to look at the exact number of Twitter followers for an artist. He needs to know that he’s booking for a midsized venue in the city he’s in and he’s probably going to be looking for promising to established artists and not looking for the mainstream to epic artists. It’s always about figuring a way to use the numbers to tell the story. Brian: I’m totally selfishly asking for myself here, but I was immediately curious. I live in Cambridge which is in the Boston area, and I am curious who are the big artists in our area and what is the concentration? I’m in a niche. I’m more in the performing arts market, in the jazz, in world music, and classical music but I’m just curious. Is there a way to look at it by the city and know what your artist community looks like? You guys do anything like that? Julien: We don’t currently. But I think YouTube has actually a C-level chart available. It’s not part of something we do because I think the users it would benefit are not the users we specifically try to work on new features. It’s more something for bookers than artists ,specifically ,but it’s exactly the type of thing that we need to think about when we prioritize new features. Brian: I’m curious just because the topic’s fairly hot. Everybody is trying to do machine learning projects these days. I don’t like the term AI because it tends to be a little bit overloaded but are you guys using machine learning to accomplish any particular problems or add any new value to your service right now? Is that on your horizon? Julien: How do you think about machine learning? Brian: A lot of times I associate it with predictive analytics or understanding where you might be running instead of just using statistics. I don’t know what kind of data you might have for your learning that you can feed in but maybe there’s aspects about artists that can predict. Especially, I would think like in the pop music world where there tends to be more commercialization of the music, I would say, where it’s like we need a two-minute dance track at this tempo specifically because DJs are going to play it. It’s a very commercial thing. It’s very different than what I’m used to. So I’m curious if there’s a way to predict out how an artist may do or what kinds of tracks are performing well. Like these tempo songs, we predict over the next six months that tech house music at 160 beats for a minute is going to do really well based on the trending. I don’t know. I’m throwing stuff out there. The goal, obviously, is not to try to use like, “Oh Home Depot has this new hammer, let’s run out and get it. We don’t even know what it’s for but everyone else is buying it.” That’s how I joke about machine learning. It’s like you need to have a problem that necessitates that particular tool. I don’t ask such that, “Oh there should be some.” I’m more curious as to whether or not it’s a tool that you guys are leveraging at this time. Julien: The Next Big Sound team doesn’t worked on features following the musical aspects of things. We really are focused on the user data. Brian: Engagement and social. Julien: Engagement data mostly, yes. But at the same time, I’m sure teams have worked on this because of the way that genome works. We have a lot of data about the way songs are made. Regarding machine learning, on the Next Big Song team, we actually have something that is called the prediction chart. You said predictions. We have this chart that is available every week. Basically, it really goes back to having data for a long time. The fact that we’ve had data since 2009, we’ve been able to see artists actually get from starting to charting on the Billboard 200. By having all of these data, we’ve been able to see some trends, some things that usually happen for artists at specific times in their career up until they get into the Billboard 200. We actually do have some algorithms that allow us to apply this learning to all of the artists on Next Big Sound right now and have a list every week of artist that we believe are most likely to appear on the Billboard 200 chart next year. Brian: I see. Got it. Do you track your accuracy rate on that internally and change it over time? Do you adjust the model? Julien: Yeah, we do. Brian: Cool That’s really neat. Tell me, this chat has been super fun. I’ve selfishly got a little indulgent because being a musician, it’s fun to talk about these two worlds that I’m really passionate about so I could go on forever with you about this. But I’m curious. Do you have any advice for other product managers or analytics practitioners about how to design good data products and services? How to make either your own organization happy or your customers happy? Do you have any advice to them? Julien: Yeah, of course. I guess it’s all about asking questions, honestly. What is very good with working at Next Big Sound is that it all started in 2009. Maybe actually I can go back and tell you the story about how it started and why it’s so different today. It started in 2009. It was actually a project, a university project by the three co-founders. Basically, they were wondering about one thing. How many plays does a major artist get on the biggest music platform in the world? At that time, it was MySpace. The artist they picked was Akon. Basically, they just built a crawler, went to bed, woke up, and discovered that an artist like Akon was getting 500,000 plays on MySpace in one night in 2009. The challenge in 2009 was to get the data. That’s why for the most part in Next Big Sound as it started was, I really think a data aggregation tool. Our goal was to get as many sources as possible and just make them easily accessible into the same place. We really are much into the information layer here. We’re giving you all the numbers and you can compare Tumblr to Vimeo, to YouTube, to Twitter, to Facebook, to Vine, to you name it into a table or a graph that you want to. The reality is, today things change. We don’t need to fight to get data anymore. We don’t need to hike our way into getting the numbers. Now, data is accessible to everyone in a very easy way. It’s kind of a contract. You, by being an artist, you know you’re going to get access to your Spotify, YouTube, Pandora, Apple Music or any other platform data very easily just by signing up and authenticating as an artist. That’s where our goal changes. Thankfully, we don’t need to convince people to care about data, we know they do already. But now the challenge is different. Now, the challenge is to make them understand what does their data mean and how can they turn it into getting even more data, getting into having even more engagement, and having even more plays. I think that’s something that is very interesting because it really resonates into the question we’ve been asked in the past few years like, “What does my data mean and when should I be looking at my data?” If anything, these two things correlated pretty well. People don’t just want to look at numbers anymore, they want to be able to use numbers to make decisions. That’s the core of what we’re trying to achieve today. We couldn’t be there if we didn’t have users that ask us the right questions. Brian: Cool that’s really insightful. Just to maybe tie it off at the end and maybe you can’t share this but what’s your home run? What is your holy grail look like? Is there a place you guys know you want to get? Maybe it’s the lack of data or you don’t have access to the data in order to provide that service. Do you guys have kind of a picture of where it is you want to take the service? Julien: What is very noble about our goal at Next Big Sound specifically is we’re here to help artists. The North Star would be to make sure that any artist at any time in their career is doing everything they can do to play more shows, to reach to more people, and to make sure their music is heard. Brian: Nice. I guess it’s like you’re already there, just maybe the level of quality and improving that experience over time, that’s your goal. It’s not so much that there’s so much unobtainable thing at this moment. Is that kind of how you see it? Julien: I think the more we don’t feel just a data analytics tool, the more we’re getting to that goal. I really hope we get to a point where people don’t need to be data analysts to look at data. We’re always going to provide a very customizable tool for the data-savvy because they know what they need more than we can ever do it for them. We want to make sure that for everyone else, we can just make it very easy and as simple as a click for them to do something that’s going to impact them positively. Brian: Cool, man. This has been really exciting to have you on the show. Julien, can you tell the listeners where can they find you on the interwebs? Are you on Twitter or LinkedIn? How do they find you? Julien: For sure. @julienbenatar on Twitter, nextbigsound.com is free for everyone. Actually, we made our data public recently, so if you ever want to learn more about what we do, please check it out. We try to post on our blog about what we learn through data science, through design, and share more about why we build what we build. I recommend to just check blog and do some commitment to learn more about what we do. Brian: I definitely recommend people check out the site. The fun thing is again, as you said, it’s public. If there’s a band you like or whatever, you can type in any group that you like to listen to and you can get access to those insights. Just kind of get a flavor of what the service does. I’ll put those links in the show notes as well as the data pyramid. Julien, cool. Thanks for coming on. Is there anything else do you like to add before we wrap it up? Julien: No, thank you so much. I love reading your newsletters and I’m very happy to be here. Brian: Cool. Thank you so much. Let’s do it again. Julien: Cool. Brian: Cool. Thank you. We hope you enjoyed this episode of Experiencing Data with Brian O’Neill. If you did enjoy it, please consider sharing it with #experiencingdata. To get future podcast updates or to subscribe to Brian’s mailing list where he shares his insights on designing valuable enterprise data products and applications, visit designingforanalytics.com/podcast.

