The conversation with my next guest was going so deep and so well…it became a two part episode! Today I’m chatting with Nadiem von Heydebrand, CEO of Mindfuel. Nadiem’s career journey led him from data science to data product management, and in this first, we will focus on the skills of data product management (DPM), including design. In part 2, we jump more into Nadiem’s take on the role of the DPM. Nadiem gives actionable insights into the realities of data product management, from the challenges of actually being able to talk to your end users, to focusing on the problems and unarticulated needs of your users rather than solutions. Nadiem and I also discuss how data product managers oversee a portfolio of initiatives, and why it’s important to view that portfolio as a series of investments. Nadiem also emphasizes the value of having designers on a data team, and why he hopes we see more designers in the industry.  Highlights/ Skip to: Brian introduces Nadiem and his background going from data science to data product management (00:36) Nadiem gives not only his definition of a data product, but also his related definitions of ‘data as product,’ ‘data as information,’ and ‘data as a model’ products (02:19) Nadiem outlines the skill set and activities he finds most valuable in a data product manager (05:15) How a data organization typically functions and the challenges a data team faces to prove their value (11:20) Brian and Nadiem discuss the challenges and realities of being able to do discovery with the end users of data products (17:42) Nadiem outlines how a portfolio of data initiatives has a certain investment attached to it and why it’s important to generate a good result from those investments (21:30) Why Nadiem wants to see more designers in the data product space and the problems designers solve for data teams (25:37) Nadiem shares a story about a time when he wished he had a designer to convert the expressed needs of the  business into the true need of the customer (30:10) The value of solving for the unarticulated needs of your product users, and Nadiem shares how focusing on problems rather than solutions helped him (32:32) Nadiem shares how you can connect with him and find out more about his company, Mindfuel (36:07) Quotes from Today’s Episode “The product mindset already says it quite well. When you look into classical product management, you have something called the viability, the desirability, the feasibility—so these are three very classic dimensions of product management—and the fourth dimension, we at Mindfuel define for ourselves and for applications are, is the datability.” — Nadiem von Heydebrand (06:51) “We can only prove our [data team’s] value if we unlock business opportunities in their [clients’] lines of businesses. So, our value contribution is indirect. And measuring indirect value contribution is very difficult in organizations.” — Nadiem von Heydebrand (11:57) “Whenever we think about data and analytics, we put a lot of investment and efforts in the delivery piece. I saw a study once where it said 3% of investments go into discovery and 90% of investments go into delivery and the rest is operations and a little bit overhead and all around. So, we have to balance and we have to do proper discovery to understand what problem do we want to solve.” — Nadiem von Heydebrand (13:59) “The best initiatives I delivered in my career, and also now within Mindfuel, are the ones where we try to build an end responsibility from the lines of businesses, among the product managers, to PO, the product owner, and then the delivery team.” – Nadiem von Heydebrand (17:00) “As a consultant, I typically think in solutions. And when we founded Mindfuel, my co-founder forced me to avoid talking about the solution for an entire ten months. So, in whatever meeting we were sitting, I was not allowed to talk about the solution, but only about the problem space.”  – Nadiem von Heydebrand (34:12) “In scaled organizations, data product managers, they typically run a portfolio of data products, and each single product can be seen a little bit like from an investment point of view, this is where we putting our money in, so that’s the reason why we also have to prioritize the right use cases or product initiatives because typically we have limited resources, either it is investment money, people, resources or our time.” – Nadiem von Heydebrand (24:02) “Unfortunately, we don’t see enough designers in data organizations yet. So, I would love to have more design people around me in the data organizations, not only from a delivery perspective, having people building amazing dashboards, but also, like, truly helping me in this kind of discovery space.” – Nadiem von Heydebrand (26:28) Links Mindfuel: https://mindfuel.ai/ Personal LinkedIn: https://www.linkedin.com/in/nadiemvh/ Mindfuel LinkedIn: https://www.linkedin.com/company/mindfuelai/

