Data in Depth

In this episode, we sat down with Carl Osipov with CounterFactual.AI and the author of Serverless Machine Learning In Action. Carl shared some real-world use cases for serverless machine learning and identified strategies to get the most from a machine learning investment. “One of the things that happens at the beginning of a machine learning project — and this is a well-known problem for data scientists and machine learning practitioners — is spending way too much time cleaning up their data sets and focusing on things like data quality instead of actually building out machine learning solutions. I think, as practitioners, machine learning developers and engineers have created a set of techniques over the past few years to help formalize and accelerate this process. But it’s still a concern, especially if you think about scenarios that are common to manufacturing where different data silos have to come together for a machine learning system. This also happens in the scenarios where manufacturers acquire companies and then integrate data and use that data for machine learning systems. What happens is that if companies don’t actually have a rigorous approach for transitioning their machine learning systems code into operations, they find themselves in a situation where data scientists and machine learning engineers actually end up doing a lot of operations involved in putting machine learning systems into production. So what I’m describing here is what I call an ML ops trap. This machine learning operations trap, where these highly compensated practitioners are essentially spending their time working on something that’s not their core competency.” Connect with Carl on LinkedIn.

In this episode, we talk with Ed Kuzemchak from Software Design Solutions. Ed digs into the ways companies can use the Internet of Things (IoT) to increase efficiency. He shares advice on how to identify areas of opportunity to implement IoT and strategies to make the most of an IoT investment. “I think the most important part for a company is to look at systems they have today and say “what part of these systems that we have, can we make more efficient or more cost effective or higher performing if we had better information?’ Cause that's really all that IOT is all about. It's about gaining data where you didn't used to have data or you couldn't get good or up-to-date data. You know, if you had to wait until the reports came back from the field, from your field sales tech or your field service techs on machine failures, you might have a two week lag on machine failures. And the data that you're looking at is always two weeks old. Well, what if it was only five seconds old?”Connect with Ed Kuzemchak on LinkedIn.

In this episode, we sat down with Kyley Darby from Mountain Point and Skye Reymond with Terbium Labs. Kyley and Skye explore how manufacturers can leverage descriptive, predictive, and prescriptive data to optimize business outcomes. They also dig into the ways Salesforce’s Einstein Analytics can help companies better plan for the future. “‘To move forward and look beyond the “what has happened,” manufacturers need to start pulling data together in a centralized manner — to switch from seeing what has happened to “what could happen, what could we change?” I think having data all over the place is something that holds them back.” - Kyley Darby“I’ll add to that, Kyley. In the past, a lot of these methods have been really technical and if you don’t have access to the technical talent that’s necessary, you can find yourself following a predictive model that’s incorrect. This can cause the business to lose a lot of money, time, and effort. That technical talent that can utilize predictive and prescriptive analytics has historically been hard to find. But, fortunately, with things like Einstein, Salesforce is making this skill more accessible to everybody. So I think in the future, you’re going to see more of that, where you don’t need an entire data science team, but a good understanding of Einstein, if you’re a Salesforce user, and what those results are going to mean for your business” - Skye ReymondConnect with Kyley and Skye.

In this episode, we talk with Bastiane Huang with OSARO. Bastiane digs into the practical uses of deep learning and machine learning. She explores beyond the academic applications of machine learning and details some real-world scenarios, including the ability to expand the use of robots in less structured environments. “We use machine learning to allow robots to react to changes in the environment, learn to handle a wide range of different items, and have a range of different tasks. And more importantly, to learn, “Oh! This task [required] minimum human supervision.” So this way, you can really save a lot on human costs and on a lot of the surrounding systems,. These kinds of surrounding systems are usually more than four to five times the robot costs, so it's really significant. And lastly, it also enables robots to be used in new use cases. For example, you don't really see robot arms being used in warehouses right now. Because in a typical warehouse that has millions of different products it’s not feasible to program a robot. You're able to deal with a million different products in a million different ways. So now, because of machine learning, robots can be used in this kind of less structured environment. ”Connect with Bastiane on LinkedIn and Medium.