Analizza le tue performance con Next Big Sound

Hey, everyone. I’m Brian O’Neill and I’m excited to share my new podcast with you called Experiencing Data. I’m a consultant specializing in design and user experience for custom enterprise data products and apps. I’m also the founder and principal of Designing for Analytics. My goal with this podcast is to expose you to you or rather to other professionals like you. Who is you? Like any good designer, I had a persona in mind when I started designing this podcast. This persona is basically, modeled on my past clients, conversations at data and analytics conferences that I’ve spoken at, and email exchanges with subscribers on my mailing list. My guest and I assume my listeners are usually going to be data product managers, engineering and analytics leaders, data scientists, and executives. Regardless of the title though, Experiencing Data is really a podcast for business leaders responsible for turning data into useful, usable, and valuable decision support via custom software applications. Maybe you’re wondering why I’m doing this and I am too a little bit. But here is why, I believe the success of analytics software and data products intended for people, since some of them obviously, don’t have interfaces as many of you probably know is, products that are intended for people are only as good as the experiences that they afford, sometimes I refer to that as kind of the last mile of this large technology projects and products that we put out. Because not all companies have trained designers and UX professionals on staff, I was curious to learn how my guests consider user experience as they design these enterprise data products and software tools. On this podcast, we’re not going to go deep on design implementation topics such as data viz and user interface design, some of these things are inherently visual, and I think reading about them and seeing examples is more relevant. But more importantly, I want to look more broadly at what I sometimes call Capital D Design. Capital D Design looks more at defining business objectives, user needs, the problem spaces especially, and the success criteria for new products and services. We’re also going to stay clear off heavy technology discussions since there’s already plenty of that kind of stuff out there and that’s not my area of expertise. Also, on occasion, I may record some solo episodes and share some of my insights on designs that you can put them into play in your daily work. If you’re looking for this kind of insight on a regular basis, you can head over to my Insights mailing list which is at designingforanalytics.com. I write pretty regularly to my list. Feel free to subscribe there if you’re interested in learning more about designing UX. I’m also a professional percussionist. I’m a professional musician and performing artist. In addition to my design consulting work that I do, I wanted to find a way to bring my two worlds together. I’m going to have occasional episodes with music technologies when it’s relevant to Experiencing Data. To kick that off, we’re going to have an upcoming episode featuring a guest who’s a product manager, and his name is Julien Benatar, he’s over at Pandora which I’m sure many of you know. He’s going to come in and talk about how Pandora has gone about designing their services analytics platform which is called Next Big Sound, so looking forward to that one. I hope you will be too. One of the things about podcasting, in general, is ironically how few analytics, we, the publishers and the producers and hosts, receive about our listeners. As those of you on my mailing list already know, I routinely going out and interviewing your customers on a one-on-one fashion; customers, users, whether they’re paying for your software or using an internal tool, I really advocate going out to uncover latent problems they’re having and latent needs that may not be necessarily expressed. But since the podcast environment though doesn’t let me eat my own dog food and do this type of research since we’re kind of in a one-way broadcast modality, with me speaking and you listening, I hope you’ll leave me feedback, either in iTunes or via email. You can reach me at brian@designingforanalytics.com. This show is my MVP, and I’m sure this show may change over time. If you don’t know what an MVP is, well, stay tuned because we will probably cover that as well. If this show sounds interesting to you, please head over to iTunes or your favorite podcast app, and click the subscribe button, and then you can join my mailing list at designingforanalytics.com/podcast. That page will be the homepage for this show. Thanks again. I’m Brian O’Neill and welcome to Experiencing Data. Subscribe for Podcast Updates Get updates on new episodes of Experiencing Data plus my occasional insights on design and UX for custom enterprise data products and apps. Email Address I understand and agree to this website's Terms of Use and Privacy Policy and consent to receiving email from Designing for Analytics, LLC.

