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2 episodes with lauren maffeo
This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubRead the full transcription of the interview hereLauren Maffeo - Senior Service Designer at Steampunk & Author of "Designing Data Governance from the Ground Up"Samia Rahman - Director of Enterprise Data Strategy and Governance at SeagenRESOURCESLaurenhttps://twitter.com/LaurenMaffeohttps://www.linkedin.com/in/laurenmaffeoSamiahttps://www.linkedin.com/in/samia-r-b7b65216https://twitter.com/rahman1_samiaDESCRIPTIONData governance manages the people, processes, and strategy needed for deploying data projects to production. But doing it well is far from easy: Less than one-fourth of business leaders say their organizations are data-driven. In Designing Data Governance from the Ground Up, you'll build a cross-functional strategy to create roadmaps and stewardship for data-focused projects, embed data governance into your engineering practice, and put processes in place to monitor data after deployment.In the last decade, the amount of data people produced grew 3,000 percent. Most organizations lack the strategy to clean, collect, organize, and automate data for production-ready projects. Without effective data governance, most businesses will keep failing to gain value from the mountain of data that's available to them.There's a plethora of content intended to help DataOps and DevOps teams reach production, but 90 percent of projects trained with big data fail to reach production because they lack governance.This book shares six steps you can take to build a data governance strategy from scratch. You'll find a data framework, pull together a team of data stewards, build a data governance team, define your roadmap, weave data governance into your development process, and monitor your data in production. [...]* Book description: © Pragmatic ProgrammersThe interview is based on the book " Designing Data Governance from the Ground Up".RECOMMENDED BOOKSLauren Maffeo • Designing Data Governance from the Ground UpKatharine Jarmul • Practical Data PrivacyKatharine Jarmul & Jacqueline Kazil • Data Wrangling with PythonYehonathan Sharvit • Data-Oriented ProgrammingZhamak Dehghani • Data MeshEberhard Wolff & Hanna Prinz • Service MeshPiethein Strengholt • Data Management at ScaleMartin Kleppmann • Designing Data-Intensive ApplicationsTwitterInstagramLinkedInFacebookLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!
Episode 86 explores the importance of Data & Analytics Management with Lauren Maffeo.Based out of Washington DC, Lauren Maffeo is the Senior Service Designer at Steampunk Inc an IT Service and IT Consulting company.Lauren is an award-winning author, analyst, and designer of data systems for the U.S. Federal government. Career highlights include leading service design for an agency database with 46 million+ unique data points and is the author of “Designing Data Governance from the Ground Up”.She is a founding editor of Springer's AI and Ethics Journal and an adjunct lecturer of Interaction Design at The George Washington University. Lauren has written for Harvard Data Science Review, Financial Times, and The Guardian. She has also presented her research on bias in AI at Princeton and Columbia Universities, Google DevFest DC, and Twitter's San Francisco headquarters.The Business of Business, topics are divided into 4 Categories: Management, Operations, Sales, and Financial. Target Audience is Business Owners, C-Level Executives, Management, and anyone considering starting a business. Support the showHelping You Run a Successful Profitable Business !For Business Consulting or to be a Podcast Guest - Contact me at: www.bcforg.comLinkedIn: https://www.linkedin.com/in/brian-fisher-72174413/
For links and resources discussed in this episode, please visit our show notes at https://www.forcepoint.com/govpodcast/e264
A conversation with Lauren Maffeo. In this episode we're joined by Lauren Maffeo. Lauren is an award-winning service designer working full-time at Steampunk where she serves the U.S. federal government. She is a founding editor of Springer's AI and Ethics Journal and an adjunct lecturer of Interaction Design at The George Washington University. Lauren has written for Harvard Data Science Review, Financial Times, and The Guardian. She has also presented her research on bias in AI at Princeton and Columbia Universities, Google DevFest DC, and Twitter's San Francisco headquarters and is the author of Designing Data Governance from the Ground Up. In this episode we talk about the societal impact of data governance, the link between human-centered design and managing data in a company, the anti-patterns in data management, the importance of culture in breaking down data silos, balancing transparency with security, the link between data quality and misinformation and many other topics… See timestamps below. Go here for show notes, links, and resources. Follow Juan Mendoza on LinkedIn and Twitter. Listen on Apple, Spotify, Google, and everywhere else. You can find Lauren on LinkedIn. Timestamps Time Topic(0:11) Guest intro(10:50) Incentives to data the practice of data governance/management(21:47) The anti-patterns in data management(27:27) Discussion around techno optimism(34:20) the importance of culture in breaking down data silos(44:06) Balancing transparency with security(50:34) The Link between data governance and misinformation(53:33) Importance of data quality & governance on commerciality such as generative AI
Lauren Maffeo is an award-winning service designer working full-time at Steampunk where she serves the U.S. federal government. She is a founding editor of Springer's AI and Ethics Journal and an adjunct lecturer of Interaction Design at The George Washington University. Lauren has written for Harvard Data Science Review, Financial Times, and The Guardian. She has also presented her research on bias in AI at Princeton and Columbia Universities, Google DevFest DC, and Twitter's San Francisco headquarters. She is the author of Designing Data Governance from the Ground Up. Listeners can buy a copy of her book, and get 35% off using the code DATAGOV23 (valid until September 2023) Summary Introduction to Lauren. Managing data and data governance. The lack of governance around data. The problem of data spoilage. Who is a hero of yours and why are they a hero?
Lauren Maffeo is an award-winning service designer working full-time at Steampunk where she serves the U.S. federal government. She is a founding editor of Springer's AI and Ethics Journal and an adjunct lecturer of Interaction Design at The George Washington University. Lauren has written for Harvard Data Science Review, Financial Times, and The Guardian. She has also presented her research on bias in AI at Princeton and Columbia Universities, Google DevFest DC, and Twitter's San Francisco headquarters. She is the author of Designing Data Governance from the Ground Up. Listeners can buy a copy of her book, and get 35% off using the code DATAGOV23 (valid until September 2023) Summary Introduction to Lauren. Managing data and data governance. The lack of governance around data. The problem of data spoilage. Who is a hero of yours and why are they a hero?
Use code DATAGOV23 for 35% off ebook copies of Designing Data Governance from the Ground Up here: https://pragprog.com/titles/lmmlops/designing-data-governance-from-the-ground-up/Please Rate and Review us on your podcast app of choice!Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn if you want to chat data mesh.Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.Lauren's LinkedIn: https://www.linkedin.com/in/laurenmaffeo/Designing Data Governance from the Ground Up (Lauren's book): https://pragprog.com/titles/lmmlops/designing-data-governance-from-the-ground-up/In this episode, Scott interviewed Lauren Maffeo, author of the book Designing Data Governance from the Ground Up and adjunct Lecturer at George Washington University. To be clear, she was only representing her own views on the episode.Some key takeaways/thoughts from Lauren's point of view:In governance, a very easy way to go down a bad path is to not automate your standards. Making governance an easy aspect of data work will go a long, long way.?Controversial?: As an industry in general, data governance maturity is still at the infancy phase. And the pace of maturation is far lower than other aspects of software like security.The majority of organizations are not mature enough with data governance to get a lot of value from things like ML or NLP.Data governance best practices are hard to come by. There isn't really even a large community specific to data governance for people to easily exchange ideas.If the...