Today I’m chatting with Kyle Winterbottom, who is the owner of Orbition Group and an advisor/recruiter for companies who are hiring top talent in the data industry. Kyle and I discuss whether the concept of data products has meaningful value to companies, or if it’s in a hype cycle of sorts. Kyle then shares his views on what sets the idea of data products apart from other trends, the well-paid opportunities he sees opening up for product leaders in the data industry, and why he feels being able to increase user adoption and quantify the business impact of your work is also relevant in a candidate’s ability to negotiate higher pay. Kyle and I also discuss the strange tendency for companies to mistakenly prioritize technical skills for these roles, the overall job market for data product leaders, average compensation numbers, and what companies can do to attract this talent. Highlights/ Skip to: Kyle introduces himself and his company, Orbition Group (01:02) Why Brian invited Kyle on the show to discuss the recruitment of technical talent for data & analytics teams (02:00) Kyle shares what’s causing companies to build out data product teams (04:49) The reason why viewing data as a product seems to be driving better adoption in Kyle’s view (07:22) Does Kyle feel that the concept of data products is mostly hype or meaningful? (11:26) The different levels of maturity Kyle sees in organizations that are approaching him for help hiring data product talent, and how soft skills are often overlooked (15:37) Kyle’s views on who is successfully landing data product manager roles and how that’s starting to change (23:20) What Kyle’s observations are on the salary bands for data product manager roles and the type of money people can make in this space (25:41) Brian and Kyle discuss how the skills of DPMs can help these leaders improve earning potential (30:30) Kyle’s observations and advice to companies seeking to improve the data product talent they attract (38:12) How listeners can learn more about Kyle and Orbition Group (47:55) Quotes from Today’s Episode “I think data products, obviously, there’s starting to get a bit of hype around it, which I’ve got no doubt will start to lead organizations to look down that route, just because they see and hear about other organizations doing it. ... [but] what it’s helping organizations to do is to drive adoption.” — Kyle Winterbottom (05:45) “I think we’re at a point now where it’s becoming more and more clear, day by day, week by week, the there’s more to [the data industry] than just the building of stuff.” – Kyle Winterbottom (12:56) “The whole soft skills piece is becoming absolutely integral because it’s become—you know, it’s night and day now, between the people that are really investing in themselves in that area and how quickly they’re progressing in their career because of that. But yeah, most organizations don’t even think about that.” – Kyle Winterbottom (18:49) “I think nine times out of ten, most businesses overestimate the importance of the technical stuff practically in every role. … Even data analysts, data scientists, all they’re bothered about is the tech stack that they’ve used, [but] there’s a lot more to it than just the tech that they use.” – Kyle Winterbottom (22:56) “There’s probably a big opportunity for really good product people to move into the data space because it’s going to be well paid with lots of opportunity. [It’s] quite an interesting space.” – Kyle Winterbottom (24:05) “As soon as you get to a point where if you can help to drive adoption and then you can quantify the commercial benefit of that adoption to the organization, that probably puts you up near the top in terms of percentile of being important to a data organization.” – Kyle Winterbottom (32:21) “We’re forever talking in our industry about the importance of storytelling. Yeah, I’ve never seen a business once tell a good story about how good it is to work for them, specifically in regards to their data analytics team and telling a story about that.” – Kyle Winterbottom (39:37) Links Kyle’s LinkedIn: https://www.linkedin.com/in/kylewinterbottom/ Orbition Group: https://www.orbitiongroup.com