In this episode, we talk with Alex Reneman with Mountain Leverage. Alex explores the importance of innovations like voice-directed solutions in the midst of a global pandemic when the supply chain may be disrupted. “There's a lot of tribal knowledge that sits in some workers that you can put into a system. So we take some of that data and put it into a voice system to voice-enable the process. So, for example, if you have a flex worker who is fully trained at one station and maybe they're only partially trained on another station, they really wouldn't be able to fill in there. But if you add a display with voice that walks them through the process, maybe they’re less efficient than if they were fully trained on the station, but they’re able to get through. So that's something that's pretty impressive normally. But then put the COVID-19 lens over it, and now that individual can be effective while maybe their partner is out with COVID-19 or unavailable based on distancing or different things. That's where we’re finding some of these solutions really interesting at these times.”Connect with Mountain Leverage on LinkedIn.

In this episode, we talk with Mendy Ezagui with Nucleus Technologies and Rapid Logistics Couriers. Mendy digs into strategies manufacturers can employ to customize automation and streamline internal processes to increase productivity.“When you review a [new] software, put it into the hands of some of your users and say: ‘Hey, check this out. Is it simple? Are you getting your job done much better than the way you're doing it right now? Is this efficient? Can you walk through your steps, far more easily than you have up to this point and could you put in the information that's important?’ Obviously in the back end of that, as an admin, as an operations leader, as a manager, as an executive, you want to review that information almost immediately and see if it's accurate. But it shouldn't be underestimated. [There’s an] incredible importance [in having ] a user experience that's intuitive to the person who's using it because that is really where much of the bad data comes from.Connect with Mendy on LinkedIn.

In this episode, we talk with Lisa Arthur, marketing expert and advisor for Scoutbee. Lisa digs into strategies manufacturers can use right now to create or expand on a data-driven marketing strategy. That strategy starts with not marketing to customers, and instead, using data to create informed buyers.“The first step around getting strategic, and building that foundation for data-driven marketing, is really deeply understanding those buyers and prospects. Understanding what they find of value. And then building the vision and the strategy around how your products and services can actually meet and exceed those needs. So, that's where you can use that virtual internal force, to pull together some of those insights and touchpoints.”Connect with Lisa on LinkedIn.

In this episode, we talk with Michael Cromheecke from SteamChain to discuss blockchain and machine-as-a-service. Michael breaks down how this model improves machine productivity and performance, with reduced risk and little up-front capital. “We use the same data management mechanism, the blockchain technology that enables cryptocurrencies to exist, but we apply it to machine-as-a-service. And what that creates for us is a record that can be shared between organizations, between corporations, between businesses in a way that's transparent to all parties. That's objective, the data that goes in, you know, it's gonna be the same data three years, five years, 10 years from now. It's resilient over time. And the big important thing is it doesn't allow one party to restrict access from the other party. So both parties truly have shared ownership of that data. No one party can change it to the disadvantage of the other. No one party can turn off access to the disadvantage of the other.”Connect with SteamChain on LinkedIn.

In this episode, we talk with Zach Boyd from Hirebotics a company that allows manufacturers to hire a robot for hourly work like they would an employee. Zach explores the ways that data and the Internet of Things (IoT) can be harnessed to provide proactive and preventative remote support and maintenance to their customers. “We Cloud Connect our robot so that we can gain real-time insights into how that robot's performing, what problems it might have. And with that, we're able to 98% of the time, come to a resolution, identify that root cause and provide a fix from a mobile application that we've developed versus sending somebody on a plane to go help that customer.”Connect with Zach on LinkedIn.

In this episode, we talk with Tyson Higginbotham from Supply Source Solutions. Tyson shares tips for manufacturers to increase the ease of doing business (EODB) for their end-users and effectively implement an eCommerce model. “We went from a manufacturing age to a digital consumer age over the course of the last 15 years...I think the big change that really happened — the hardest part for both the wholesaler as well as the manufacturer to understand — is the end-user consumer is no longer calling that wholesale distributor to have a salesperson come visit them at their office or location and bring a catalog with different sell sheets to sell them on the value proposition of that manufactured good. They are now able to access all of that information at the point of their buying journey when they are at the point of awareness and then consideration and evaluation. They do that on the manufacturer's website because they wanna go straight to the source to understand truly the value in features and benefits of what that product is.”Connect with Tyson on LinkedIn.