Data Governance may not sound exciting - but it's critical! It covers how your company produces, consumes, collects, and destroys data. So, with businesses generating and using more data than ever, why is this role so often forgotten? Our guest today is Lauren Maffeo, Author of "Designing Data Governance from the Ground Up" and an adjunct Lecturer of Design at The George Washington University. Lauren sets the tone for us by explaining what Data Governance is and how too much data is produced for one person or even a single team to own it all. Lauren lays out how you can create data points from subject matter experts around key areas of data your company produces or ingests, such as Sales, Marketing, and Customer Data; each of which can then have sub-sets to provide even more structure. Lauren shares why you need to understand how data works in the subject matter expert's day to day job and how data governance will help them do their job more effectively. In addition, we learn about data dictionaries and the big part they play in data governance and enablement efforts to ensure clarity is provided across domains on the exact meaning of terms that might have different meanings depending on the context. Three Key Takeaways: · The biggest challenge to doing data governance well is having a thought leadership strategy around it to get other colleagues on board. · There is too much data produced today for one person or one team to own all of it. You need to make it a collective effort across technical and non-technical roles. · You can not succeed in sales, marketing, or customer success without data.
In this episode of the show, I continue my deep dive into data, human values, and governance with an interview featuring Lauren Maffeo. We talk about the future of data governance, the possibilities of, and the catastrophe that Lauren thinks our society may need to experience in order to turn the corner on an data governance and ethics. Lauren Maffeo is an award-winning designer and analyst who currently works as a service designer at Steampunk, a human-centered design firm serving the federal government. She is also a founding editor of Springer's AI and Ethics journal and an adjunct lecturer in Interaction Design at The George Washington University. Her first book, Designing Data Governance from the Ground Up, is available from The Pragmatic Programmers. Lauren has written for Harvard Data Science Review, Financial Times, and The Guardian, among other publications. She is a fellow of the Royal Society of Arts, a former member of the Association for Computing Machinery's Distinguished Speakers Program, and a member of the International Academy of Digital Arts and Sciences, where she helps judge the Webby Awards.
Get ready to unlock the secrets to a more transparent data future! Join us in this captivating episode as we sit down with Lauren Maffeo, Senior Service Designer at Steampunk and Author of "Designing Data Governance from the Ground Up".Delve into the world of data transparency and its profound impact on organizations, as we explore practical strategies to enhance data practices and build trust. From striking the right balance between privacy and transparency to fostering data literacy, our expert guest shares invaluable insights for businesses and individuals alike. Whether you're a data enthusiast or a decision-maker seeking a more transparent data culture, this conversation is your gateway to harnessing the true power of data. Tune in now and embark on a journey to a more transparent and data-driven future!
In this episode of Data Driven, Frank and Andy Leonard are joined by guest speaker Lauren Maffeo to discuss data governance from the ground up. The conversation revolves around the importance of data governance in relation to generative AI, copyright infringement, and protecting consumer rights.They explore topics such as the need for proactive cybersecurity measures, the challenges faced by startups in implementing data governance, and the cultural transformation required for successful implementation.Overall, it is a thought-provoking discussion that provides insights into the complexities and potential solutions related to data governance in today's data-driven world.Moments00:05:49 Civic Tech serves the public through technology.00:07:50 Data governance: a holistic, cultural business strategy.00:12:25 Data as tangible asset, managing as product.00:14:38 Implementing data governance: start small, connect to business.00:20:34 Data growth, lack of management, legislative progress. Clear framework for data quality needed.00:25:14 Startups prioritize innovation for survival. Large industries restrict innovation due to regulation. Motivations and context are key in governance.00:28:54 Data governance and copyright infringement in generative AI. The future of consumer rights and cybersecurity.00:33:44 Encourage caution with sharing proprietary information00:36:36 Bias in AI and data governance intertwined. Risk reduction, troubleshooting. Not all intent is negative. Challenges in data work solvable. Nonprofits and cybersecurity models for governance.00:40:38 Encouraging shift in conversation about data governance.00:44:34 Data found me, sparked interest in AI.00:49:20 Technology saves time, allowing for more productivity.00:54:03 Adopting foster pets: fun without long-term responsibility.00:55:57 Connect on LinkedIn, visit Pragprov.com, feedback welcome.
Lauren Maffeo is the author of Designing Data Governance from the Ground Up. Victoria talks to Lauren about human-centered design work, data stewardship and governance, and writing a book anybody can use regardless of industry or team size. Designing Data Governance from the Ground Up (https://www.amazon.com/Designing-Data-Governance-Ground-Data-Driven/dp/1680509802) Follow Lauren Maffeo on LinkedIn (https://www.linkedin.com/in/laurenmaffeo/) or Twitter (https://twitter.com/LaurenMaffeo). Follow thoughtbot on Twitter (https://twitter.com/thoughtbot) or LinkedIn (https://www.linkedin.com/company/150727/). Become a Sponsor (https://thoughtbot.com/sponsorship) of Giant Robots! Transcript: VICTORIA: Hey there. It's your host Victoria. And I'm here today with Dawn Delatte and Jordyn Bonds from our Ignite team. We are thrilled to announce the summer 2023 session of our new incubator program. If you have a business idea that involves a web or mobile app, we encourage you to apply for our 8-week program. We'll help you validate the market opportunity, experiment with messaging and product ideas, and move forward with confidence towards an MVP. Learn more and apply at tbot.io/incubator. Dawn and Jordyn, thank you for joining and sharing the news with me today. JORDYN: Thanks for having us. DAWN: Yeah, glad to be here. VICTORIA: So, tell me a little bit more about the incubator program. This will be your second session, right? JORDYN: Indeed. We are just now wrapping up the first session. We had a really great 8 weeks, and we're excited to do it again. VICTORIA: Wonderful. And I think we're going to have the person from your program on a Giant Robots episode soon. JORDYN: Wonderful. VICTORIA: Maybe you can give us a little preview. What were some of your main takeaways from this first round? JORDYN: You know, as ever with early-stage work, it's about identifying your best early adopter market and user persona, and then learning as much as you possibly can about them to inform a roadmap to a product. VICTORIA: What made you decide to start this incubator program this year with thoughtbot? DAWN: We had been doing work with early-stage products and founders, as well as some innovation leads or research and development leads in existing organizations. We had been applying a lot of these processes, like the customer discovery process, Product Design Sprint process to validate new product ideas. And we've been doing that for a really long time. And we've also been noodling on this idea of exploring how we might offer value even sooner to clients that are maybe pre-software product idea. Like many of the initiatives at thoughtbot, it was a little bit experimental for us. We decided to sort of dig into better understanding that market, and seeing how the expertise that we had could be applied in the earlier stage. It's also been a great opportunity for our team to learn and grow. We had Jordyn join our team as Director of Product Strategy. Their experience with having worked at startups and being an early-stage startup founder has been so wonderful for our team to engage with and learn from. And we've been able to offer that value to clients as well. VICTORIA: I love that. So it's for people who have identified a problem, and they think they can come up with a software solution. But they're not quite at the point of being ready to actually build something yet. Is that right? DAWN: Yeah. We've always championed the idea of doing your due diligence around validating the right thing to build. And so that's been a part of the process at thoughtbot for a really long time. But it's always been sort of in the context of building your MVP. So this is going slightly earlier with that idea and saying, what's the next right step for this business? It's really about understanding if there is a market and product opportunity, and then moving into exploring what that opportunity looks like. And then validating that and doing that through user research, and talking to customers, and applying early product and business strategy thinking to the process. VICTORIA: Great. So that probably sets you up for really building the right thing, keeping your overall investment costs lower because you're not wasting time building the wrong thing. And setting you up for that due diligence when you go to investors to say, here's how well I vetted out my idea. Here's the rigor that I applied to building the MVP. JORDYN: Exactly. It's not just about convincing external stakeholders, so that's a key part. You know, maybe it's investors, maybe it's new team members you're looking to hire after the program. It could be anyone. But it's also about convincing yourself. Really, walking down the path of pursuing a startup is not a small undertaking. And we just want to make sure folks are starting with their best foot forward. You know, like Dawn said, let's build the right thing. Let's figure out what that thing is, and then we can think about how to build it right. That's a little quote from a book I really enjoy, by the way. I cannot take credit for that. [laughs] There's this really great book about early-stage validation called The Right It by Alberto Savoia. He was an engineer at Google, started a couple of