C Space is a destination at CES for all disruptive trends that are changing the future of entertainment. This week we focus on a section that is critical to how we view content: streaming. Our guests include a pioneer in the streaming music sector and a new disruptor in video streaming. GUESTS: Jeff Shultz, Chief Business Officer, Pluto TV Alex White, Head of Next Big Sound and Curation Programming, Pandora

In episode 2 of the Small Crowds Matter podcast, we're talking about how artists can easily access your streaming numbers and some of the interactive ways to leverage those statistics to build engagement and relationships with your supporters. Resources such as Spotify for Artists, Apple Music Artists, Next Big Sound, etc. make this process very straightforward and for those that are partial to SoundCloud, there are some fairly untapped methods available if you have a Pro Subscription. All music is courtesy of Atlanta producer/beatmaker STLNDRMS, stream his new album Veggie Tacos 3 on Spotify: https://open.spotify.com/album/0GXsyifaUI3xIFWtE8C7jo

Juicy M has quickly become one of the most popular and promising female producers in the world. With a strong background in hip-hop DJing and turntablism, she entered the electronic scene in 2013 with viral YouTube videos of her mixing live on four decks. Since then, Juicy's gained million of fans on Facebook, was featured in Billboard's “Next Big Sound” chart, and has been acknowledged by popular magazines, blogs and events like Tomorrowland, Ultra Music, Balaton Sound and Medusa. The early years of Marta Martus, Juicy M's real name, were tightly connected with music. By the age of 16, she already had been working as a reporter and news host on a music television channel in Ukraine and started learning DJ skills at a local music school. After graduating, she began working as a hip-hop DJ at top spots in Kiev, and found herself warming up for huge acts like the Backstreet Boys and the Black Eyed Peas as well as participating in DJ contests like the DMC World Championship. But, she always dreamt about standing on festival main stages in front of thousands of party heads, and eventually this dream brought her to electronic dance music. Juicy M's first production, an unofficial remix of Major Lazer's “Watchout For This”, has been supported by heavy hitters like Calvin Harris, Bob Sinclar and Diplo himself, and hit #4 on a weekly chart by the well-known US promo pool DJ City. Soon after, she opened her own record label (JUMMP Records), launched her popular radio show JuicyLand (FM-broadcasted in 11 countries), and landed gigs at club destinations like Privilege, Pacha, and Green Valley. This jump to worldwide fame has made her an inspiring character for upcoming DJs and built an army of loyal and devoted fans worldwide. Her first original tracks “Samurai”, “Show Me Love”, and “Blowback” were released on her own imprint and hit the Beatport charts at 30th, 57th, and 19th place respectively.------------------ Tracklist: 1. Juicy M, Kronic, Sort Of Sick, Asporyz, Klubbheads, David Guetta, GLOWINTHEDARK, Harrison - Ain't a Party Without Maracana [Juicy M Intro Edit] 2. Gammer - THE DROP 3. RIOT - The Mob 4. Jantsen - Bring It Back (feat. Yaysh) 5. Space Laces, Getter - Choppaz 6. Pierce - Mental 7. Unity - The Bump 8. Space Laces - Overdrive 9. Meaux Green - Work (Como Se Dice) (feat. Kstylis) 10. Yellow Claw, DJ Snake - Public Enemy 11. Daft Punk, Marshmello, Afrojack - Pon De Alone [Wuki vs. Benzi Edit] 12. Lil Uzi Vert - XO TOUR Llif3 [Havok Roth Remix] 13. Basky - TGIAF 14. EXSSV - Getting To Ya' (feat. Bianca) 15. Cyran - I'm A Winner 16. LeKtriQue, The Arcturians - Mercy 17. Luca Rezza - Watcha Dem 18. DJ Snake, Lauv, Aazar, WarMchne - A Magenta Way [Juicy M Edit] 19. BDGR$ - Sadamhi 20. Stoltenhoff - Hitman 21. Mike Cervello, Cesqeaux, Avicii, Sandro Cavazza, Rawtek - Without SMACK! [Juicy M Edit] 22. Tha Boogie Bandit, Jilla - Sabotage (feat. Rico Act) 23. Rawtek, Out of Cookies - Badder Den Dem (feat. Ashwin Jaydee) 24. Major Lazer, Busy Signal, Flexican, Garmiani, Komb - Watch Out For Bomb [Juicy M Edit] 25. Stoltenhoff, Skellism - Jump! 26. Valentino Khan - Lick It [Dyro vs. Gammer Remix] 27. Jaguar Skills - LICK OFF (feat. Shakes) 28. Juicy M, ATRIP - No Remorse 29. Dyro - Bring It Down 30. PBH, Jack Shizzle, AFISHAL, Daav One, LOUD ABOUT US! - Genres Race [Juicy M Edit] 31. Swanky Tunes - Drop It 32. Kid Cudi, MGMT, Steve Aoki, Sikdope, LOUD ABOUT US!, Calvin Harris, Dua Lipa, Ton Don - Back to Pursuit Of One Kiss Happiness Again [Juicy M Edit] 33. Childish Gambino - This Is America [Matroda VIP] 34. Panda Eyes - Isolated 35. Snavs, WiDE AWAKE - Turn Left 36. Juicy M, HIDDN - Polaroids and Dirty Dances (feat. Kepler) ------------------ Check out Juicy M: http://juicy-m.com http://www.facebook.com/djjuicym http://twitter.com/djjuicym http://soundcloud.com/dj-juicy-m http://www.instagram.com/djjuicym http://www.youtube.com/user/Juicymtv ------------------ Listen to more exclusive mixes: iedm.com/blogs/iedmradio www.youtube.com/c/Iedmradiopodcast www.mixcloud.com/iEDMradio/ iedmradio.podbean.com/?source=pb   Need the hottest in EDM apparel? Buy the latest here: iedm.com/ ------------------ Discover more: www.facebook.com/iEDMOfficial www.twitter.com/iEDMofficial instagram.com/iEDMofficial iEDMofficial.tumblr.com www.youtube.com/c/IEDMhomeoftherave

