100 episodes

How do you create innovative machine learning and analytics products? Brian T. O’Neill reveals the strategies and activities that CxOs and innovative leaders in technical product management, data science and analytics are using to deliver indispensable experiences around data. From traditional analytics to machine learning and AI, Brian and his guests explore how extraordinary value can be created when the outputs of data science and analytics are turned into engaging, valuable decision support applications and user experiences centered around the humans in the loop. Experiencing Data also features special guests on design, ethics, explainable AI (XAI), and innovation who relate their expertise to the world of data-driven software. If you're in charge of creating simple, valuable, human-centered data products that produce business value in the last mile, you'll enjoy #ExperiencingData.

Transcripts available at: https://designingforanalytics.com/ed

ABOUT THE HOST
Brian T. O’Neill is a consulting product designer who helps companies create innovative ML and analytics solutions. He is also the founder and principal of Designing for Analytics.…and a professional percussionist/drummer.

Experiencing Data with Brian T. O’Neill Brian T. O’Neill from Designing for Analytics

    • Technology
    • 5.0 • 1 Rating

How do you create innovative machine learning and analytics products? Brian T. O’Neill reveals the strategies and activities that CxOs and innovative leaders in technical product management, data science and analytics are using to deliver indispensable experiences around data. From traditional analytics to machine learning and AI, Brian and his guests explore how extraordinary value can be created when the outputs of data science and analytics are turned into engaging, valuable decision support applications and user experiences centered around the humans in the loop. Experiencing Data also features special guests on design, ethics, explainable AI (XAI), and innovation who relate their expertise to the world of data-driven software. If you're in charge of creating simple, valuable, human-centered data products that produce business value in the last mile, you'll enjoy #ExperiencingData.

Transcripts available at: https://designingforanalytics.com/ed

ABOUT THE HOST
Brian T. O’Neill is a consulting product designer who helps companies create innovative ML and analytics solutions. He is also the founder and principal of Designing for Analytics.…and a professional percussionist/drummer.

    109 - The Role of Product Management and Design in Turning ML/AI into a Valuable Business with Bob Mason from Argon Ventures

    109 - The Role of Product Management and Design in Turning ML/AI into a Valuable Business with Bob Mason from Argon Ventures

    Today I’m chatting with Bob Mason, Managing Partner at Argon Ventures. Bob is a VC who seeks out early-stage founders in the ML/AI space and helps them inform their go-to-market, product, and design strategies. In this episode, Bob reveals what he looks for in early-stage data and intelligence startups who are trying to leverage ML/AI. He goes on to explain why it’s important to identify what your strengths are and what you enjoy doing so you can surround yourself with the right team. Bob also shares valuable insight into how to earn trust with potential customers as an early-stage startup, how design impacts a product’s success, and his strategy for differentiating yourself and creating a valuable product outside of the ubiquitous “platform play.” 
     
    Highlights/ Skip to:
    Bob explains why and how Argon Ventures focuses their investments in intelligent industry companies (00:53)
    Brian and Bob discuss the importance of prioritizing go-to-market strategy over technology (03:42)
    How Bob views the career progression from data science to product management, and the ways in which his own career has paralleled that journey (07:21)
    The role customer adoption and user experience play for Bob and the companies he invests in, both pre-investment and post-investment (11:10)
    Brian and Bob discuss the design capabilities of different teams and why Bob feels it’s something leaders need to keep top of mind (15:25)
    Bob explains his recommendation to seek out quick wins for AI companies who can’t expect customers to wait for an ROI (19:09)
    The importance Bob sees in identifying early adopters during a sales cycle for early-stage startups (21:34)
    Bob describes how being customer-centric allows start-ups to build trust, garner quick wins, and inform their product strategy (23:42)
    Bob and Brian dive into Bob’s belief that solving intrinsic business problems by vertical increases a start-up’s chance of success substantially over “the platform play” (27:29)
    Bob gives insight into product trends he believes are going to be extremely impactful in the near future (29:05)
    Quotes from Today’s Episode
    “In a former life, I was a software engineer, founder, and CTO myself, so I have to watch myself to not just geek out on the technology itself because the most important element when you’re determining if you want to move forward with investment or not, is this: is there a real problem here to be solved or is this technology in search of a problem?” — Bob Mason (01:51)
    “User-centric research is really valuable, particularly at the earliest stages. If you’re just off by a degree or two, several years down the road, that can be a really material roadblock that you hit. And so, starting off on the right foot, I think is super, super valuable.” – Bob Mason (06:12)
     