startups himself, failed in some ways, failed to validate a market opportunity before marching off into building something. And the pain of that caused him to write this book about how to quickly and cheaply validate some market opportunity, market assumptions you might have when you're first starting out. The way he frames that is let's figure out if it's the right it before we build it right. And I just love that book, and I love that framing. You know, if you don't have a market for what you're building, or if they don't understand that they have the pain point you're solving for, it doesn't matter what you build. You got to do that first. And that's really what the focus of this incubator program is. It's that phase of work. Is there a there there? Is there something worth the hard, arduous path of building some software? Is there something there worth walking that path for before you start walking it? VICTORIA: Right. I love that. Well, thank you both so much for coming on and sharing a little bit more about the program. I'm super excited to see what comes out of the first round, and then who gets selected for the second round. So I'm happy to help promote. Any other final takeaways for our listeners today? DAWN: If this sounds intriguing to you, maybe you're at the stage where you're thinking about this process, I definitely encourage people to follow along. We're trying to share as much as we can about this process and this journey for us and our founders. So you can follow along on our blog, on LinkedIn. We're doing a LinkedIn live weekly with the founder in the program. We'll continue to do that with the next founders. And we're really trying to build a community and extend the community, you know, that thoughtbot has built with early-stage founders, so please join us. We'd love to have you. VICTORIA: Wonderful. That's amazing. Thank you both so much. INTRO MUSIC: VICTORIA: This is the Giant Robots Smashing Into Other Giant Robots Podcast, where we explore the design, development, and business of great products. I'm your host, Victoria Guido. And with me today is Lauren Maffeo, Author of Designing Data Governance from the Ground Up. Lauren, thank you for joining us. LAUREN: Thanks so much for having me, Victoria. I'm excited to be here. VICTORIA: Wonderful. I'm excited to dive right into this topic. But first, maybe just tell me what led you to start writing this book? LAUREN: I was first inspired to write this book by my clients, actually. I was working as a service designer at Steampunk, which is a human-centered design firm serving the federal government. I still do work for Steampunk. And a few years ago, I was working with a client who had a very large database containing millions of unique data points going back several centuries. And I realized throughout the course of my discovery process, which is a big part of human-centered design work, that most of their processes for managing the data in this database were purely manual. There was no DevSecOps integrated into their workflows. These workflows often included several people and took up to a week to complete. And this was an organization that had many data points, as mentioned, in its purview. They also had a large team to manage the data in various ways. But they still really struggled with an overall lack of processes. And really, more importantly, they lacked quality standards for data, which they could then automate throughout their production processes. I realized that even when organizations exist to have data in their purview and to share it with their users, that doesn't necessarily mean that they actually have governance principles that they abide by. And so that led me to really consider, more broadly, the bigger challenges that we see with technology like AI, machine learning, large language models. We know now that there is a big risk of bias within these technologies themselves due to the data. And when I dug deeper, first as a research analyst at Gartner and then as a service designer at Steampunk, I realized that the big challenge that makes this a reality is lack of governance. It's not having the quality standards for deciding how data is fit for use. It's not categorizing your data according to the top domains in your organization that produce data. It's lack of clear ownership regarding who owns which data sets and who is able to make decisions about data. It's not having things like a data destruction policy, which shows people how long you hold on to data for. So that knowledge and seeing firsthand how many organizations struggle with that lack of governance that's what inspired me to write the book itself. And I wanted to write it from the lens of a service designer. I have my own bias towards that, given that I am a practicing service designer. But I do believe that data governance when approached through a design thinking lens, can yield stronger results than if it is that top-down IT approach that many organizations use today unsuccessfully. VICTORIA: So let me play that back a little bit. So, in your experience, organizations that struggle to make the most out of their data have an issue with defining the authority and who has that authority to make decisions, and you refer to that as governance. So that when it comes down to it, if you're building things and you want to say, is this ethical? Is this right? Is this secure? Is it private enough? Someone needs to be responsible [laughs] for answering that. And I love that you're bringing this human-centered design approach into it. LAUREN: Yeah, that's exactly right. And I would say that ownership is a big part of data governance. It is one of the most crucial parts. I have a chapter in my book on data stewards, what they are, the roles they play, and how to select them and get them on board with your data governance vision. The main thing I want to emphasize about data stewardship is that it is not just the technical members of your team. Data scientists, data architects, and engineers can all be exceptional data stewards, especially because they work with the data day in and day out. The challenge I see is that these people typically are not very close to the data, and so they don't have that context for what different data points mean. They might not know offhand what the definitions per data piece are. They might not know the format that the data originates in. That's information that people in non-technical roles tend to possess. And so, data stewardship and governance is not about turning your sales director into a data engineer or having them build ETL pipelines. But it is about having the people who know that data best be in positions where they're able to make decisions about it, to define it, to decide which pieces of metadata are attached to each piece of data. And then those standards are what get automated throughout the DevSecOps process to make better life cycles that produce better-quality data faster, at speed with fewer resources. VICTORIA: So, when we talk about authority, what we really mean is, like, who has enough context to make smart decisions? LAUREN: Who has enough context and also enough expertise? I think a big mistake that we as an industry have made with data management is that we have given the responsibility for all data in an organization to one team, sometimes one person. So, typically, what we've done in the past is we've seen all data in an organization managed by IT. They, as a department, make top-down decisions about who has access to which data, what data definitions exist, where the data catalog lives, if it exists in an organization at all. And that creates a lot of blockers for people if you always have to go through one team or person to get permission to use data. And then, on top of that, the IT team doesn't have the context that your subject matter experts do about the data in their respective divisions. And so it really is about expanding the idea of who owns data and who is in a position of authority to make decisions about it by collaborating across silos. This is very challenging work to do. But I would actually say that for smaller organizations, they might lack the resources in, time, and money, and people to do data governance at scale. But what they can do is start embedding data governance as a core principle into the fabric of their organizations. And ultimately, I think that will power them for success in a way that larger organizations were not able to because there is a lot of technical debt out there when it comes to bad data. And one way to avoid that in the future or to at least mitigate it is to establish data governance standards early on. VICTORIA: Talk me through what your approach would be if you were working with an organization who wants to build-in this into the fabric of how they work. What would be your first steps in engaging with them and identifying where they have needs in part of that discovery process? LAUREN: In human-centered design, the discovery process occurs very early in a project. This is where you are working hand in hand with your client to figure out what their core needs are and how you can help them solve those core needs. And this is important to do because it's not always obvious what those needs are. You might get a contract to work on something very specific, whether it's designing the user interface of a database or it's migrating a website. Those are technical challenges to solve. And those are typically the reason why you get contracted to work with your client. But you still have to do quite a bit of work to figure out what the real ask is there and what is causing the need for them to have hired you in the first place. And so, the first thing I would do if I was walking a client through this is I would start by asking who the most technical senior lead in the organization is. And I would ask how they are managing data today. I think it's really important, to be honest about the state of data in your organization today. The work that we do designing data governance is very forward-thinking in a lot of ways, but you need a foundation to build upon. And I think people need to be honest about the state of that foundation in their organization. So the first thing I would do is find that most-senior data leader who is responsible for making decisions about data and owns the data strategy because that person is tasked with figuring out how to use data in a way that is going to benefit the business writ large. And so, data governance is a big part of what they are tasked to do. And so, in the first instance, what I would do is I would host a workshop with the client where I would ask