Juicy M has quickly become one of the most popular and promising female producers in the world. With a strong background in hip-hop DJing and turntablism, she entered the electronic scene in 2013 with viral YouTube videos of her mixing live on four decks. Since then, Juicy's gained million of fans on Facebook, was featured in Billboard's “Next Big Sound” chart, and has been acknowledged by popular magazines, blogs and events like Tomorrowland, Ultra Music, Balaton Sound and Medusa. Juicy M's first production, an unofficial remix of Major Lazer's “Watchout For This”, has been supported by heavy hitters like Calvin Harris, Bob Sinclar and Diplo himself, and hit #4 on a weekly chart by the well-known US promo pool DJ City. Soon after, she opened her own record label (JUMMP Records), launched her popular radio show JuicyLand (FM-broadcasted in 11 countries), and landed gigs at club destinations like Privilege, Pacha, and Green Valley. This jump to worldwide fame has made her an inspiring character for upcoming DJs and built an army of loyal and devoted fans worldwide. Her first original tracks “Samurai”, “Show Me Love”, and “Blowback” were released on her own imprint and hit the Beatport charts at 30th, 57th, and 19th place respectively. ------------------ Tracklist: 1. Juicy M, Kronic, Sort Of Sick, Asporyz, Klubbheads, David Guetta, GLOWINTHEDARK, Harrison - Ain't a Party Without Maracana [Juicy M Intro Edit] 2. Gammer - THE DROP 3. RIOT - The Mob 4. Jantsen - Bring It Back (feat. Yaysh) 5. Space Laces, Getter - Choppaz 6. Pierce - Mental 7. Unity - The Bump 8. Space Laces - Overdrive 9. Meaux Green - Work (Como Se Dice) (feat. Kstylis) 10. Yellow Claw, DJ Snake - Public Enemy 11. Daft Punk, Marshmello, Afrojack - Pon De Alone [Wuki vs. Benzi Edit] 12. Lil Uzi Vert - XO TOUR Llif3 [Havok Roth Remix] 13. Basky - TGIAF 14. EXSSV - Getting To Ya' (feat. Bianca) 15. Cyran - I'm A Winner 16. LeKtriQue, The Arcturians - Mercy 17. Luca Rezza - Watcha Dem 18. DJ Snake, Lauv, Aazar, WarMchne - A Magenta Way [Juicy M Edit] 19. BDGR$ - Sadamhi 20. Stoltenhoff - Hitman 21. Mike Cervello, Cesqeaux, Avicii, Sandro Cavazza, Rawtek - Without SMACK! [Juicy M Edit] 22. Tha Boogie Bandit, Jilla - Sabotage (feat. Rico Act) 23. Rawtek, Out of Cookies - Badder Den Dem (feat. Ashwin Jaydee) 24. Major Lazer, Busy Signal, Flexican, Garmiani, Komb - Watch Out For Bomb [Juicy M Edit] 25. Stoltenhoff, Skellism - Jump! 26. Valentino Khan - Lick It [Dyro vs. Gammer Remix] 27. Jaguar Skills - LICK OFF (feat. Shakes) 28. Juicy M, ATRIP - No Remorse 29. Dyro - Bring It Down 30. PBH, Jack Shizzle, AFISHAL, Daav One, LOUD ABOUT US! - Genres Race [Juicy M Edit] 31. Swanky Tunes - Drop It 32. Childish Gambino - This Is America [Matroda VIP] 33. Panda Eyes - Isolated 34. Snavs, WiDE AWAKE - Turn Left 35. Juicy M, HIDDN - Polaroids and Dirty Dances (feat. Kepler) ------------------ Check out Juicy M: http://juicy-m.com http://www.facebook.com/djjuicym http://twitter.com/djjuicym http://soundcloud.com/dj-juicy-m http://www.instagram.com/djjuicym http://www.youtube.com/user/Juicymtv ------------------ Listen to more exclusive mixes: iedm.com/blogs/iedmradio www.youtube.com/c/Iedmradiopodcast www.mixcloud.com/iEDMradio/ iedmradio.podbean.com/?source=pb Need the hottest in EDM apparel? Buy the latest here: iedm.com/ ------------------ Discover more: www.facebook.com/iEDMOfficial www.twitter.com/iEDMofficial instagram.com/iEDMofficial iEDMofficial.tumblr.com www.youtube.com/c/IEDMhomeoftherave