In this episode, we talk with Nick Humpries from Zeiss. Nick shares strategies to head off waste and ensure quality by integrating datasets to monitor production in real-time.“The time that they used to spend wearing out a path between their workstation and the quality lab, is now all spent at their workstation and they are able to make adjustments in a much quicker fashion. And then the data integrity increased because they were able to mask those first few parts that they ended up scrapping. So we saved them a lot of time, for one, and then a lot of money in scrap as well.”.Connect with Nick on LinkedIn.

In this episode, we talk with Clark Richey from FactGem. Clark offers insights into how manufacturers can take small — but effective — steps to break down data silos for a more complete view of the company."Information is now being connected, and that's what we've come to expect. And successful businesses are doing that really well. Whether it's in the manufacturing space, supply chain, retail, they're connecting all the dots. And that's where those silos lead to problems. We're separating those dots, and we have hard walls between them. So we need to figure a way to reconnect those so we can create that larger context to allow the business to make decisions between those silos. So to understand, for example, how does my inventory affect my ability to deliver and manage my supply chain and not to see them as separate pieces of the puzzle?"Connect with Clark on LinkedIn. ---

In this episode, we talk with Caroline Hilla from Cisco's Global Manufacturing team. Caroline offers practical and foundational tips for capturing, integrating — and effectively using — IoT data. "There's a perception right now that if it's not broke, don't fix it. And while that may have worked for 15, 20 years now, what we're really seeing is that you're not really able to tap into the full potential of the data that you can be collecting from your different devices and machines using the internet of things until you've really been able to use it and actually unite those data islands... And when it comes to business productivity, I mean, this is huge. This is what allows you to operate as efficiently as possible. This is what allows you to reduce downtime. And ultimately, this allows you to get your products out the door faster and tap into new markets that your competitors have not been able to reach yet." - Caroline Hilla, CiscoConnect with Caroline on LinkedIn.Listen in to the Cisco Manufacturing Leaders Podcast

"Too many companies I've seen initially get into this thinking they're gonna have some 12, 18-month ROI... they're gonna put a bunch of data in and all of a sudden they're going to have some super whiz-bang thing happening. And it's definitely not that. It's a long-term journey."In this episode, we talk with John Clevenger, Managing Director of Accession Consulting. John shares the many opportunities the Internet of Things (IoT) brings to the manufacturing industry. He offers practical tips for getting started and outlines what NOT to do or expect when implementing an IoT strategy. Connect with John on LinkedIn.Check out Accession Consulting.---This season, we're giving away a pair of Bose QuietComfort 35 wireless headphones! How to enter: To be eligible to win, you must complete ALL of the following steps by 11/11/19.Subscribe on ANY of the following:Apple PodcastsStitcherGoogle PlaySpotifyAlexa/Tune InReview us on Apple Podcasts or Stitcher;Follow us on Twitter @DataInDepth; andTweet us letting us know when you’ve completed all the steps. Be sure to mention @DatainDepth. Full details >

"Since we've started this, we're literally adding tens of thousands of dollars of extra revenue when our product ships." In this episode, we talk with Chris Muto, Sales Operations Manager for Pro-Tech, a manufacturer of industrial snow plows. Chris shares how Pro-Tech is using data to improve customers' experience, drive additional sales, and do more with less.Connect with Chris on LinkedIn.Check out Pro-Tech.---This season, we're giving away a pair of Bose QuietComfort 35 wireless headphones! How to enter: To be eligible to win, you must complete ALL of the following steps by 11/11/19.Subscribe on ANY of the following:Apple PodcastsStitcherGoogle PlaySpotifyAlexa/Tune InReview us on Apple Podcasts or Stitcher;Follow us on Twitter @DataInDepth; andTweet us letting us know when you’ve completed all the steps. Be sure to mention @DatainDepth. Full details >

In this episode, we talk with Francois Gau, owner and CEO of Levy Industrial. This episode is the second of a two-part segment with Francois. During this portion of the interview, Francois digs into the concept of Inbound Marketing and explains why manufacturing and industrial companies need to focus messaging on thought leadership rather than selling. He shares key metrics companies can use to gain insight into the effectiveness of their efforts and connect marketing investments to sales and profits. Connect with Francois on Twitter or LinkedIn.Check out Levy Industrial.---This season, we're giving away a pair of Bose QuietComfort 35 wireless headphones! How to enter: To be eligible to win, you must complete ALL of the following steps by 11/11/19.Subscribe on ANY of the following:Apple PodcastsStitcherGoogle PlaySpotifyAlexa/Tune InReview us on Apple Podcasts or Stitcher;Follow us on Twitter @DataInDepth; andTweet us letting us know when you’ve completed all the steps. Be sure to mention @DatainDepth.