them to do a few things. They would start by answering two questions: What is my company's mission statement, and how do we use data to fulfill that mission statement? These are very baseline questions. And the first one is so obvious and simple that it might be a little bit off-putting because you're tempted to think, as a senior leader, I already know what my company does. Why do I need to answer it like this? And you need to answer it like this because just like we often get contracts to work on particular technical problems, you'd be surprised by how many senior leaders cannot articulate their company's mission statements. They'll talk to you about their jobs, the tools they use to do their jobs, who they work with on a daily basis. But they still aren't ultimately answering the question of how their job, how the technology they use fulfills a bigger organizational need. And so, without understanding what that organizational need is, you won't be able to articulate how data fulfills that mission. And if you're not able to explain how data fulfills your company's mission, I doubt you can explain which servers your data lives on, which file format it needs to be converted to, who owns which data sets, where they originate, what your DevSecOps processes are. So answering those two questions about the company mission and how data is used to fulfill that mission is the first step. The second thing I would do is ask this senior leader, let's say the chief data officer, to define the data domains within their organization. And when we talk about data domains, we are talking about the areas of the business that are the key areas of interest. This can also be the problem spaces that your organization addresses. It also can have a hand in how your organization is designed as is; in other words, who reports to whom? Do you have sales and marketing within one part of the organization, or are they separate? Do you have customer success as its own wing of the organization separate from product? However your organization is architected, you can draw lines between those different teams, departments, and the domains that your organization works in. And then, most importantly, you want to be looking at who leads each domain and has oversight over the data in that domain. This is a really important aspect of the work because, as mentioned, stewards play a really key role in upholding and executing data governance. You need data stewards across non-technical and technical roles. So defining not just what the data domains are but who leads each domain in a senior role is really important to mapping out who your data stewards will be and to architect your first data governance council. And then, finally, the last thing I would have them do in the first instance is map out a business capability map showing not only what their data domains are but then the sub-domains underneath. So, for example, you have sales, and that can be a business capability. But then, within the sales data domain, you're going to have very different types of sales data. You're going to have quarterly sales, bi-annual sales, inbound leads versus outbound leads. You're going to have very different types of data within that sales data domain. And you want to build those out as much as you possibly can across all of your data domains. If you are a small organization, it's common to have about four to six data domains with subdomains underneath, each of those four to six. But it varies according to each startup and organization and how they are structured. Regardless of how your organization is structured, there's always value in doing those three things. So you start by identifying what your organization does and how data fulfills that goal. You define the core data domains in your organization, including who owns each domain. And then, you take that information about data domains, and you create a capability map showing not just your core data domains but the subdomains underneath because you're going to use all of that information to architect a future data governance program based on what you currently have today. VICTORIA: I think that's a great approach, and it makes a lot of sense. Is that kind of, like, the minimum that people should be doing for a data governance program? Like, what's the essentials to do, like, maybe even your due diligence, say, as a health tech startup company? LAUREN: This is the bare minimum of what I think every organization should do. The specifics of that are different depending on industry, depending on company size, organizational structure. But I wrote this book to be a compass that any organization can use. There's a lot of nuance, especially when we get into the production environment an organization has. There's a lot of nuance there depending on tools, all of that. And so I wanted to write a book that anybody could use regardless of industry size, team size, all of that information. I would say that those are the essential first steps. And I do think that is part of the discovery process is figuring out where you stand today, and no matter how ugly it might be. Because, like we've mentioned, there is more data produced on a daily basis than ever before. And you are not going into this data governance work with a clean slate. You already have work in your organization that you do to manage data. And you really need to know where there are gaps so that you can address those gaps. And so, when we go into the production environment and thinking about what you need to do to be managing data for quality on a regular basis, there are a couple of key things. The first is that you need a plan for how you're going to govern data throughout each lifecycle. So you are very likely not using a piece of data once and never again. You are likely using it through several projects. So you always want to have a plan for governance in production that includes policies on data usage, data archiving, and data destruction. Because you want to make sure that you are fulfilling those principles, whatever they are, throughout each lifecycle because you are managing data as a product. And that brings me to the next thing that I would encourage people working in data governance to consider, which is taking the data mesh principle of managing data as a product. And this is a fundamental mind shift from how big data has been managed in the past, where it was more of a service. There are many detriments to that, given the volume of data that exists today and given how much data environments have changed. So, when we think about data mesh, we're really thinking about four key principles. The first is that you want to manage your data according to specific domains. So you want to be creating a cloud environment that really accounts for the nuance of each data domain. That's why it's so important to define what those data domains are. You're going to not just document what those domains are. You're going to be managing and owning data in a domain-specific way. The second thing is managing data as a product. And so, rather than taking the data as a service approach, you have data stewards who manage their respective data as products within the cloud environment. And so then, for instance, rather than using data about customer interactions in a single business context, you can instead use that data in a range of ways across the organization, and other colleagues can use that data as well. You also want to have data available as a self-service infrastructure. This is really important in data mesh. Because it emphasizes keeping all data on a centralized platform that manages your storage, streaming, pipelines, and anything else, and this is crucial because it prevents data from leaving in disparate systems on various servers. And it also erases or eases the need to build integrations between those different systems and databases. And it also gives each data steward a way to manage their domain data from the same source. And then the last principle for data mesh is ecosystem governance. And really, what we're talking about here is reinforcing the data framework and mission statement that you are using to guide all of your work. It's very common in tech for tech startups to operate according to a bigger vision and according to principles that really establish the rationale for why that startup deserves to exist in the world. And likewise, you want to be doing all of your production work with data according to a bigger framework and mission that you've already shared. And you want to make sure that all of your data is formatted, standardized, and discoverable against equal standards that govern the quality of your data. VICTORIA: That sounds like data is your biggest value as a company and your greatest source of liability [laughs] and in many ways. And, I'm curious, you mentioned just data as a product, if you can talk more about how that fits into how company owners and founders should be thinking about data and the company they're building. LAUREN: So that's a very astute comment about data as a liability. That is absolutely true. And that is one of the reasons why governance is not just nice to have. It's really essential, especially in this day and age. The U.S. has been quite lax when it comes to data privacy and protection standards for U.S. citizens. But I do think that that will change over the next several years. I think U.S. citizens will get more data protections. And that means that organizations are going to have to be more astute about tracking their data and making sure that they are using it in appropriate ways. So, when we're talking to founders who want to consider how to govern data as a product, you're thinking about data stewards taking on the role of product managers and using data in ways that benefits not just them and their respective domains but also giving it context and making it available to the wider business in a way that it was not available before. So if you are architecting your data mesh environment in the cloud, what you might be able to do is create various domains that exist on their own little microservice environments. And so you have all of these different domains that exist in one environment, but then they all connect to this bigger data mesh catalog. And from the catalog, that is where your colleagues across the business can access the data in your domain. Now, you don't want to necessarily give free rein for anybody in your organization to get any data at any time. You might want to establish guardrails for who is able to access which data and what those parameters are. And the data as a product mindset allows you to