Data Strategy Manager Britnee Foreman of Downtown Music Publishing and Songtrust talks about the importance of Analytics, Data, Algorithms and other information that can help assist creators in making an income from their creative works. Find out what platforms are used most by specific groups and how you can strategize and make the most out of your internet presence. Understand how you can leverage this information to position yourself for more success. Learn about tools such as Soundcharts, Indify, Next Big Sound and more. If you would like to keep up with Britnee you can follow her on IG here, lil_missawesome. If you would like to learn more about Downtown Music Publishing and Songtrust visit their websites through the following links, https://www.dmpgroup.com/ and https://www.songtrust.com/.

In this talk, Karl Sluis will recap the different approaches to creating the right context for a team, what's worked for him, what hasn't, and provide a few things Product Leaders can try right away to provide better context for their teams. One of the most important jobs a Product Leader has is making sure everyone working on a product, at any time, clearly knows why they're spending their time (minute to minute, commit to commit) on a given piece of work. In Karl's role at Next Big Sound, he experimented constantly with how to create the context for the team. As the direction of the company changed everyone wasn't just brought along for the ride, but was part of determining the course of action.

In 2014 Christchurch band Doprah was announced as the "Next Big Sound" on Billboard's website, causing a flurry of international attention. But Doprah wound up calling it a day, leaving the members to pursue other projects. Singer Indira Force has just released her debut solo album Precipice under the name indi, and it has a surprisingly strong classical influence.

In this episode I interview Alex White, the founder and head of Next Big Sound which is the data group at Pandora, a leading online music service. Music is a competitive industry, and even though it’s driven by artistic creativity the business of music is driven heavily by data. Alex walks us through how difficult it can be to get reliable data about music and data can become a competitive advantage.

Currently in London, she’s a singer/songwriter who has overcome a significant physical challenge and plays guitar and piano. Her independently released debut EP garnered over two million plays on Spotify and charted at #6 on Billboard’s “Next Big Sound.” She has toured the southeast of the U.S. twice and is #1 on ReverbNation in the Netherlands, and #96 globally. This coming Fall she’ll be attending NYU Clive Davis Institute of Recorded Music while simultaneously working on new material. She was also a two-time full-scholarship winner of Berklee College of Music's Summer Performance Program.

When Next Big Sound launched in 2009, MySpace was the music industry’s online focus. iMeem and iLike were next. Spotify was years away from a US launch, Facebook had no public profile pages for brands and artists, fewer than 100 bands were on Twitter and physical revenue was still worth more than digital for every major label. Obviously a lot has changed, but some things have not: music is heard and consumed more each day; more music is produced and released every year than ever before; and the data generated from fans following, interacting, stalking, listening to and supporting their favourite artists is bigger and more valuable than at any point in history. Alex White co-founded Next Big Sound and now runs the Next Big Sound division within Pandora Media. Join him as he puts the ever-changing music industry – from a data perspective – under the microscope.

Big Data has become a term that we hear a lot these days. We are all trying to tap into our own data and find ways to turn it into useful information. That's where Data Scientists and Data Journalists come in. They are the ones that interpret the information and then turn that information into insightful information that we can all understand. There are some great tools out there available to the general public. With some great companies helping make sense of it all, a few of which are focused on the music industry. They help turn relevant data into gold mines of information that can help advance your career and build your brand. I have been a big fan of Next Big Sound and personally reaped the benefits of their product, as have my clients. In combination with other tools, it has given us creative marketing ideas, a new perspective on how and where to release music, the types of venues to focus on, concepts for custom merch designs that appeal to a specific demographic, and even the type of outside the box partnerships we have developed. Liv Buli has been successfully turning the raw data into insightful articles that make even the least tech savvy person find an interest in big data. As she puts it, she uses data to decipher the business of music. Liv has traveled all over the world talking about what she does – from Brisbane, to Chile, to NYU classrooms. She has been invited to speak and participate in panels at SXSW, CMJ, Music Biz, Future of Music Policy Summit, SF Music Tech, BigSound, ByLarm and more. Lately, she has been giving thought as to how to visualize data in simple, elegant ways to build better stories. As she says "...the best data journalism strikes a balance between finding (data science), showing (data visualization), and telling (journalism) a story." I enjoyed this conversation and as always learned something from it. I hope you do too! This episode was edited by Andy Warren of www.applesandchocolate.com Aaron Bethune Music Specialist. Author. Manager. Creative Collaborator. Speaker. http://www.playitloudmusic.com

** Introduction Song: Flamingosis - To the Clouds ** This week, we have a special mini-podcast episode of the FuseBox Radio Broadcast with DJ Fusion & Ausar Ra Black Hawk which features some audio from the New York Media Festival (NYME) panel, "Technology, Humans, Curation & the Future of Music Discovery" featuring Alex White (Head & Co-Founder, Next Big Sound, Pandora), Natasha Diggs (DJ, Soulinthehorn), Francois Vaxelaire, (Founder & Producer, The Lot Radio) and Nico Perez (Co-founder, Mixcloud). Panel Description via the NYME website: "In the ongoing drive to create the perfect music discovery solution, the balance between data-based algorithms vs human recommendations is in full swing. This panel will feature leaders in both spaces discussing the best practices for fans, artists, and the business alike." For more information about the New York Media Festival, go to http://www.MEFest.com.