In this episode, we talk with Francois Gau, owner and CEO of Levy Industrial. Francois outlines his data-driven approach for generating growth for industrial and tech-based B2B companies. This episode is the first of a two-part segment with Francois. During this portion of the interview, Francois paints a picture of what a modern marketing program looks like in the context of the manufacturing industry. He outlines how manufacturers can move beyond the old standbys of trade shows and distributor newsletters — digging into concepts like e-commerce, engagement with the end customer, and marketing automation. And he shares his tried and true tips for market mapping and customer segmentation. Behind it all? Data, data and more data. You won't want to miss it! Connect with Francois on Twitter or LinkedIn.Check out Levy Industrial.---This season, we're giving away a pair of Bose QuietComfort 35 wireless headphones! How to enter: To be eligible to win, you must complete ALL of the following steps by 11/11/19.Subscribe on ANY of the following:Apple PodcastsStitcherGoogle PlaySpotifyAlexa/Tune InReview us on Apple Podcasts or Stitcher;Follow us on Twitter @DataInDepth; andTweet us letting us know when you’ve completed all the steps. Be sure to mention @DatainDepth. Full details >

In this episode, we talk with Louis Columbus, a Forbes columnist and principal at IQMS. Louis talks through five ways artificial intelligence and machine learning are revolutionizing the manufacturing industry — from marketing and sales to efficiency, from addressing the talent gap to ensuring quality, safety and security. 13:24 - Labor Challenges I visit a lot of manufacturers. I visit probably five to 10 manufacturers a month, and I periodically do surveys every quarter as part of my role at Dassault IQMS. We did a survey and we asked what are the top impediments to your growth in 2019, and we asked them in April. We asked 150 North American discrete manufacturers in the mid tier of the market, and number one at 67% was we don't have enough people. Labor is incredibly challenging right now for the mid tier, the American manufacturing or the North American manufacturing.19:21 - Zero Trust SecurityZero trust security by definition is always verify, never trust approach to every security perimeter on a manufacturing location, and what zero trust security does is it verifies every device and it treats every device and every identity as a new part of the security perimeter. And so with the growth more and more of real time monitoring of machinery and every various threats or if it's that a manufacturer has being exposed, it's really critical to be able to protect it down to that specific device level.22:38 - AI and Machine Learning Impacts on SafetyNow what's really fascinating when you go and walk shop floors and you meet manufacturers who have invested anywhere from five to 10 million dollars in new smart connected machinery. That machinery's got all kinds of safeguards in it. But more importantly, those machines have the ability to heal themselves, but also have their own innate operating systems. And how this all relates to safety is they will tell you if they are getting to a heat point or to a point with their own metrics of like, hey, this may get borderline unsafe in this environment, so therefore I'm gonna shut myself off. Or, therefore I'm gonna send you an alert to the quality manager. Safety is much more sophisticated than many opponents of AI and machine learning and manufacturing give it credit for, much, much more sophisticated.---This season, we're giving away a pair of Bose QuietComfort 35 wireless headphones! How to enter: To be eligible to win, you must complete ALL of the following steps by 11/11/19.Subscribe on ANY of the following:Apple PodcastsStitcherGoogle PlaySpotifyAlexa/Tune InReview us on Apple Podcasts or Stitcher;Follow us on Twitter @DataInDepth; andTweet us letting us know when you’ve completed all the steps. Be sure to mention @DatainDepth. Full details >