do that because it gives you, as the data steward/pseudo pm, the autonomy to define how and when your data is used, rather than giving that responsibility to a third-party colleague who does not have that context about the data in your domain. VICTORIA: I like that about really giving the people who have the right context the ability to manage their product and their data within their product. That makes a lot of sense to me. Mid-Roll Ad: As life moves online, bricks-and-mortar businesses are having to adapt to survive. With over 18 years of experience building reliable web products and services, thoughtbot is the technology partner you can trust. We provide the technical expertise to enable your business to adapt and thrive in a changing environment. We start by understanding what's important to your customers to help you transition to intuitive digital services your customers will trust. We take the time to understand what makes your business great and work fast yet thoroughly to build, test, and validate ideas, helping you discover new customers. Take your business online with design-driven digital acceleration. Find out more at tbot.io/acceleration or click the link in the show notes for this episode. VICTORIA: What is it like to really bring in this culture of design-thinking into an organization that's built a product around data? LAUREN: It can be incredibly hard. I have found that folks really vary in their approach to this type of work. I think many people that I talk to have tried doing data governance to some degree in the past, and, for various reasons, it was not successful. So as a result, they're very hesitant to try again. I think also for many technical leaders, if they're in CIO, CDO, CTO roles, they are not used to design thinking or to doing human-centered design work. That's not the ethos that was part of the tech space for a very long time. It was all about the technology, building what you could, experimenting and tinkering, and then figuring out the user part later. And so this is a real fundamental mindset shift to insist on having a vision for how data benefits your business before you start investing money and people into building different data pipelines and resources. It's also a fundamental shift for everyone in an organization because we, in society writ large, are taught to believe that data is the responsibility of one person or one team. And we just can't afford to think like that anymore. There is too much data produced and ingested on a daily basis for it to fall to one person or one team. And even if you do have a technical team who is most adept at managing the cloud environment, the data architecture, building the new models for things like fraud detection, that's all the purview of maybe one team that is more technical. But that does not mean that the rest of the organization doesn't have a part to play in defining the standards for data that govern everything about the technical environment. And I think a big comparison we can make is to security. Many of us… most of us, even if we work in tech, are not cybersecurity experts. But we also know that employees are the number one cause of breaches at organizations. There's no malintent behind that, but people are most likely to expose company data and cause a breach from within the company itself. And so organizations know that they are responsible for creating not just secure technical environments but educating their employees and their workforce on how to be stewards of security. And so, even at my company, we run constant tests to see who is going to be vulnerable to phishing? Who is going to click on malicious links? They run quarterly tests to assess how healthy we are from a cybersecurity perspective. And if you click on a phishing attempt and you fall for it, you are directed to a self-service education video that you have to complete, going over the aspects of this phishing test, what made it malicious. And then you're taught to educate yourself on what to look for in the future. We really need to be doing something very similar with data. And it doesn't mean that you host a two-hour training and then never talk about data again. You really need to look at ways to weave data governance into the fabric of your organization so that it is not disruptive to anybody's day. It's a natural part of their day, and it is part of working at your organization. Part of your organizational goals include having people serve as data stewards. And you emphasize that stewardship is for everyone, not just the people in the technology side of the business. VICTORIA: I love that. And I think there's something to be said for having more people involved in the data process and how that will impact just the quality of your data and the inclusivity of what you're building to bring those perspectives together. LAUREN: I agree. And that's the real goal. And I think this is, again, something that's actually easier for startups to do because startups are naturally more nimble. They find out what works, what doesn't work. They're willing to try things. They have to be willing to try things. Because, to use a really clichéd phrase, if they're not innovating, then they're going to get stale and go out of business. But the other benefit that I think startups have when they're doing this work is the small size. Yes, you don't have the budget or team size of a company like JP Morgan, that is enormous, or a big bank. But you still have an opportunity to really design a culture, an organizational culture that puts data first, regardless of role. And then you can architect the structure of every role according to that vision. And I think that's a really exciting opportunity for companies, especially if they are selling data or already giving data as a product in some way. If they're selling, you know, data as a product services, this is a really great approach and a unique approach to solving data governance and making it everyone's opportunity to grow their own roles and work smarter. VICTORIA: Right. And when it's really the core of your business, it makes sense to pay more attention to that area [laughs]. It's what makes it worthwhile. It's what makes potential investors know that you're a real company who takes things seriously. [laughs] LAUREN: That's true. That's very true. VICTORIA: I'm thinking, what questions...do you have any questions for me? LAUREN: I'm curious to know, when you talk to thoughtbot clients, what are the main aspects of data that they struggle with? I hear a variety of reasons for data struggles when I talk to clients, when I talk to people on the tech side, either as engineers or architects. I'm curious to hear what the thoughtbot community struggles with the most when it comes to managing big data. VICTORIA: I think, in my experience, in the last less than a year that I've been with thoughtbot, one challenge which is sort of related to data...but I think for many small companies or startups they don't really have an IT department per se. So, like, what you mentioned early on in the discovery process as, like, who is the most senior technical person on your team? And that person may have little to no experience managing an IT operations group. I think it's really bringing consulting from the ground up for an organization on IT operations, data management, user and access management. Those types of policies might just be something they hadn't considered before because it's not in their background and experience. But maybe once they've gotten set up, I think the other interesting part that happens is sometimes there's just data that's just not being managed at all. And there are processes and bits and pieces of code in app that no one really knows what they are, who they're used for, [laughs] where the data goes. And then, you know, the connections between data. So everything that you're mentioning that could happen when you don't do data governance, where it can slow down deployment processes. It can mean that you're giving access to people who maybe shouldn't have access to production data. It can mean that you have vulnerabilities in your infrastructure. That means someone could have compromised your data already, and you just don't know about it. Just some of the issues that we see related to data across the spectrum of people in their lifecycle of their startups. LAUREN: That makes total sense, I think, especially when you are in a startup. If you're going by the typical startup model, you have that business-minded founder, and then you likely have a more technical co-founder. But we, I think, make the assumption that if you are, quote, unquote, "technical," you, therefore, know how to do anything and everything about every system, every framework, every type of cloud environment. And we all know that that's just not the case. And so it's easy to try to find the Chief Technology Officer or the Chief Information Officer if one exists and to think, oh, this is the right person for the job. And they might be the most qualified person given the context, but that still doesn't mean that they have experience doing this work. The reality is that very few people today have deep hands-on experience making decisions about data with the volume that we see today. And so it's a new frontier for many people. And then, on top of that, like you said as well, it's really difficult to know where your data lives and to track it. And the amount of work that goes into answering those very basic questions is enormous. And that's why documentation is so important. That's why data lineage in your architecture is so important. It really gives you a snapshot of which data lives where, how it's used. And that is invaluable in terms of reducing technical debt. VICTORIA: I agree. And I wonder if you have any tips for people facilitating conversations in their organization about data governance. What would you tell them to make it less scary and more fun, more appealing to work on? LAUREN: I both love and hate the term data governance. Because it's a word that you say, and whether you are technical or not, many people tune out as soon as they hear it because it is, in a way, a scary word. It makes people think purely of compliance, of being told what they can't do. And that can be a real challenge for folks. So I would say that if you are tasked with making a data governance program across your organization, you have to invest in making it real for people. You have to sell them on stewardship by articulating what folks will gain from serving as stewards. I think that's really critical because we are going to be asking folks to join a cause that they're