    “I don’t think the technical folks in an early-stage startup absolve themselves of not being really intimately involved with their go-to-market and who they’re ultimately creating value for.” – Bob Mason (07:07)
     
    “When we’re making an investment decision, startups don’t generally have any customers, and so we don’t necessarily use the signal of long-term customer adoption as a driver for our initial investment decision. But it’s very much top of mind after investment and as we’re trying to build and bring the first version of the product to market. Being very thoughtful and mindful of sort of customer experience and long-term adoption is absolutely critical.” – Bob Mason (11:23)
     
    “If you’re a scientist, the way you’re presenting both raw data and sort of summaries of data could be quite different than if you’re working with a business analyst that’s a few years out of college with a liberal arts degree. How you interpret results and then present those results, I think, is actually a very interesting design problem.” – Bob Mason (18:40)
     
    “I think initially, a lot of early AI startups just kind of assumed that

    • 32 min
    108 - Google Cloud’s Bruno Aziza on What Makes a Good Customer-Obsessed Data Product Manager

    108 - Google Cloud’s Bruno Aziza on What Makes a Good Customer-Obsessed Data Product Manager

    Today I’m chatting with Bruno Aziza, Head of Data & Analytics at Google Cloud. Bruno leads a team of outbound product managers in charge of BigQuery, Dataproc, Dataflow and Looker and we dive deep on what Bruno looks for in terms of skills for these leaders. Bruno describes the three patterns of operational alignment he’s observed in data product management, as well as why he feels ownership and customer obsession are two of the most important qualities a good product manager can have. Bruno and I also dive into how to effectively abstract the core problem you’re solving, as well as how to determine whether a problem might be solved in a better way. 
     
    Highlights / Skip to:
    Bruno introduces himself and explains how he created his “CarCast” podcast (00:45)
    Bruno describes his role at Google, the product managers he leads, and the specific Google Cloud products in his portfolio (02:36)
    What Bruno feels are the most important attributes to look for in a good data product manager (03:59)
    Bruno details how a good product manager focuses on not only the core problem, but how the problem is currently solved and whether or not that’s acceptable (07:20)
    What effective abstracting the problem looks like in Bruno’s view and why he positions product management as a way to help users move forward in their career (12:38)
    Why Bruno sees extracting value from data as the number one pain point for data teams and their respective companies (17:55)
    Bruno gives his definition of a data product (21:42)
    The three patterns Bruno has observed of operational alignment when it comes to data product management (27:57)
    Bruno explains the best practices he’s seen for cross-team goal setting and problem-framing (35:30)
     
    Quotes from Today’s Episode
     
    “What’s happening in the industry is really interesting. For people that are running data teams today and listening to us, the makeup of their teams is starting to look more like what we do [in] product management.” — Bruno Aziza (04:29)
    “The problem is the problem, so focus on the problem, decompose the problem, look at the frictions that are acceptable, look at the frictions that are not acceptable, and look at how by assembling a solution, you can make it most seamless for the individual to go out and get the job done.” – Bruno Aziza (11:28)
     
    “As a product manager, yes, we’re in the business of software, but in fact, I think you’re in the career management business. Your job is to make sure that whatever your customer’s job is that you’re making it so much easier that they, in fact, get so much more done, and by doing so they will get promoted, get the next job.” – Bruno Aziza (15:41)
     
    “I think that is the task of any technology company, of any product manager that’s helping these technology companies: don’t be building a product that’s looking for a problem. Just start with the problem back and solution from that. Just make sure you understand the problem very well.” (19:52)
     
    “If you’re a data product manager today, you look at your data estate and you ask yourself, ‘What am I building to save money? When am I building to make money?’ If you can do both, that’s absolutely awesome. And so, the data product is an asset that has been built repeatedly by a team and generates value out of data.” – Bruno Aziza (23:12)
     
    “[Machine learning is] hard because multiple teams have to work together, right? You got your business analyst over here, you’ve got your data scientists over there, they’re not even the same team. And so, sometimes you’re struggling with just the human aspect of it.” (30:30)
     
    “As a data leader, an IT leader, you got to think about those soft ways to accomplish the stuff that’s binary, that’s the hard [stuff], right? I always joke, the hard stuff is the soft stuff for people like us because we think about data, we think about logic, we think, ‘Okay if it makes sense, it will be implemented.’