Alex White has put together puzzle pieces to connect music and data, building Next Big Sound over the past six years and selling it to Pandora. He shared similarities between business-to-business (B2B) sales in music and other industries.  He also shared what he sees coming up the road as problems to be solved in music and technology. Guest: Alex White, Pandora, Head of Next Big Sound As the Head of Next Big Sound at Pandora, Alex White oversees a NYC-based team of two dozen data engineers, designers, product managers, and data scientists focused on prediction research and cross-platform performance measurement. The team’s mission to make all the data that’s available about every artist in the world useful to music makers and Pandora’s brand clients. White and his Next Big Sound co-founders have been featured in Fast Company (#1 most innovative company in the music industry, 2015), and Forbes (30 under 30) in the music category three times, Billboard (10 best music companies). LinkedIn:  https://www.linkedin.com/in/alexanderswhite/ Twitter:  @mralexwhite Next Big Sound: https://www.nextbigsound.com/

O'Reilly Radar Podcast: Learning from both failure and success to make our systems more resilient.O'Reilly's Jenn Webb chats with Dave Zwieback, head of engineering at Next Big Sound and CTO of Lotus Outreach. Zwieback is the author of a new book, Beyond Blame: Learning from Failure and Success, that outlines an approach to make postmortems not only blameless, but to turn them into a productive learning process. We talk about his book, the framework for conducting a "learning review," and how humans can keep pace with the growing complexity of the systems we're building.When you add scale to anything, it becomes sort of its own problem. Meaning, let's say you have a single computer, right? The mean time to failure of the hard drive or the computer is actually fairly lengthy. When you have 10,000 of them or 10 million of them, you're having tens if not hundreds of failures every single day. That certainly changes how you go about designing systems. Again, whenever I say systems, I also mean organizations. To me, they're not really separate. I spent a bunch of my time in fairly large-scale organizations, and I've witnessed and been part of a significant number of outages or issues. I've seen how dysfunctional organizations dealing with failure can be. By the way, when we mention failure, it's important for us not to forget about success. All the things that we find in the default ways that people and organizations deal with failure, we find in the default ways that they deal with success. It's just a mirror image of each other. We can learn from both failures and success. If we're only learning from failures, which is what the current practice of postmortem is focused on, then we're missing ... the other 99% of the time when they're not failing. The practice of learning reviews allows for learning from both failures and successes. In the practice of learning reviews—and of course, this is also present in the "blameless postmortems"—we don't focus on a single root cause, but we focus on a bunch of conditions. That comes not out of anything other than the realization of the complexity of the systems that we work with. In the current practice of postmortems, we talk about accountability, but really what that version of accountability means is, who's throat are we going to choke. Who are we going to punish? ... In a learning review, we go beyond blame to achieve real accountability. ... If there's blame, or there's punishment, then you're not going to get the full account. You really cannot fully hold people accountable. The other sort of lineage of removing blame and punishment actually comes from a normal non-restorative or punitive justice system, where in certain situations, we give people immunity. Why? So that they can give us the full account of what happened. We do that sometimes with people we know have done bad things. In mafia cases. In those cases, what we are saying or doing by giving people immunity is that we value the information they provide to us more than pushing them. Why do we want to go beyond blame, why do we want to go beyond bias? Those two are the short tickets to learning. ... More importantly, it's not a one time thing. We continually have to be learning about our systems and feeding that knowledge back into that system to make it more resilient. Subscribe to the O'Reilly Radar Podcast: Stitcher, TuneIn, iTunes, SoundCloud, RSS

O'Reilly Radar Podcast: Learning from both failure and success to make our systems more resilient.O'Reilly's Jenn Webb chats with Dave Zwieback, head of engineering at Next Big Sound and CTO of Lotus Outreach. Zwieback is the author of a new book, Beyond Blame: Learning from Failure and Success, that outlines an approach to make postmortems not only blameless, but to turn them into a productive learning process. We talk about his book, the framework for conducting a "learning review," and how humans can keep pace with the growing complexity of the systems we're building.When you add scale to anything, it becomes sort of its own problem. Meaning, let's say you have a single computer, right? The mean time to failure of the hard drive or the computer is actually fairly lengthy. When you have 10,000 of them or 10 million of them, you're having tens if not hundreds of failures every single day. That certainly changes how you go about designing systems. Again, whenever I say systems, I also mean organizations. To me, they're not really separate. I spent a bunch of my time in fairly large-scale organizations, and I've witnessed and been part of a significant number of outages or issues. I've seen how dysfunctional organizations dealing with failure can be. By the way, when we mention failure, it's important for us not to forget about success. All the things that we find in the default ways that people and organizations deal with failure, we find in the default ways that they deal with success. It's just a mirror image of each other. We can learn from both failures and success. If we're only learning from failures, which is what the current practice of postmortem is focused on, then we're missing ... the other 99% of the time when they're not failing. The practice of learning reviews allows for learning from both failures and successes. In the practice of learning reviews—and of course, this is also present in the "blameless postmortems"—we don't focus on a single root cause, but we focus on a bunch of conditions. That comes not out of anything other than the realization of the complexity of the systems that we work with. In the current practice of postmortems, we talk about accountability, but really what that version of accountability means is, who's throat are we going to choke. Who are we going to punish? ... In a learning review, we go beyond blame to achieve real accountability. ... If there's blame, or there's punishment, then you're not going to get the full account. You really cannot fully hold people accountable. The other sort of lineage of removing blame and punishment actually comes from a normal non-restorative or punitive justice system, where in certain situations, we give people immunity. Why? So that they can give us the full account of what happened. We do that sometimes with people we know have done bad things. In mafia cases. In those cases, what we are saying or doing by giving people immunity is that we value the information they provide to us more than pushing them. Why do we want to go beyond blame, why do we want to go beyond bias? Those two are the short tickets to learning. ... More importantly, it's not a one time thing. We continually have to be learning about our systems and feeding that knowledge back into that system to make it more resilient. Subscribe to the O'Reilly Radar Podcast: Stitcher, TuneIn, iTunes, SoundCloud, RSS