not going to understand why it affects them or why it benefits them at first. And so it's really your job to articulate not only the benefits to them of helping to set up this data stewardship work but also articulating how data governance will help them get better at their jobs. I also think you have to create a culture where you are not only encouraging people to work across party lines, so to speak, to work across silos but to reward them for doing so. You are, especially in the early months, asking a lot of people who join your data stewardship initiatives and your data governance council you're asking them to build something from the ground up, and that's not easy work. So I think any opportunity you can come up with to reward stewards in the form of bonuses or in terms of giving them more leeway to do their jobs more of a title bump than they might have had otherwise. Giving them formal recognition for their contributions to data governance is really essential as well. Because then they see that they are rewarded for contributing to the thought leadership that helps the data governance move forward. VICTORIA: I'm curious, what is your favorite way to be rewarded at work, Lauren? LAUREN: So I am a words person. When we talk about love languages, one of them is words of affirmation. And I would say that is the best way to quote, unquote, "reward me." I save emails and screenshots of text messages and emails that have really meant a lot to me. If someone sends me a handwritten card that really strikes a chord, I will save that card for years. My refrigerator is filled with holiday cards and birthday cards, even from years past. And so any way to recognize people for the job they're doing and to let someone know that they're seen, and their work is seen and valued really resonates with me. I think this is especially important in remote environments because I love working from home, and I am at home alone all day. And so, especially if you are the only person of your kind, of your role on your team, it's very easy to feel insular and to wonder if you're hitting the mark, if you're doing a good job. I think recognition, whether verbally or on Slack, of a job well done it really resonates with me. And that's a great way to feel rewarded. VICTORIA: I love that. And being fully remote with thoughtbot, I can feel that as well. We have a big culture of recognizing people. At least weekly, we do 15Five as a tool to kind of give people high-fives across the company. LAUREN: Yep, Steampunk does...we use Lattice. And people can submit praise and recognition for their colleagues in Lattice. And it's hooked up to Slack. And so then, when someone submits positive feedback or a kudos to a colleague in Lattice, then everyone sees it in Slack. And I think that's a great way to boost morale and give people a little visibility that they might not have gotten otherwise, especially because we also do consulting work. So we are knee-deep in our projects on a daily basis, and we don't always see or know what our colleagues are working on. So little things like that go a long way towards making people feel recognized and valued as part of a bigger company. But I'm also curious, Victoria, what's your favorite way to get rewarded and recognized at work? VICTORIA: I think I also like the verbal. I feel like I like giving high-fives more than I like receiving them. But sometimes also, like, working at thoughtbot, there are just so many amazing people who help me all throughout the day. I start writing them, and then I'm like, well, I have to also thank this person, and then this person. And then I just get overwhelmed. [laughs] So I'm trying to do more often so I don't have a backlog of them throughout the week and then get overwhelmed on Friday. LAUREN: I think that's a great way to do it, and I think it's especially important when you're in a leadership role. Something that I'm realizing more and more as I progress in my career is that the more senior you are, the more your morale and attitude sets the tone for the rest of the team. And that's why I think if you are in a position to lead data governance, your approach to it is so crucial to success. Because you really have to get people on board with something that they might not understand at first, that they might resent it first. This is work that seems simple on the surface, but it's actually very difficult. The technology is easy. The people are what's hard. And you really have to come in, I think, emphasizing to your data stewards and your broader organization, not just what governance is, because, frankly, a lot of people don't care. But you really have to make it tangible for them. And you have to help them see that governance affects everyone, and everyone can have a hand in co-creating it through shared standards. I think there's a lot to be learned from the open-source community in this regard. The open-source community, more than any other I can think of, is the model of self-governance. It does not mean that it's perfect. But it does mean that people from all roles, backgrounds have a shared mission to build something from nothing and to make it an initiative that other people will benefit from. And I think that attitude is really well-positioned for success with data governance. VICTORIA: I love that. And great points all around on how data governance can really impact an organization. Are there any final takeaways for our listeners? LAUREN: The biggest takeaway I would say is to be thoughtful about how you roll out data governance in your organization. But don't be scared if your organization is small. Again, it's very common for people to think my business is too small to really implement governance. We don't have the budget for, you know, the AWS environment we might need. Or we don't have the right number of people to serve as stewards. We don't actually have many data domains yet because we're so new. And I would say start with what you have. If you are a business in today's day and age, I guarantee that you have enough data in your possession to start building out a data governance program that is thoughtful and mission-oriented. And I would really encourage everyone to do that, regardless of how big your organization is. And then the other takeaway I would say is, if you remember nothing else about data governance, I would say to remember that you automate your standards. Your standards for data quality, data destruction, data usage are not divorced from your technical team's production environments; it's the exact opposite. Your standards should govern your environment, and they should be a lighthouse when you are doing that work. And so you always want to try to integrate your standards into your production environment, into your ETL pipelines, into your DevSecOps. That is where the magic happens. Keeping them siloed won't work. And so I'd love for people, if you really enjoyed this episode and the conversation resonated with you, too, get a copy of the book. It is my first book. And I was really excited to work with the Pragmatic Programmers on it. So if readers go to pragprog.com, they can get a copy of the book directly through the publisher. But the book is also available at Target, Barnes & Noble, Amazon, and local bookstores. So I am very grateful as a first-time author for any and all support. And I would really also love to hear from thoughtbot clients and podcast listeners what you thought of the book because version two is not out of the question. VICTORIA: Well, looking forward to it. Thank you again so much, Lauren, for joining us today. You can subscribe to the show and find notes along with a complete transcript for this episode at giantrobots.fm. If you have questions or comments, email us at hosts@giantrobots.fm. And you can find me on Twitter @victori_ousg. This podcast is brought to you by thoughtbot and produced and edited by Mandy Moore. ANNOUNCER: This podcast is brought to you by thoughtbot, your expert strategy, design, development, and product management partner. We bring digital products from idea to success and teach you how because we care. Learn more at thoughtbot.com. Special Guest: Lauren Maffeo.
Lauren Maffeo is an award-winning writer and designer whose first book, "Designing Data Governance from the Ground Up," is in beta and debuted at #1 in the publisher's catalog. Top 3 Value Bombs: 1. Be honest about what the book is and how you want it to be received. That involves being honest about the fact that your book isn't for everyone. It allows you to define your audience and what you want them to gain from the book. 2. You can't do the fun stuff in tech without data and without good quality data. That really is the backbone of every strong company today. 3. It's very different to write a book with a consistent narrative that flows between chapters and each chapter builds upon each other. Visit and get Lauren's book - Designing Data Governance from the Ground Up Sponsors: FranBridge: Jon Ostenson, founder of FranBridge Consulting and top 1% consultant, represents the premier source for the best opportunities in the non-food franchise world. Sign up for a free consultation at FranBridgeConsulting.com! HubSpot: Learn how HubSpot can help your business grow better and get a special offer of 20% off on eligible plans at HubSpot.com/eof!
Lauren Maffeo is an award-winning writer and designer whose first book, "Designing Data Governance from the Ground Up," is in beta and debuted at #1 in the publisher's catalog. Top 3 Value Bombs: 1. Be honest about what the book is and how you want it to be received. That involves being honest about the fact that your book isn't for everyone. It allows you to define your audience and what you want them to gain from the book. 2. You can't do the fun stuff in tech without data and without good quality data. That really is the backbone of every strong company today. 3. It's very different to write a book with a consistent narrative that flows between chapters and each chapter builds upon each other. Visit and get Lauren's book - Designing Data Governance from the Ground Up Sponsors: FranBridge: Jon Ostenson, founder of FranBridge Consulting and top 1% consultant, represents the premier source for the best opportunities in the non-food franchise world. Sign up for a free consultation at FranBridgeConsulting.com! HubSpot: Learn how HubSpot can help your business grow better and get a special offer of 20% off on eligible plans at HubSpot.com/eof!
Jessi sits down with listener Lauren Maffeo to discuss Lauren’s transition from journalism to civic tech, and her advice for others interested in making a career change.