Today I’m chatting with Phil Harvey, co-author of Data: A Guide to Humans and a technology professional with 23 years of experience working with AI and startups. In his book, Phil describes his philosophy of how empathy leads to more successful outcomes in data product development and the journey he took to arrive at this perspective. But what does empathy mean, and how do you measure its success? Brian and Phil dig into those questions, and Phil explains why he feels cognitive empathy is a learnable skill that one can develop and apply. Phil describes some leading indicators that empathy is needed on a data team, as well as leading indicators that a more empathetic approach to product development is working. While I use the term “design” or “UX” to describe a lot of what Phil is talking about, Phil actually has some strong opinions about UX and shares those on this episode. Phil also reveals why he decided to write Data: A Guide to Humans and some of the experiences that helped shape the book’s philosophy.  Highlights/ Skip to: Phil introduces himself and explains how he landed on the name for his book (00:54)  How Phil met his co-author, Noelia Jimenez Martinez, and the reason they started writing Data: A Guide to Humans (02:31) Phil unpacks his understanding of how he defines empathy, why it leads to success on AI projects, and what success means to him (03:54) Phil walks through a couple scenarios where empathy for users and stakeholders was lacking and the impacts it had (07:53) The work Phil has done internally to get comfortable doing the non-technical work required to make ML/AI/data products successful  (13:45) Phil describes some indicators that data teams can look for to know their design strategy is working (17:10) How Phil sees the methodology in his book relating to the world of UX (user experience) design (21:49) Phil walks through what an abstract concept like “empathy” means to him in his work and how it can be learned and applied as a practical skill (29:00) Quotes from Today’s Episode “If you take success in itself, this is about achieving your intended outcomes. And if you do that with empathy, your outcomes will be aligned to the needs of the people the outcomes are for. Your outcomes will be accepted by stakeholders because they’ll understand them.” — Phil Harvey (05:05) “Where there’s people not discussing and not considering the needs and feelings of others, you start to get this breakdown, data quality issues, all that.” – Phil Harvey (11:10)   “I wanted to write code; I didn’t want to deal with people. And you feel when you can do technical things, whether it’s machine-learning or these things, you end up with the ‘I’ve got a hammer and now everything looks like a nail problem.’ But you also have the [attitude] that my programming will solve everything.” – Phil Harvey (14:48)   “This is what startup-land really taught me—you can’t do everything. It’s very easy to think that you can and then burn yourself out. You need a team of people.” – Phil Harvey (15:09)   “Let’s listen to the users. Let’s bring that perspective in as opposed to thinking about aligning the two perspectives. Because any product is a change. You don’t ride a horse then jump in a car and expect the car to work like the horse.” – Phil Harvey (22:41)   “Let’s say you’re a leader in this space. … Listen out carefully for who’s complaining about who’s not listening to them. That’s a first early signal that there’s work to be done from an empathy perspective.” – Phil Harvey (25:00)   “The perspective of the book that Noelia and I have written is that empathy—and cognitive empathy particularly—is also a learnable skill. There are concrete and real things you can practice and do to improve in those skills.” – Phil Harvey (29:09) Links Data: A Guide to Humans: https://www.amazon.com/Data-A-Guide-to-Humans/dp/1783528648 Twitter: https://twitter.com/codebeard LinkedIn: https://www.linkedin.com/in/philipdavidharvey/ Mastodon: https://mastodonapp.uk/@codebeard

Do you ever find it hard to get the requirements, problems, or needs out of your customers, stakeholders, or users when creating a data product? This week I’m coming to you solo to share reasons your stakeholders, users, or customers may not be making time for your discovery efforts. I’ve outlined 10 reasons, and delve into those in the first part of this episode.    In part two, I am going to share a big update about the Data Product Leadership Community (DPLC) I’m hoping to launch in June 2023. I have created a Google Doc outlining how v1 of the community will work as well as 6 specific benefits that I hope you’ll be able to achieve in the first year of participating. However, I need your feedback to know if this is shaping up into the community you want to join. As such, at the end of this episode, I’ll ask you to head over to the Google Doc and leave a comment. To get the document link, just add your email address to the DPLC announcement list at http://designingforanalytics.com/community and you’ll get a confirmation email back with the link.  Links Join the Data Product Leadership Community at designingforanalytics.com/thecommunity My definition of “data product” is outlined on Experiencing Data Episode 105  Product vs. Feature Teams by Marty Cagan Email Brian at brian@designingforanalytics.com.