    • 50 min
    107 - Tom Davenport on Data Product Management and the Impact of a Product Orientation on Enterprise Data Science and ML Initiatives

    107 - Tom Davenport on Data Product Management and the Impact of a Product Orientation on Enterprise Data Science and ML Initiatives

    Today I’m chatting with returning guest Tom Davenport, who is a Distinguished Professor at Babson College, a Visiting Professor at Oxford, a Research Fellow at MIT, and a Senior Advisor to Deloitte’s AI practice. He is also the author of three new books (!) on AI and in this episode, we’re discussing the role of product orientation in enterprise data science teams, the skills required, what he’s seeing in the wild in terms of teams adopting this approach, and the value it can create. Back in episode 26, Tom was a guest on my show and he gave the data science/analytics industry an approximate “2 out of 10” rating in terms of its ability to generate value with data. So, naturally, I asked him for an update on that rating, and he kindly obliged. How are you all doing? Listen in to find out!
    Highlights / Skip to:
    Tom provides an updated rating (between 1-10) as to how well he thinks data science and analytics teams are doing these days at creating economic value (00:44)
    Why Tom believes that “motivation is not enough for data science work” (03:06)
    Tom provides his definition of what data products are and some opinions on other industry definitions (04:22)
    How Tom views the rise of taking a product approach to data roles and why data products must be tied to value (07:55)
    Tom explains why he feels top down executive support is needed to drive a product orientation (11:51)
    Brian and Tom discuss how they feel companies should prioritize true data products versus more informal AI efforts (16:26)
    The trends Tom sees in the companies and teams that are implementing a data product orientation (19:18)
    Brian and Tom discuss the models they typically see for data teams and their key components (23:18)
    Tom explains the value and necessity of data product management (34:49)
    Tom describes his three new books (39:00)
    Quotes from Today’s Episode
    “Data science in general, I think has been focused heavily on motivation to fit lines and curves to data points, and that particular motivation certainly isn’t enough in that even if you create a good model that fits the data, it doesn’t mean at all that is going to produce any economic value.” – Tom Davenport  (03:05)
    “If data scientists don’t worry about deployment, then they’re not going to be in their jobs for terribly long because they’re not providing any value to their organizations.” – Tom Davenport (13:25)
    “Product also means you got to market this thing if it’s going to be successful. You just can’t assume because it’s a brilliant algorithm with capturing a lot of area under the curve that it’s somehow going to be great for your company.” – Tom Davenport (19:04)
     
    “[PM is] a hard thing, even for people in non-technical roles, because product management has always been a sort of ‘minister without portfolio’ sort of job, and you know, influence without formal authority, where you are responsible for a lot of things happening, but the people don’t report to you, generally.” – Tom Davenport (22:03)
     
    “This collaboration between a human being making a decision and an AI system that might in some cases come up with a different decision but can’t explain itself, that’s a really tough thing to do [well].” – Tom Davenport (28:04)
    “This idea that we’re going to use externally-sourced systems for ML is not likely to succeed in many cases because, you know, those vendors didn’t work closely with everybody in your organization” – Tom Davenport (30:21)
     
    “I think it’s unlikely that [organizational gaps] are going to be successfully addressed by merging everybody together in one organization. I think that’s what product managers do is they try to address those gaps in the organization and develop a process that makes coordination at least possible, if not true, all the time.” – Tom Davenport (36:49)
    Links
    Tom’s LinkedIn: https://www.linkedin.com/in/davenporttom/
    Tom’s Twitter: https://twitter.com/tdav
    Al

    • 42 min
    106 - Ideaflow: Applying the Practice of Design and Innovation to Internal Data Products w/ Jeremy Utley

    106 - Ideaflow: Applying the Practice of Design and Innovation to Internal Data Products w/ Jeremy Utley

    Today I’m chatting with former-analyst-turned-design-educator Jeremy Utley of the Stanford d.school and co-author of Ideaflow. Jeremy reveals the psychology behind great innovation, and the importance of creating psychological safety for a team to generate what they may view as bad ideas. Jeremy speaks to the critical collision of unrelated frames of reference when problem-solving, as well as why creativity is actually more of a numbers game than awaiting that singular stroke of genius. Listen as Jeremy gives real-world examples of how to practice and measure (!) your innovation efforts and apply them to data products.
     