GetApp is an online resource for businesses exploring software products. Its comparison shopping platform and free interactive tools help buyers compare software products side-by-side and navigate the world of software purchasing. GetApp features software research, insights, trends, and validated user reviews, giving buyers the tools they need to make informed decisions for their organization. GetApp is also a Gartner company. But it was their State of Analytics in HR report that caught my eye. GetApp recommends that employers can set their teams up for success in 2020 by auditing HR processes and then using analytics to improve these processes and reviewing embedded analytics features to see what tools are already at the team’s disposal. Employers can also use data analytics to improve staff retention. For example, some employers may find that there is a correlation between longer commute times and the likelihood to quit. To prevent staff from leaving, employers can have proactive conversations with employees and offer more flexible working options. Using analytics to spot these trends could be very beneficial for companies, as employee turnover can cost a 100-person business up to $2.6 million per year. Lauren Maffeo, an associate principal analyst at GetApp, joins me on the Tech Talks Daily Podcast to talk about all this and much more. Lauren covers the impact of emerging tech like AI and blockchain on small and midsize business owners. She has also been cited by sources such as Information Management, TechTarget, CIO Online, DevOps Digest, The Atlantic, Entrepreneur, and Inc.com. In 2017, Lauren was named to The Drum’s 50 Under 30 list of women worth watching in digital. That same year, she helped organize Women Startup Challenge Europe, which was the continent’s largest venture capital competition for women-led startups. Lauren has served as a mentor for Girls in Technology’s Maryland chapter, and DCA Live included her in its 2018 list of “The NEW Power Women of Tech”. Lauren was also shortlisted for the Future Stars of Tech Award in AI and Machine Learning by Information Age in 2019.
02:26 - Lauren’s Superpower: Remembering Useful Yet Sentimental Facts About People 03:57 - Lauren’s Professional Background 07:35 - Bias in the Downsides of AI * Automation vs. Augmentation * Meredith Broussard (https://en.wikipedia.org/wiki/Meredith_Broussard) 11:15 - Media and AI/How the Media Affects People’s Perception of AI 14:32 - Concerns of Small and Midsize Businesses Pertaining to AI 18:37 - How to Mitigate Bias in AI 22:23 - Ethics in AI * Loomis v. Wisconsin (https://en.wikipedia.org/wiki/Loomis_v._Wisconsin) 25:39 - Defining Bias in AI * Georgetown University Law Center (https://www.law.georgetown.edu/) * Unconscious Bias * Harvard Implicit Bias Test (https://implicit.harvard.edu/implicit/takeatest.html) 32:04 - Fairness vs. Accuracy in Algorithms 38:30 - Preventing Bias in AI Resources * Gartner (https://www.gartner.com/en) * Towards Data Science Blog (https://towardsdatascience.com/) * Github (https://github.com/) 41:00 - Working Remotely * Proactively Communicating * Setting Boundaries 50:45 - Diversity and Inclusion in the Workplace * Slack (https://slack.com/) * Aubrey Blanche, Atlassian (https://www.atlassian.com/) Reflections: John: Lauren talking about the work she’s doing to pre-educate people so they can prevent themselves from getting in trouble even before they build their models. Chanté: It’s not enough to just be doing this internally. Bias happens in all shapes, sizes, and forms and it’s important to recognize that. Jacob: In a biased society we can’t expect completely unbiased data; therefore we can’t train an algorithm on the theoretical equitable world that we want to create. There will always be a trace of the bias we have now. Lauren: The first step is acknowledging the bias exists in the first place. This episode was brought to you by @therubyrep (https://twitter.com/therubyrep) of DevReps,LLC (http://www.devreps.com/). To pledge your support and to join our awesome Slack community, visit patreon.com/greaterthancode (https://www.patreon.com/greaterthancode) To make a one-time donation so that we can continue to bring you more content and transcripts like this, please do so at paypal.me/devreps (https://www.paypal.me/devreps). You will also get an invitation to our Slack community this way as well. Amazon links may be affiliate links, which means you’re supporting the show when you purchase our recommendations. Thanks! Special Guest: Lauren Maffeo.
Lauren Maffeo studies the emerging threats of artificial intelligence in her work as an analyst for GetApp, a software reviews site under Gartner Research that uses proprietary data to help match software buyers with the best tools for their businesses. In these two episodes of The New Stack Makers, Maffeo provides her perspective on artificial intelligence, its power and the threats it poses when unchecked. The top performing companies in the financial markets are using technologies based upon artificial intelligence. These technologies are powerful but can at times prove to pose indirect biases. That can lead to a bank loan getting denied, a passport not issued, a payment getting stopped and a black person getting a longer prison sentence due to the color of their skin.
The threads and threats that come with computer intelligence were apparent to Pamela McCorduck in 1960 as a graduate student in English Literature. Those same threads and threats are apparent today in the biases that can come with black box algorithms and indirect biases that Lauren Maffeo studies in her work as an analyst for GetApp, a software reviews site under Gartner Research that uses proprietary data to help match software buyers with the best tools for their businesses. In these two episodes of The New Stack Makers, McCorduck and Maffeo each provide their perspectives on artificial intelligence, its power and the threats it poses when unchecked.
In this Marketing Over Coffee: Learn about Sentiment Analysis, Modeling Intent, Ethical Debt and more! Direct Link to File Brought to you by our sponsors: Intercom and Trust Insights Associate Principal Advisor at GetApp The five ways sentiment analysis presents itself in software 8:12 Intercom will help you give your customers the one thing they’re looking […] The post Talking Sentiment and Bias with Lauren Maffeo appeared first on Marketing Over Coffee Marketing Podcast.
Are demographics worthless? How do we strike a balance with personalization? What responsibility do companies have with data? In this episode of IBM thinkLeaders podcast, we are joined by David Allison (founder of the Valuegraphics Database) and Lauren Maffeo (associate principal analyst at GetApp). We talk to David and Lauren about what the best way to understand a customer is, tailoring experiences based on behavioral data, and the importance of making consumers aware of how their data is used. We also get into data governance, GDPR, algorithmic bias, gender bias, and whether predictive analytics offends our notions of free will. “Technologies need to understand what we care about, they need to understand what our values are first and foremost, and then start making decisions about some of these finer points around option a, stimulus B, whatever the scenario might be. The root of it though needs to be what we find important.” -David Allison, founder of the Valuegraphics Database “I think there's a general sentiment that we as consumers find it creepy and yet the benefits of saving time and all of that outweigh that sentiment. And until we see a big impact in behavior, AKA people abandoning these top five brands that have all of this data, I think the sentiment only goes so far because thus far we haven't really seen it change people's behavior or engagements with these brands in a significant way.” -Lauren Maffeo, associate principal analyst at GetApp Connect with us: @IBMthinkLeaders @AudienceValues @LaurenMaffeo BIOS DAVID ALLISON David Allison has spent his career helping organizations motivate, influence and engage audiences. He is the founder of Valuegraphics, the world's first database that can verify what your target audience wants and what messages will trigger them to act. The data contains insights from 250,000 surveys about 380 metrics in 59 countries, and will be globally representative by 2020, with a data accuracy and confidence that surpasses benchmarks for any PhD thesis. His bestselling book, WE ARE ALL THE SAME AGE NOW: THE END OF DEMOGRAPHIC STEREOTYPES was listed by INC Magazine as one of the top ten leadership books of the year, and Kirkus reviews called it a "genuinely authentic contribution to the field of marketing literature." LAUREN MAFFEO Lauren Maffeo has reported on and worked within the global technology sector. She started her career as a freelance journalist covering tech trends for The Guardian and The Next Web from London. Today, she works as an associate principal analyst at GetApp (a Gartner company), where she covers the impact of emerging tech like AI and blockchain on small and midsize business owners. She is also a community moderator for OpenSource.comand a member of the ACM's Distinguished Speakers Program. Lauren has been cited by sources such as Information Management, TechTarget, CIO Online, DevOps Digest, The Atlantic, Entrepreneur, and Inc.com. Her writing on technology has also been cited by researchers at Cornell Law School, Northwestern University, and the University of Cambridge. She has spoken at global events including Gartner’s Symposium in Florida, The World Web Forum in Zurich, Open Source Summit North America in Vancouver, and DrupalCon in Seattle. In 2017, Lauren was named to The Drum’s 50 Under 30 list of women worth watching in digital. That same year, she helped organize Women Startup Challenge Europe, which was the continent’s largest venture capital competition for women-led startups. She has served as a mentor for Girls in Technology’s Maryland chapter, and DCA Live included her in its 2018 list of “The NEW Power Women of Tech”. Lauren was also shortlisted for the Future Stars of Tech Award in AI and Machine Learning by Information Age in 2019. Lauren holds an MSc from The London School of Economics and a certificate in Artificial Intelligence: Implications for Business Strategy from MIT’s Sloan School of Management.