In this episode, we talk with Mike Wertheim from Hayward Industries. Mike shares how Hayward is using data to boost the company's bottom line and outlines his team's top priorities for 2020. 3:41: 3 Ways Data Can Drive ProfitsMike: Data is important to everybody. For Hayward, we are a manufacturing company and it comes down to the bottom line. So, I'll give you 3 ideas of some things that make data very important to [us]. We are a big company in a mature market. The way to grow in that market is through acquisition. When you acquire a company and there's a different set of data, there's a lot of problems. The other thing is we have traditionally been a B2B company. And we're trying to compete in this age of online purchasing and there are so many channels that people buy their products from. To do that, we've really got to go deeper and reach out to the actual consumers when possible. The last thing is technology. IoT is a big thing, but we're trying to do more than just make the next great product. We're trying to gather the data that's going to help us make business decisions, grow business, and use those smart devices for our business intelligence.6:13: Mergers, Acquisitions, and Master Data ManagementMike: I've been with the company 5 years and [during that time] we have acquired 5 different companies. That gives us a major focus on things like master data management, where we've got to all be talking the same language. 9:47: B2B2CMike: We're so blind when it comes to selling our products through a distribution channel. So now in conjunction with our distributors, we are actually collecting data about the products that they sell to their customers. That data is so valuable. It's probably the number one thing that our executives are looking for. 15:16: How IoT is Driving Service and Product DevelopmentMike: We have a chemical monitoring system, a floating connected device. The consumers have an app where they can see what's going on and they can share that information with servicers. So servicers can see... "oh, you're having problems. Maybe I should come out and help you." The other thing we do is we sell chemicals in what you would think of as pods, like Tide pods. They're color-coded. So your connected device tells you, "Oh, you've got an issue. Please drop in 2 blue, 3 green and a red into your pool." So, we're connecting on the service side, and we're connecting on the consumable side. ---This season, we're giving away a pair of Bose QuietComfort 35 wireless headphones! How to enter: To be eligible to win, you must complete ALL of the following steps by 11/11/19.Subscribe on ANY of the following:Apple PodcastsStitcherGoogle PlaySpotifyAlexa/Tune InReview us on Apple Podcasts or Stitcher;Follow us on Twitter @DataInDepth; andTweet us letting us know when you’ve completed all the steps. Be sure to mention @DatainDepth. Full details >

On this episode of Data in Depth, we dig into how sophisticated data and analytics are transforming logistics and changing the way companies manage their supply chains. We talk with James Lumb, CEO of Zenkraft, who offers cutting-edge examples of how logistics data can be used to avoid waste, improve customer experience, and even improve your product line. Chapter markers: 4:48 - Supply Chain 3606:05 - Internet of Things6:54 - Product Improvement Feedback Loops8:55 - The Power of Data Integration9:57 - 10x ROI12:03 - Customer Engagement Opportunities13:10 - Artificial Intelligence and Data Sharing in Logistics14:01 - Quoting Shipments in CPQ15:34 - Win a Pair of Bose Headphones---We want to say “THANK YOU” for subscribing and following the first season of Data in Depth. So we’re giving you the chance to win some great prizes. One lucky listener will snag a pair of Bose QuietComfort 35 wireless headphones! On top of that, we’re giving away other awesome swag including Yeti insulated coffee mugs! How to enter:To be eligible to win, you must complete ALL of the following steps.Subscribe on ANY of the following platforms:Apple PodcastsStitcherGoogle PlaySpotifyAlexa/Tune InProvide a review on Apple Podcasts or Stitcher;Follow us on Twitter @DataInDepth; andTweet us letting us know when you’ve completed all the steps. Be sure to use the #DataInDepth hashtag and mention @DatainDepth. Enter by November 11, 2019. Full contest details >