    Highlights/ Skip to:
     
    Jeremy explains the methodology of thinking he’s adopted after moving from highly analytical roles to the role he’s in now (01:38)
    The approach Jeremy takes to the existential challenge of balancing innovation with efficiency (03:54)
    Brian shares a story of a creative breakthrough he had recently and Jeremy uses that to highlight how innovation often comes in a way contrary to normalcy and professionalism (09:37)
    Why Jeremy feels innovation and creativity demand multiple attempts at finding solutions (16:13)
    How to take a innovation-forward approach like the ones Jeremy has described when working on internal tool development (19:33)
    Jeremy’s advice for accelerating working through bad ideas to get to the good ideas (25:18)
    The approach Jeremy takes to generate a large volume of ideas, rather than focusing only on “good” ideas, including a real-life example (31:54)
    Jeremy’s beliefs on the importance of creating psychological safety to promote innovation and creative problem-solving (35:11)
    Quotes from Today’s Episode
    “I’m in spreadsheets every day to this day, but I recognize that there’s a time and place when that’s the tool that’s needed, and then specifically, there’s a time and a place where that’s not going to help me and the answer is not going to be found in the spreadsheet.” – Jeremy Utley (03:13)
    “There’s the question of, ‘Are we doing it right?’ And then there’s a different question, which is, ‘Are we doing the right “it”?’ And I think a lot of us tend to fixate on, ‘Are we doing it right?’ And we have an ability to perfectly optimize that what should not be done.” – Jeremy Utley (05:05)
    “I think a vendetta that I have is against this wrong placement of—this exaltation of efficiency is the end-all, be-all. Innovation is not efficient. And the question is not how can I be efficient. It’s what is effective. And effectiveness, oftentimes when it comes to innovation and breaking through, doesn’t feel efficient.” – Jeremy Utley (09:17)
    “The way the brain works, we actually understand it. The way breakthroughs work we actually understand them. The difficulty is it challenges our definitions of efficiency and professionalism and all of these things.” – Jeremy Utley (15:13)
     
    “What’s the a priori probability that any solution is the right solution? Or any idea is a good idea? It’s exceptionally low. You have to be exceptionally arrogant to think that most of your ideas are good. They’re not. That’s fine, we don’t mind because then what’s efficient is actually to generate a lot.” – Jeremy Utley (26:20)

    “If you don’t learn that nothing happens when the ball hits the floor, you can never learn how to juggle. And to me, it’s a really good metaphor. The teams that don’t learn nothing happens when they have a bad idea. Literally, the world does not end. They don’t get fired. They don’t get ridiculed. Now, if they do get fired or ridiculed, that’s a leadership problem.” – Jeremy Utley (35:59)
     
    [The following] is an essential question for a team leader to ask. Do people on my team have the freedom, at least with me, to share what they truly fear could be an incredibly stupid idea?” – Jeremy Utley (41:52)
     
    Links
    Ideaflow: https://www.amazon.com/Ideaflow-Only-Business-M

    • 44 min
    105 - Defining “Data Product” the Producty Way and the Non-technical Skills ML/AI Product Managers Need

    105 - Defining “Data Product” the Producty Way and the Non-technical Skills ML/AI Product Managers Need

    Today I’m discussing something we’ve been talking about a lot on the podcast recently - the definition of a “data product.” While my definition is still a work in progress, I think it’s worth putting out into the world at this point to get more feedback. In addition to sharing my definition of data products (as defined the “producty way”), on today’s episode definition, I also discuss some of the non-technical skills that data product managers (DPMs) in the ML and AI space need if they want to achieve good user adoption of their solutions. I’ll also share my thoughts on whether data scientists can make good data product managers, what a DPM can do to better understand your users and stakeholders, and how product and UX design factors into this role. 
    Highlights/ Skip to:
    I introduce my reasons for sharing my definition of a data product (0:46)
    My definition of data product (7:26)
    Thinking the “producty” way (8:14)
    My thoughts on necessary skills for data PMs (in particular, AI & machine learning product management) (12:21)
    How data scientists can become good data product managers (DPMs) by taking off the data science hat (13:42)
    Understanding the role of UX design within the context of DPM (16:37)
    Crafting your sales and marketing strategies to emphasize the value of your product to the people who can use or purchase it (23:07)
    How to build a team that will help you increase adoption of your data product (30:01)
    How to build relationships with stakeholders/customers that allow you to find the right solutions for them (33:47)
    Letting go of a technical identity to develop a new identity as a DPM who can lead a team to build a product that actually gets used (36:32)
     