Today I’m chatting with Osian Jones, Head of Product for the Data Platform at Stuart. Osian describes how impact and ROI can be difficult metrics to measure in a data platform, and how the team at Stuart has sought to answer this challenge. He also reveals how user experience is intrinsically linked to adoption and the technical problems that data platforms seek to solve. Throughout our conversation, Osian shares a holistic overview of what it was like to design a data platform from scratch, the lessons he’s learned along the way, and the advice he’d give to other data product managers taking on similar projects.  Highlights/ Skip to: Osian describes his role at Stuart (01:36) Brian and Osian explore the importance of creating an intentional user experience strategy (04:29) Osian explains how having a clear mission enables him to create parameters to measure product success (11:44) How Stuart developed the KPIs for their data platform (17:09) Osian gives his take on the pros and cons of how data departments are handled in regards to company oversight (21:23) Brian and Osian discuss how vital it is to listen to your end users rather than relying on analytics alone to measure adoption (26:50) Osian reveals how he and his team went about designing their platform (31:33) What Osian learned from building out the platform and what he would change if he had to tackle a data product like this all over again (36:34) Quotes from Today’s Episode “Analytics has been treated very much as a technical problem, and very much so on the data platform side, which is more on the infrastructure and the tooling to enable analytics to take place. And so, viewing that purely as a technical problem left us at odds in a way, compared to [teams that had] a product leader, where the user was the focus [and] the user experience was very much driving a lot of what was roadmap.” — Osian Jones (03:15) “Whenever we get this question of what’s the impact? What’s the value? How does it impact our company top line? How does it impact our company OKRs? This is when we start to panic sometimes, as data platform leaders because that’s an answer that’s really challenging for us, simply because we are mostly enablers for analytics teams who are themselves enablers. It’s almost like there’s two different degrees away from the direct impact that your team can have.” — Osian Jones (12:45) “We have to start with a very clear mission. And our mission is to empower everyone to make the best data-driven decisions as fast as possible. And so, hidden within there, that’s a function of reducing time to insight, it’s also about maximizing trust and obviously minimizing costs.” — Osian Jones (13:48) “We can track [metrics like reliability, incidents, time to resolution, etc.], but also there is a perception aspect to that as well. We can’t underestimate the importance of listening to our users and qualitative data.” — Osian Jones (30:16) “These were questions that I felt that I naturally had to ask myself as a product manager. … Understanding who our users are, what they are trying to do with data and what is the current state of our data platform—so those were the three main things that I really wanted to get to the heart of, and connecting those three things together.” – Osian Jones (35:29) “The advice that I would give to anyone who is taking on the role of a leader of a data platform or a similar role is, you can easily get overwhelmed by just so many different use cases. And so, I would really encourage [leaders] to avoid that.” – Osian Jones (37:57) “Really look at your data platform from an end-user perspective and almost think of it as if you were to put the data platform on a supermarket shelf, what would that look like? And so, for each of the different components, how would you market that in a single one-liner in terms of what can this do for me?” – Osian Jones (39:22) Links Stuart: https://stuart.com/ Article on IIA: https://iianalytics.com/community/blog/how-to-build-a-data-platform-as-a-product-a-retrospective Experiencing Data Episode 80 with Doug Hubbard: https://designingforanalytics.com/resources/episodes/080-how-to-measure-the-impact-of-data-productsand-anything-else-with-forecasting-and-measurement-expert-doug-hubbard/ LinkedIn: https://www.linkedin.com/in/osianllwydjones/ Medium: https://medium.com/@osianllwyd