Music Biz 101 & More is the only radio show in America that focuses on the business side of the music & entertainment worlds. Hosted by William Paterson University's Dr. Stephen Marcone & Professor David Philp, the show airs live each Wednesday at 8pm on WPSC-FM, Brave New Radio. In this episode, Next Big Sound Data Journalist Liv Buli explains what Next Big Sound does, what the heck a Data Journalist is, and gets into the importance of data in the music industry and how it can be used for good. There's more, so listen to the whole she-bang and be happy. Enjoy the talk, listener tweets, and see what you can get out of this. Like what you hear? Tweet us anytime: @MusicBiz101wp Engage and Adore us on The Facebook, The Twitter & Instagram: www.facebook.com/MusicBiz101wp twitter.com/MusicBiz101WP instagram.com/musicbiz101wp/

Our guest on the podcast this week is Dave Zwieback, Author of Beyond Blame, Head of Engineering at Next Big Sound, and CTO at Lotus Outreach. We discuss how to reach a blame-free culture where outages are linked to multiple conditions instead of one person. Dave shares his framework for Learning Reviews and effective Postmortems. In an increasingly complex world, it is impossible to predict how a system will fail, but through these practices we can see emergent trends and understand how to continuously improve our systems.

In this episode of Music Business Podcast, I talk with Anu Kirk, the former Director of Music Services at Sony Network Entertainment. He spent over three years as the business owner of Sony Music Unlimited, a global multi-platform music subscription service. He also worked on MOG and Rhapsody for several years. I talk to Kirk about Spotify Now, Pandora’s acquisition of Next Big Sound, and music streaming services more broadly. Who is creating the best product? How could they be improved upon? These are all important questions to think about as the music industry gets ready for the Apple Music announcement on Monday. Make sure that you subscribe to Music Business Podcast in iTunes or your favorite podcast app.

Has your playlist become blah and got you all down? No worries… there’s a cure for your depression! It is called “Electro-Convulsic Therapy.” The man with the medicine is Erik Hale, a San Diego based dubstep artist and producer.  Convulsic answers the next questions in the interview with IKE ELLIS. Great interview for artist development, promotional techniques, and knowledge in the music industry.  Explain the meaning behind your name?   What type of music do you currently make?    How long have you been doing music?   At what point did you realize you wanted to do music full time?   What challenges do you face now as oppose to when you first started?   How important is artist development to your career?   What direction are you trying to go as independent artist?   Tell us more about being labeled on the billboard charts? THE NEXT BIG SOUND?    What does your target market look like? How do they dress? Where do the hang?    How do you feel about made up numbers versus obtaining a real fan base through hard work and dedication?    How do you feel about being 3-dimensional type artist versus 1 dimensional?    Game called No hesitation  I'm going say a few words and you choose the first answer Talent or hard work?  Guitar or drums East coast or west coast Music or movies Musician or one hit wonder Kobe or lebron  Groupie or wife   

Data journalist Liv Bulli crunches the numbers at Next Big Sound, then puts them into words that even the lay-artist can understand. She and her team have an uncanny way of predicting future hits; they’ve called out Iggy Azalea and Sam Smith way before they were commending awards and headlines. Liv joins the podcast to talk about her role, why artists shouldn’t compare themselves to Taylor Swift, and why tweeting isn’t enough — you actually need to engage your base.

Dave Zwieback, VP of Engineering at Next Big Sound and Mike Rembetsy, VP of Technical Operations at Etsy discuss learning from the unexpected and examining failure without blame. With practical tips about technical tools and philosophical insights into the human factors and cognitive biases in play, these industry experts offer useful guidance for the thorny questions around the topic of failure.

Dave Zwieback, VP of Engineering at Next Big Sound and Mike Rembetsy, VP of Technical Operations at Etsy discuss learning from the unexpected and examining failure without blame. With practical tips about technical tools and philosophical insights into the human factors and cognitive biases in play, these industry experts offer useful guidance for the thorny questions around the topic of failure.

This is the eleventh episode of Hack To Start. Your hosts, Franco Varriano (on Twitter @ FrancoVarriano) and Tyler Copeland (on Twitter @ TylerCopeland), speak with Zack Shapiro(on Twitter @ ZackShapiro), Founder of Silencer.io, Luna, and Built In Public, about the value of building products with transparency. Zack graduated university with a degree in journalism before taking on roles in a business capacity with The Next Big Sound, TechStars and Path, before teaching himself how to code through an internship with Task Rabbit. He's now building awesome product publicly with Built In Public and working on Product Hunt.