In this episode, we talk with Tom Brennan from Rootstock. Tom discusses the pitfalls of 'FrankenCloud' (multiple cloud systems roughly connected). He also digs into the role of the data-driven CFO and shares how companies can move from a transactional to a strategic approach. FrankenCloud - 3:45Tom: People are adding more clouds, and we're in a 'FrankenCloud' stage where you've got multiple clouds and on-premise. You try to draw inferences about that customer and it's very difficult to do. Andrew: I agree. I'd argue it's probably the number one problem that we see. Managing Acquisitions and Mergers - 6:32Andrew: These disparate systems are often the results of acquisitions. I often feel like there's not enough time, energy, or money that's being spent to think about that as a part of the acquisition strategy. Tom: Yeah. You know, some companies will replace their ERP system wholesale and move everything to one cloud to solve the problem... [Or] they can put in another ERP system such as ours alongside Salesforce pretty easily. Because if they have Salesforce in place, they've already got a cloud stack, and they already know how to administer users, they know how to write reports, do workflow, use chatter... So adding another piece-system is not as intrusive as it would be otherwise, where you'd have to put in a brand new stack, new skillset, new everything. So if people want to get there incrementally, they can, they can add on an app like ours into their Salesforce environment. Artificial Intelligence in ERP - 9:06Tom: ... I think AI, in particular, provides the ability to triangulate all this information that we have about a customer and to predict what's going to go on. And so you'll be able to look easily — and especially when it's all in one platform — across maybe outstanding opportunities for the customer, across quotes... And then look at service cases and activities in your call center. Then maybe look at shipments made or returns that have happened... things under warranty, uh, credits that have happened, where they are in their payment cycle, how good of a payer they are... Early Warning Signs - 10:37Tom: This is one way to get an early warning sign as to what's really going on. The customer may be ordering a lot of things, but returning a lot. There's a lot of credits on it after rebates and things like that. And they're not profitable. So you really need all of what's in ERP and all of what's in CRM to get that view.The Data-Driven CFO - 12:18Tom: [We've] been looking at how finance pros can move beyond doing the day-to-day transactional things and into a more strategic role. One of the critical underpinnings to making that move is data. These CFOs are "data masters." They're able to provide more insight as to what's going on as opposed to just saying, here's your P and L.Links:Connect with TomLearn more about RootstockBrian Sommers on FrankensoftHow to be an effective finance business partner: Insights for manufacturing CFOsHow to get started with Cloud ERP

On this episode, we talk with Ben Cheng from Parsable. Ben delves into the world of the connected worker. He shares ideas for how we can capture the experiences and knowledge of people on the shop floor to develop shared best practices, anticipate future problems, and improve efficiency. Here are a few highlights: Shop KnowledgeBen: During my years in planning, I was always very frustrated with the way that the shop floor workers were being asked to operate. The investment was continually going into our office workers, but we were never really spending money on automating or helping out our knowledge workers [in the shop], the guys that are actually doing the work and are responsible for throughput. We never really thought about how do we make their lives easier.A New Role for WorkersBen: I think the factory of the future is extremely exciting. If you fast forward all the way down to a complete lights out factory, in which there is virtually no humans whatsoever, right? What you actually find is it increases the value of the human even more because in the inopportune time that the factory goes down, then the human has to be involved. And you can only imagine the level of automation and the line speeds that are in place in a factory like that and how many units are getting produced. And it only puts more relevance on the human to fix it correctly, right the first time.Andrew: Absolutely. This technology doesn't replace workers per se. It's more about empowering them to do different aspects of their job than they were previously asked to do. So, more manual work — robots and machines are capable of doing that. The workers of the future need to understand the data and all these streams [and to know] how the shop floor is optimized. Then make those decisions to ensure that development times and changeovers and things that you just described are working to full effect.X DataBen: So there's this concept of “O” data and “X” data, right? O data is operational data. That's traditionally your ERP data, your IoT data. Basically it tells you what happened. But not why or how it happened. O data is just table stakes, right? It's your ticket to entry into this overall game. What’s really exciting now is X data. Which is experience-based data...What I’d really like to start seeing is capturing X data too. Which is really uncharted territory on the shop floor. Experience data is where I feel we're going to achieve a lot of return on investment.Capturing and Sharing Worker Knowledge and ExperienceBen: So when we think about the personal knowledge and experiences of someone like “the gray beard” [in your shop]. He's the guy who can touch a machine and just from the vibration is able to diagnose the problem as well as repair it. So that's fantastic, but it's not scalable. Right? So how do you then capture that data and recycle it back to the new generation of workers?Ben: [And then there’s] the company knowledge. What we always call the best practices. Shop procedures and such. And right now, that’s the last mile that’s often not digitized. Links: Connect with BenConnect with ParsableGoing Digital? Prioritize Talent over Tech