Today I’m chatting with Josh Noble, Principal User Researcher at TruEra. TruEra is working to improve AI quality by developing products that help data scientists and machine learning engineers improve their AI/ML models by combatting things like bias and improving explainability. Throughout our conversation, Josh—who also used to work as a Design Lead at IDEO.org—explains the unique challenges and importance of doing design and user research, even for technical users such as data scientists. He also shares tangible insights on what informs his product design strategy, the importance of measuring product success accurately, and the importance of understanding the current state of a solution when trying to improve it. Highlights/ Skip to: Josh introduces himself and explains why it’s important to do design and user research work for technical tools used by data scientists (00:43) The work that TruEra does to mitigate bias in AI as well as their broader focus on AI quality management (05:10) Josh describes how user roles informed TruEra’s design their upcoming monitoring product, and the emphasis he places on iterating with users (10:24)  How Josh approaches striking a balance between displaying extraneous information in the tools he designs vs. removing explainability (14:28) Josh explains how TruEra measures product success now and how they envision that changing in the future (17:59) The difference Josh sees between explainability and interpretability (26:56) How Josh decided to go from being a designer to getting a data science degree (31:08) Josh gives his take on what skills are most valuable as a designer and how to develop them (36:12) Quotes from Today’s Episode “We want to make machine learning better by testing it, helping people analyze it, helping people monitor models. Bias and fairness is an important part of that, as is accuracy, as is explainability, and as is more broadly AI quality.” — Josh Noble (05:13) “These two groups, the data scientists and the machine-learning engineer, they think quite differently about the problems that they need to solve. And they have very different toolsets. … Looking at how we can think about making a product and building tools that make sense to both of those different groups is a really important part of user experience.” – Josh Noble (09:04) “I’m a big advocate for iterating with users. To the degree possible, get things in front of people so they can tell you whether it works for them or not, whether it fits their expectations or not.” – Josh Noble (12:15) “Our goal is to get people to think about AI quality differently, not to necessarily change. We don’t want to change their performance metrics. We don’t want to make them change how they calculate something or change a workflow that works for them. We just want to get them to a place where they can bring together our four pillars and build better models and build better AI.” – Josh Noble (17:38) “I’ve always wanted to know what was going on underneath the design. I think it’s an important part of designing anything to understand how the thing that you are making is actually built.” – Josh Noble (31:56) “There’s a empathy-building exercise that comes from using these tools and understanding where they come from. I do understand the argument that some designers make. If you want to find a better way to do something, spending a ton of time in the trenches of the current way that it’s done is not always the solution, right?” – Josh Noble (36:12) “There’s a real empathy that you build and understanding that you build from seeing how your designs are actually implemented that makes you a better teammate. It makes you a better collaborator and ultimately, I think, makes you a better designer because of that.” – Josh Noble (36:46) “I would say to the non-designers who work with designers, measuring designs is not invalidating the designer. It doesn’t invalidate the craft of design. It shouldn’t be something that designers are hesitant to do. I think it’s really important to understand in a qualitative way what your design is doing and understand in a quantitative way what your design is doing.” – Josh Noble (38:18) Links Truera: https://truera.com/ Medium: https://medium.com/@fctry2

Today I’m chatting with Cole Swain, VP of Product at Tomorrow.io. Tomorrow.io is an untraditional weather company that creates data products to deliver relevant business insights to their customers. Together, Cole and I explore the challenges and opportunities that come with building an untraditional data product. Cole describes some of the practical strategies he’s developed for collecting and implementing qualitative data from customers, as well as why he feels rapport-building with users is a critical skill for product managers. Cole also reveals how scientists are part of the fold when developing products at Tomorrow.io, and the impact that their product has on decision-making across multiple industries.  Highlights/ Skip to: Cole describes what Tomorrow.io does (00:56) The types of companies that purchase Tomorrow.io and how they’re using the products (03:45) Cole explains how Tomorrow.io developed practical strategies for helping customers get the insights they need from their products (06:10) The challenges Cole has encountered trying to design a good user experience for an untraditional data product (11:08) Cole describes a time when a Tomorrow.io product didn’t get adopted, and how he and the team pivoted successfully (13:01) The impacts and outcomes of decisions made by customers using products from Tomorrow.io (15:16) Cole describes the value of understanding your active users and what skills and attributes he feels make a great product manager (20:11) Cole explains the challenges of being horizontally positioned rather than operating within an [industry] vertical (23:53) The different functions that are involved in developing Tomorrow.io (28:08) What keeps Cole up at night as the VP of Product for Tomorrow.io (33:47) Cole explains what he would do differently if he could come into his role from the beginning all over again (36:14) Quotes from Today’s Episode “[Customers aren't] just going to listen to that objective summary and go do the action. It really has to be supplied with a tremendous amount of information around it in a concise way. ... The assumption upfront was just, if we give you a recommendation, you’ll be able to go ahead and go do that. But it’s just not the case.” – Cole Swain (13:40) “The first challenge is designing this product in a way that you can communicate that value really fast. Because everybody who signs up for new product, they’re very lazy at the beginning. You have to motivate them to be able to realize that, hey, this is something that you can actually harness to change the way that you operate around the weather.” – Cole Swain (11:46) “People kind of overestimate at times the validity of even just real-time data. So, how do you create an experience that’s intuitive enough to be decision support and create confidence that this tool is different for them, while still having the empathy with the user, that this is still just a forecast in itself; you have to make your own decisions around it.” – Cole Swain (12:43) “What we often find in weather is that the bigger decisions aren’t made in silos. People don’t feel confident to make it on their own and they require a team to be able to come in because they know the unpredictability of the scenarios and they feel that they need to be able to have partners or comrades in the situation that are in it together with them.” – Cole Swain (17:24) “To me, there’s two super key capabilities or strengths in being a successful product manager. It’s pattern recognition and it’s the ability to create fast rapport with a customer: in your first conversation with a customer, within five minutes of talking with them, connect with them.” – Cole Swain (22:06) “[It’s] not about ‘how can we deliver the best value singularly to a particular client,’ but ‘how can we recognize the patterns that rise the tide for all of our customers?’ And it might sound obvious that that’s something that you need to do, but it’s so easy to teeter into the direction of building something unique for a particular vertical.” – Cole Swain (25:41) “Our sales team is just always finding new use cases. And we have to continue to say no and we have to continue to be disciplined in this arena. But I’d be lying to tell you if that didn’t keep me up at night when I hear about this opportunity of this solution we could build, and I know it can be done in a matter of X amount of time. But the risk of doing that is just too high, sometimes.” – Cole Swain (35:42) Links Company website: https://Tomorrow.io Twitter: https://twitter.com/colemswain

Today I’m chatting with Samir Sharma, CEO of datazuum. Samir is passionate about developing data strategies that drive business outcomes, and shares valuable insights into how problem framing and research can be done effectively from both the data and business side. Samir also provides his definition of a data strategy, and why it can be complicated to uncover whose job it is to create one. Throughout the conversation, Samir and I uncover the value of including different perspectives when implementing a data strategy and discuss solutions to various communication barriers. Of course, dashboards and data products also popped up in this episode as well!    Highlights/ Skip to: How Samir defines a data strategy and whose job it is to create one (01:39) The challenges Samir sees when trying to uncover and understand a company’s existing data strategy (03:39) The problem with the problem statements that Samir commonly encounters (08:37) Samir unpacks the communication challenges that lead to negative business outcomes when developing data products (14:05) An example of how improving research and problem framing solved a problem for Samir’s first big client (24:33) How speaking in a language your users understand can open the door to more exciting and valuable projects (31:08) Quotes from Today’s Episode “I don’t think business teams really care how you do it. If you can get an outcome—even if it’s quick and dirty. We’re not supposed to be doing these things for months on end. We’re supposed to be iterating quickly to start to show that result and add value and then building on top of that to show more value, more results.” — Samir Sharma (07:29) “Language is so important for business teams and technical teams and data teams to actually be able to speak a common language which has common business constructs. Why are organizations trying to train 20,000 people on data literacy, when they’ve got a ten-person data team? Why not just teach