    Quotes from Today’s Episode
    “This is what’s missing in some of the other definitions that I see around data products  [...] they’re not talking about it from the customer of the data product lens. And that orientation sums up all of the work that I’m doing and trying to get you to do as well, which is to put the people at the center of the work that you’re doing and not the data science, engineering, tech, or design. I want you to put the people at the center.” (6:12)
    “A data product is a data-driven, end-to-end, human-in-the-loop decision support solution that’s so valuable, users would potentially pay to use it.” (7:26)
    “I want to plunge all the way in and say, ‘if you want to do this kind of work, then you need to be thinking the product-y way.’ And this means inherently letting go of some of the data science-y way of thinking and the data-first kinds of ways of thinking.” (11:46)
    “I’ve read in a few places that data scientists don’t make for good data product managers. [While it may be true that they’re more introverted,] I don’t think that necessarily means that there’s an inherent problem with data scientists becoming good data product managers. I think the main challenge will be—and this is the same thing for almost any career transitioning into product management—is knowing when to let go of your former identity and wear the right hat at the right time.” (14:24)
    “Make better things for people that will improve their life and their outcomes and the business value will follow if you’ve properly aligned those two things together.” (17:21)
    “The big message here is this: there is always a design and experience, whether it is an API, or a platform, a dashboard, a full application, etc. Since there are no null design choices, how much are you going to intentionally shape that UX, or just pray that it comes out good on the other end? Prayer is not really a reliable strategy.  If you want to routinely do this work right, you need to put intention behind it.” (22:33) 
    “Relationship building is a must, and this is where applying user experience research can be very useful—not just for users, but also with stakeholders. It’s learning how to ask really good questions and learning

    • 41 min
    104 - Surfacing the Unarticulated Needs of Users and Stakeholders through Effective Listening

    104 - Surfacing the Unarticulated Needs of Users and Stakeholders through Effective Listening

    Today I’m chatting with Indi Young, independent qualitative data scientist and author of Time to Listen. Indi explains how it is possible to gather and analyze qualitative data in a way that is meaningful to the desired future state of your users, and that learning how to listen and not just interview users is much like learning to ride a bicycle. Listen (!) to find out why pushing back is a necessary part of the design research process, how to build an internal sensor that allows you to truly uncover the nuggets of information that are critical to your projects, and the importance of understanding thought processes to prevent harmful outcomes.
     
    Highlights/ Skip to:
    Indi introduces her perspective on analyzing qualitative data sets (00:51)
    Indi’s motivation for working in design research and the importance of being able to capture and understand patterns to prevent harmful outcomes (05:09)
    The process Indi goes through for problem framing and understanding a user’s desired future state (11:11)
    Indi explains how to listen effectively in order to understand the thinking style of potential end users (15:42)
    Why Indi feels pushing back on problems within projects is a vital part of taking responsibility and her recommendations for doing so effectively (21:45)
    The importance Indi sees in building up a sensor in order to be able to detect nuggets clients give you for their upcoming projects (28:25)
    The difference in techniques Indi observes between an interview, a listening session, and a survey (33:13)
    Indi describes her published books and reveals which one she’d recommend listeners start with (37:34)
    Quotes from Today’s Episode
    “A lot of qualitative data is not trusted, mainly because the people who are doing the not trusting have encountered bad qualitative data.” — Indi Young (03:23)
    “When you’re learning to ride a bike, when you’re learning to decide what knowledge is needed, you’re probably going to burn through a bunch of money-making knowledge that never gets used. So, that’s when you start to learn, ‘I need to frame this better, and to frame it, I can’t do it by myself.’” – Indi Young (11:57)
    “What you want to do is get beyond the exterior and get to the interior, which is where somebody tells you what actually went through their mind when they did that thing in the past, not what’s going through their mind right now. And it’s that’s a very important distinction.” – Indi Young (20:28)
    “Re: dealing with stakeholders: You’re not doing your job if you don’t push back. You built up a lot of experience, you got hired, they hired you and your thinking and your experience, and if what went through your mind is, like, ‘This is wrong,’ but you don’t act on it, then they should not pay you a salary.” – Indi Young (22:45)
    “I’ve seen a lot of people leave their perfectly promising career because it was too hard to get to the point of accepting that you have to network, that I’m not going to be that one-in-a-million person who’s the brilliant person with a brilliant idea and get my just rewards that way.” – Indi Young (25:13)
    “What’s really interesting about a listening session is that it doesn’t—aside from building this sensor and learning what the techniques are for helping a person get to their interior cognition rather than that exterior … to get past that into the inner thinking, the emotional reactions, and the guiding principles, aside from the sensor and those techniques, there’s not much to it.” – Indi Young (32:45)
    “And once you start building that [sensor], and this idea of just having one generative question about the purpose—because the whole thing is framed by the purpose—there you go. Get started. You have to practice. So, it’s like riding a bike. Go for it. You won’t have those sensors at first, but you’ll start to learn how to build them.” – Indi Young (36:41)
    Links Referenced:
    Time to Listen: https://www.a

    • 44 min

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Claribelleee ,

Valuable info and tips!

Brilliant show! I highly recommend!

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