In this episode, we talk with Shekar Hariharan, VP of Product Marketing at Jitterbit. Shekar highlights the crucial role data integrations and APIs play in driving innovation, efficiency, and agility in the manufacturing sector. 2:57 - Digital DisruptionShekar: Manufacturing, just like any other industry, is being impacted by digital disruption...there's data scattered across different systems, and… unless you connect these systems together, you have what you call data silos. 3:58 - The MatrixAndrew: I have this mental image of disparate databases housing lots of data, machines on the shop floor streaming lots of data, suppliers in your supply chain both upstream and downstream... and it just seems like the scene from the Matrix where you see all this data just flowing everywhere and you don't really know how to make sense of it. 7:40 - Integration Use CasesShekar: So, let's break it down into the business goals of why a company would connect data. Streamlining product design and development could be one use case… a great example is retail, where a clothing that is in fashion today may not be six months from now. So, the way you handle that is to set up your systems to support agile manufacturing. So, a product is designed and that's pushed by the design team into production. The production team does a prototype. That goes into quality and testing and eventually into production and deployment, and then you get feedback from your customers on how they like it. That input comes from various channels like we talked about earlier... social, [and sales] and different data points. So just there, you have one use case where companies are trying to streamline their design and development through integration and how the data flows through different systems, from PLM to production, distribution, and back to the design team for continuous improvement. 11:04 - IoTAndrew: I think that's a good segue into the Internet of Things, and the blending of machines and humans. 12:03 - Predictive MaintenanceShekar: Yeah, absolutely. A great example would be a machine that is working 'round the clock because companies can’t afford downtime. And then, eventually, for you to do preventative maintenance, you need to know the cycle time, maybe other variables are of interest, like your temperature, your pressure... You need data to come in real time from these devices back to tracking systems so that way you can plan. 13:30 - Proactive ServiceAndrew: This is also giving manufacturers more power to provide additional services and support to the end customer. So, getting more into the predictive aspect of the business so that they can proactively go out and service or support things before a customer even knows that they may be experiencing downtime.14:40 - Industry 4.0 Starts with IntegrationShekar: At Jitterbit, we surveyed hundreds of manufacturers globally, and the insight we got is only about 1/3 have a cohesive strategy to implement Industry 4.0. At the end of the day, if you want to get more ROI, if you want to deliver great customer experiences, digitize your processes, be agile enough to respond to changing market needs... if you want to do all of that, you have to have a strategy in place. And that strategy starts with integration.Links: From the shop floor to customer experience, data drives manufacturingConnect with Shekar

In our first ever episode, we talk with data scientist Skye Reymond. Skye lays the groundwork for our series focusing on how data and analytics are key competitive differentiators for manufacturing companies. Here are just a few highlights: 1:44: Start where you are Skye: There’s a book that I reference when a company is assessing where they stand in their analytical strategy. It’s called ‘Competing on Analytics.’ It outlines 5 levels of maturity...2:22: Stage 1: Analytically Impaired Skye: This is when a company is flying blind, they’re very reactive, the systems might not be integrated, and their data is poor quality.3:13: Stage 2: Localized AnalyticsSkye: These companies collect transactional data, something like you would see in an ERP system. But it’s still very reactive.5:58: Stage 3: Analytical AspirationsSkye: These companies are making investments in the right talent and tools. They’re preparing to use analytics to improve a distinctive capability of their company. They have a roadmap to automation.6:57: The Road MapSkye: A really good place to start is in the area of your business that you believe is going to be your differentiator. So if you have a repair shop, maybe your differentiator is service. If you’re a logistics company, your differentiator is going to be speed of delivery...8:19: Stage 4: An Analytical CompanySkye: This is an enterprise-wide analytical strategy that’s viewed as a company priority… They're often using more automated analytics and more advanced modeling techniques… things like artificial intelligence, time series forecasting... 9:30: Building A Data-Driven Culture Skye: It really needs to start from the top down... The next step is to give the people who are doing the jobs day-to-day some ownership and input. These are the experts who can give you some of the most valuable insight as you’re figuring out your analytics strategy. 10:31: Stage 5: The Ultimate LevelSkye: This is [a company] using analytics as a key component in their competitive strategy…analytics are fully automated, completely integrated. Decisions organization-wide are data-driven. Analytics are the central theme to how the organization operates. 11:52: The Last DifferentiatorSkye: Analytics are really going to be the last differentiator. Analytics are going to be the big advantage that makes companies win over others. 16:05: Descriptive, Predictive, and Prescriptive AnalyticsAndrew: I like this concept because it helps you connect your analytical strategy toward tangible goals for your business and it really guides your thinking towards asking the right questions... References: Competing on Analytics by Thomas H. Davenport6 Steps Manufacturers Should Take to Optimize and Monetize Their DataIDC: Revenues for Big Data and Business AnalyticsTesla’s Over the Air FixConnecting Business Data to Business Goals