the ten people in the data team business language?” — Samir Sharma (10:52)   “I will continuously talk about processes because there’s not enough done actually understanding processes and how data is an event that occurs when a process is kicked off. … If you don’t understand the process and how data is enabling that process, or how data is being generated and the trigger points, then you’re just building something without really understanding where I need to fit that product in or where I need to fit that workflow in.” – Samir Sharma (11:46)   “But I start with asking clear questions about if I built you this dashboard, what is the decision you’re going to make off the back of it? Nine times out of ten, that question isn’t asked, if I build you this widget on this dashboard, what decision or action are you going to make or take? And how is that going to be linked back to the map that strategic objective? And if you can ask that question, you can build with purpose.” – Samir Sharma (19:27)   “You show [users] a bit of value, you show them what they’ve been dying to have, you give them a little bit extra in that so they can really optimize their decisions, and suddenly, you’ve got both sides now speaking a language that is really based on business outcomes and results.” – Samir Sharma (32:38)    “If the people in that conversation are the developers on one side, the business team, and they’re starting to see a new narrative, even the developers will start to say, “Oh! Now, I know exactly why I’m doing this. Now, I know why I’m building it.” So, they’re also starting to learn about the business, about what impacts sales, and maybe how marketing then intertwines into that. It’s important that that is done, but not enough time has been taken on that approach.” – Samir Sharma (24:05) The thing for me is, business teams don’t know what they don’t know, right? Most of the time, they’re asking a question. If I was on the data team and I’d already built a dashboard that would [answer that question], then I haven’t built it properly in the first instance. What I’ve done is I’ve built it for the beauty and the visualization instead of the what I would class is the ugliness and impact that I need.” – Samir Sharma (17:05) Links datazuum: https://datazuum.com/ LinkedIn: https://www.linkedin.com/in/samirsharma1/

Today I’m chatting with Yuval Gonczarowski, Founder & CEO of the startup, Akooda. Yuval is a self-described “socially capable nerd” who has learned how to understand and meet the needs of his customers outside of a purely data-driven lens. Yuval describes how Akooda is able to solve a universal data challenge for leaders who don’t have complete visibility into how their teams are working, and also explains why it’s important that Akooda provide those data insights without bias. Yuval and I also explore why it’s so challenging to find great product leaders and his rule for getting useful feedback from customers and stakeholders.    Highlights/ Skip to: Yuval describes what Akooda does (00:35) The types of technical skills Yuval had to move away from to adopt better leadership capabilities within a startup (02:15) Yuval explains how Akooda solves what he sees as a universal data problem for anyone in management positions (04:15) How Akooda goes about designing for multiple user types (personas) (06:29) Yuval describes how using Akooda internally (dogfooding!) helps inform their design strategy for various use cases (09:09) The different strategies Akooda employs to ensure they receive honest and valuable feedback from their customers (11:08) Yuval explains the three sales cycles that Akooda goes through to ensure their product is properly adapted to both their buyers and the end users of their tool (15:37) How Yuval learned the importance of providing data-driven insights without a bias of whether the results are good or bad (18:22) Yuval describes his core leadership values and why he feels a product can never be simple enough (24:22) The biggest learnings Yuval had when building Akooda and what he’d do different if he had to start from scratch (28:18) Why Yuval feels being the first Head of Product that reports to a CEO is both a very difficult position to be in and a very hard hire to get right (29:16) Quotes from Today’s Episode “Re: moving from a technical to product role: My first inclination would be straight up talk about the how, but that’s not necessarily my job anymore. We want to talk about the why and how does the customer perceive things, how do they look at things, how would they experience this new feature? And in a sense, [that’s] my biggest change in the way I see the world.” — Yuval Gonczarowski (03:01) “We are a very data-driven organization. Part of it is our DNA, my own background. When you first start a company and you’re into your first handful of customers, a lot of decisions have to be made based on gut feelings, sort of hypotheses, scenarios… I’ve lived through this pain.” — Yuval Gonczarowski (09:43)   “I don’t believe I will get