E59 - Breaking Into Tech with a Background In Liberal Arts with Lauren Maffeo of GetApp Episode 59 of the Hacker Noon Podcast: An interview with Lauren Maffeo of GetApp Listen to the interview on iTunes, or Google Podcast, or watch on YouTube. In this episode Derek Bernard interviews Lauren Maffeo of GetApp. Lauren shares how she went from a Media Studies degree in Liberal Arts Media to Research Analyst of Technology at GetApp. “I majored in media studies in college pretty intent on going into journalism when I graduated. Unfortunately about halfway through college the recession hit and that was about the same time that ad spend was shifting from news organizations and news sites over to digital websites like Facebook and Google who now own an enormous total collective ad spend” “The business model for a lot of journalism outlets collapsed and what was a competitive industry before became very difficult to enter after the fact.” "In hindsight I think my media studies degree I thought at the time that those degrees were going to prepare me for a career as a reporter, but I actually think they were better preparation for what I do now as a research analyst because that kind of education primes you to look at a market, find gaps within that market, ask critical questions about the status quo and then give solutions for what to do differently. And I see a lot of parallels between my work as an analyst here and what I did academically, and so in hindsight that humanities education was great preparation for a career in tech.” — Lauren Maffeo Host, production, and music by Derek Bernard - https://haberdasherband.com/production https://hackernoon.com/ https://community.hackernoon.com/ https://contribute.hackernoon.com/ https://sponsor.hackernoon.com/ https://podcast.hackernoon.com/ https://twitter.com/hackernoon/ https://facebook.com/hackernoon/ P.S. If you dig the new Hacker Noon Podcast, consider giving us a 5 star review on iTunes. Also check out the top stories from July, the latest stories, and today’s homepage.
Machine bias in artificial intelligence is a known and unavoidable problem—but it is not unmanageable. Scott talks to Lauren Maffeo about practical techniques teams can use to manage priorities in AI. You can monitor your datasets throughout the product lifecycle, focus on the subject, not the context, and more. 6 steps to stop ethical debt in AI product development Lauren on Twitter
Today's guest is Lauren Maffeo, who has spent most of her career reporting on and working within the global technology sector. She started as a freelance journalist covering tech trends for The Guardian and The Next Web from London, and today, Lauren works as an Associate Principal Analyst at GetApp, where she covers the impact of emerging tech like AI and blockchain on small and mid-sized business owners. For more information, visit: http://Brandeis.edu
Lauren Maffeo, software-as-a-service expert and senior content analyst at GetApp, provides some comforting answers to questions surrounding AI and machine learning. No, AI isn't after your job, but big changes are coming down the pipeline.
Links: *GetApp articles *Talk at Women Techmakers Montreal 2018 *Twitter profile In this episode, we are talking to Lauren Maffeo a Technology writer and a diversity & inclusion advocate. Lauren talks about her transition into the tech sector with a liberal arts background. She explains why she chose to earn a certificate in AI for Business Strategy from MIT Sloan. Aspects of how shame plays a role in diversity and inclusion in STEAM fields. Listen to Lauren's interesting STE"A"M story.
Bio: Lauren Maffeo has reported on and worked within the global technology sector. She started her career as a freelance journalist covering tech trends for The Guardian and The Next Web from London. Today, she works as a senior content analyst at GetApp (a Gartner company), where she covers the impact of emerging tech like AI and blockchain on small and midsize business owners. Lauren's research and writing have been cited by sources including Forbes, Fox Business, The Atlantic, and Inc.com. She has spoken at events including Gartner's Symposium in Florida, The Global Talent Summit at ETH Zurich in Switzerland, Women Techmakers Montreal in Canada, and Mashable's Social Media Day. In 2017, Lauren was named to The Drum's 50 Under 30 list of women worth watching in digital. That same year, she helped organize Women Startup Challenge Europe, which was the continent's largest venture capital competition for women-led startups. Lauren Maffeo holds a certificate in Artificial Intelligence: Implications for Business Strategy from MIT's Sloan School of Management. She has consulted and reported as a senior content analyst for GetApp, covering the influence of various emerging technologies, such as blockchain and artificial intelligence, on small to midsize businesses. Maffeo has extensively studied machine learning, natural language processing, and robotics. Maffeo discusses how the perception of AI can sometimes be that it is a monolithic entity devoid of humanity, but in fact, AI has a significant swath of use cases that make it ideal for many human-powered, customer service oriented businesses. Maffeo states that AI is often best when introduced alongside human workers to enhance the overall workflow. Many businesses state that they simply don't have the staffing necessary to implement AI projects, while others state that they have difficulty in defining their overall AI strategy. Still, other businesses state that they are unsure of how to get started with AI altogether. Ultimately, at current, AI is touted as a transformative tool, but hype aside, some businesses are simply struggling to understand how to best utilize it. The technology consultant outlines some of the aspects of AI that will become more advanced and specialized in the coming years, such as AI's social perception and context. While automation with AI will facilitate many industries, such as construction or other industrial types, Maffeo states that the perception of AI taking jobs from human workers is skewed. In fact, in many industries, it is a lack of available human workers that is spurring the advent of AI in certain workplaces. Maffeo discusses the potential impediments to complete implementation of AI in a wider scope. Cost would be an issue, but even before the consideration of cost, there are some other issues to consider. The topic of ‘use cases,' essentially knowing how AI can be used effectively, is a fundamental issue. The need for quality data scientists is also a critical issue—to regulate data in an AI system to ensure that it is healthy, unbiased data. Additionally, high-level architects that are capable of building networks become an issue simply because they are in demand but are not readily available. Maffeo provides further insight into some of the areas of AI that will proliferate. Innovations in advanced AI such as chatbots will be a growing industry in the coming years. Predictions indicate that as many as 10% of new IT hires will be tasked with writing bot scripts as chatbots become ubiquitous in online commerce. Machine learning will allow chatbots to interface with customers at an advanced level as chatbots can access a wealth of data about the user. And although current data demonstrate that the majority of people prefer a human interaction, as AI refines the chatbot experience the expectation is that users will acclimate to the advancing technology.
There are some women that GeekGirl keeps missing! Honestly, we have relationships with women entirely through social who we've never been able to meet! And Lauren is one of these awesome ladies! One of our first remote interviews, GeekGirl was delighted to be joined by Lauren from 'Over the Pond'. Lauren has done a fair bit - we're pretty darn impressed! Her career and studies have taken her around the world, and she has a clear love of travel. She started her career as a freelance reporter covering tech trends in London for The Next Web and The Guardian, and now covers the impact of tech trends like AI on small business owners for GetApp, a Gartner company. In 2017 she helped organize Women Startup Challenge - Europe's largest venture capital competition for women-led startups, and was named to The Drum's 50 Under 30 list of women worth watching in digital in 2017. She's also spoken at events and venues on three continents, including the British Embassy in Washington, DC, and The Global Talent Summit at ETH Zurich in Switzerland. We get her top tips for putting yourself forward and chasing the opportunities ahead of you! There's a load of great advice in here from Lauren and you can reach her on Twitter @laurenmaffeo.
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