Support this podcast with a tip for as little as $1 an episode at www.patreon.com/Jabari Alex White is the CEO and co-founder of Next Big Sound, a company that provides music analytics and insights. The company measures the growth and popularity of bands across social networks, streaming services and radio. www.NextBigSound.com www.twitter.com/nextbigsound www.facebook.com/nextbigsound www.instagram.com/NextBigSound Follow Jabari: www.youtube.com/Jabari www.twitter.com/Jabari www.instagram.com/Jabari www.facebook.com/JabariLife Jabari@iamjabari.com Words with Friends Artwork: Rodney Curl www.cargocollective.com/curl

Andrea Leonelli from Digital Music Trends interviews Alex White from Next Big Sound. The post SXSW 2014: Alex White, CEO at Next Big Sound appeared first on Digital Music Trends.

Digital Music Trends chats with Brian Hamilton, SVP of Worldwide Sales and Marketing at Gracenote. We chat about the Tribune acquisition in 2013, the new Gracenote Rhythm initiative, their involvement in hack days, the partnership with Next Big Sound, the international business and much more. Andrea Leonelli http://www.digitalmusictrends.com/ http://www.twitter.com/digimusictrends The post Brian Hamilton, SVP Worldwide Sales & Marketing at Gracenote (DMT at Midem 2014) appeared first on Digital Music Trends.

Jin Lee is a one man, bass-blasting, self-taught prodigy hailing from Korea and emerging in Denver, Colorado. Lee, A.K.A. WRKD, uses powerful resounding bass lines mixed with original melodies and hip-hop overtones to create his own unique sound. WRKD is not your average amateur trap artist; he consistantly incorporates inventive sounds, complex melodies, and bass that progressively intensify to unleash your inner party-animal. Although WRKD is heavily influenced by trap and hip-hop, he also incorporates elements of various EDM genres into his production. WRKD is one of the most genuine and technically advanced electronic artists to join the already impressive list of Colorado EDM acts. Since his debut in late 2012, WRKD has been garnering mass attention from EDM websites and radio stations, and has already opened for such EDM acts as Flosstradamus, Dillon Francis and Kaskade. He was featured by RCRD LBL on “BEST OF 2012: PARTY MUSIC,” charted on a Top Ten Must Hear Trap Tracks during Week 8 of Beatport, aired live on Florida radio Station WVUM 90.5 FM The Voice, and has been featured on a large array of music blogs such as Earmilk, Trapmusic.net, Billboard’s Next Big Sound, Real Trap, Trap and Bass, and many others. The list of achievements he has managed to accomplish in his short time as WRKD showcases his impressive momentum that is spurring an ever-growing popularity. Transform any situation into an outrageous party; Turn up your speakers, roll up the Purp, prepare for the bass cannon, and GET WRKD. TRACKLISTING1. Champion Rocka - Devour (WRKD Remix) - Unreleased 2. WRKD - Dropped (Original Mix) 3. Victor Niglio - Jager (WRKD Remix) - Unreleased 4. WRKD - Purps feat. Los Rakas 5. WRKD - Full Assault (Original Mix) 6. Die Antwoord - Diz Is Why I'm Hot (WRKD Remix) 7. WRKD - #WINNING (Original Mix) 8. WRKD - Show No Mercy (Original Mix) 9. The R.O.A.R.- Shes Out Of Control (WRKD Remix) 10. WRKD X The Krillionaires - MethamFetaCheese

Jin Lee is a one man, bass-blasting, self-taught prodigy hailing from Korea and emerging in Denver, Colorado. Lee, A.K.A. WRKD, uses powerful resounding bass lines mixed with original melodies and hip-hop overtones to create his own unique sound. WRKD is not your average amateur trap artist; he consistantly incorporates inventive sounds, complex melodies, and bass that progressively intensify to unleash your inner party-animal. Although WRKD is heavily influenced by trap and hip-hop, he also incorporates elements of various EDM genres into his production. WRKD is one of the most genuine and technically advanced electronic artists to join the already impressive list of Colorado EDM acts. Since his debut in late 2012, WRKD has been garnering mass attention from EDM websites and radio stations, and has already opened for such EDM acts as Flosstradamus, Dillon Francis and Kaskade. He was featured by RCRD LBL on “BEST OF 2012: PARTY MUSIC,” charted on a Top Ten Must Hear Trap Tracks during Week 8 of Beatport, aired live on Florida radio Station WVUM 90.5 FM The Voice, and has been featured on a large array of music blogs such as Earmilk, Trapmusic.net, Billboard’s Next Big Sound, Real Trap, Trap and Bass, and many others. The list of achievements he has managed to accomplish in his short time as WRKD showcases his impressive momentum that is spurring an ever-growing popularity. Transform any situation into an outrageous party; Turn up your speakers, roll up the Purp, prepare for the bass cannon, and GET WRKD. TRACKLISTING1. Champion Rocka - Devour (WRKD Remix) - Unreleased 2. WRKD - Dropped (Original Mix) 3. Victor Niglio - Jager (WRKD Remix) - Unreleased 4. WRKD - Purps feat. Los Rakas 5. WRKD - Full Assault (Original Mix) 6. Die Antwoord - Diz Is Why I'm Hot (WRKD Remix) 7. WRKD - #WINNING (Original Mix) 8. WRKD - Show No Mercy (Original Mix) 9. The R.O.A.R.- Shes Out Of Control (WRKD Remix) 10. WRKD X The Krillionaires - MethamFetaCheese