honest feedback from a customer if I don’t hurt their pocket. If you want honest feedback [from customers], you got to charge.” — Yuval Gonczarowski (11:38) “Engineering is the most expensive resource we have. Whenever we allocate engineering resources, they have to be something the customer is going to use.” – Yuval Gonczarowski (13:04)   When selling a data product: “If you don’t build the right collateral and the right approach and mindset to the fact that it’s not enough when the contract is signed, it’s actually these three sales cycles of making sure that customer adoption is done properly, then you haven’t finished selling. Contract is step one, installation is step two, usage is step three. Until step three is done, haven’t really sold the product.” — Yuval Gonczarowski (16:59)   “By definition, all products are too complex. And it’s always tempting to add another button, another feature, another toggle. Let’s see what we can remove to make it easier.” – Yuval Gonczarowski (26:35) Links Akooda: https://akooda.co/ Yuval’s Email: y@akooda.co Yuval’s LinkedIn: https://www.linkedin.com/in/goncho/

Today I’m chatting with Dr. Sebastian Klapdor, Chief Data Officer for Vista. Sebastian has developed and grown a successful Data Product Management team at Vista, and it all began with selling his vision to the rest of the executive leadership. In this episode, Sebastian explains what that process was like and what he learned. Sebastian shares valuable insights on how he implemented a data product orientation at Vista, what makes a good data product manager, and why technology usage isn’t the only metric that matters when measuring success. He also shares what he would do differently if he had to do it all over again.   Highlights/ Skip to: How Sebastian defines a data product (01:48) Brian asks Sebastian about the change management process in leadership when implementing a data product approach (07:40) The three dimensions that Sebastian and his team measure to determine adoption success (10:22) Sebastian shares the financial results of Vista adopting a data product approach (12:56) The size and scale of the data team at Vista, and how their different roles ensure success (14:30) Sebastian explains how Vista created and grew a team of 35 data product managers (16:47) The skills Sebastian feels data product managers need to be successful at Vista (22:02) Sebastian describes what he would do differently if he had to implement a data product approach at a company again (29:46) Quotes from Today’s Episode “You need to establish a culture, and that’s often the hardest part that takes the longest -  to treat data as an asset, and not to treat it as a byproduct, but to treat it as a product and treat it as a valuable thing.” – Sebastian Klapdor (07:56) “One source of data product managers is taking data professionals. So, you take data engineers, data scientists, or former analysts, and develop them into the role by coaching them [through] the product management skills from the software industry.” – Sebastian Klapdor (17:39)   “We went out there and we were hiring people in the market who were experienced [Product Managers]. But we also see internal people, actually grooming and growing into all of these roles, both from these 80 folks who have been around before, but also from other areas of Vista.” – Sebastian Klapdor (20:28)   “[Being a good Product Manager] comes back to the good old classics of collaborating, of being empathetic to where other people are at, their priorities, and understanding where [our] priorities fit into their bigger piece, and jointly aligning on what is valuable for Vista.” – Sebastian Klapdor (22:27)   “I think there’s nothing more detrimental than saying, ‘Yeah, sure, we can deliver things, and with data, it can do everything.’ And then you disappoint people and you don’t stick to your promises. … If you don’t stick to your promise, it will hurt you.” – Sebastian Klapdor (23:04) “You don’t do the typical waterfall approach of solving business problems with data. You don’t do the approach that a data scientist tries to get some data, builds a model, and hands it over to data engineer who should productionize that. And then the data engineer gets back and says certain features can’t be productionized because it’s very complex to get the data on a daily basis, or in real time. By doing [this work] in a data product team, you can work actually in Agile and you’re super fast building what we call a minimum lovable product.” – Sebastian Klapdor (26:15) “That was the biggest learning … whom do we staff as data product managers? And what do we expect of a good data product manager? How does a career path look like? That took us a really long time to figure out.” – Sebastian Klapdor (30:18) “We have a big, big, big commitment that we want to start stuffing UX designers onto our [data] product teams.” - Sebastian Klapdor (21:12) Links Vista: https://vista.io LinkedIn: https://www.linkedin.com/in/sebastianklapdor/ Vista Blog: https://vista.io/blog