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 w/ Brian T. O’Neill - Data Products, Product Management, & UX Design Brian T. O’Neill from Designing for Analytics

    • Technology
    • 5.0 • 36 Ratings

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.

    140 - Why Data Visualization Alone Doesn’t Fix UI/UX Design Problems in Analytical Data Products with T from Data Rocks NZ

    140 - Why Data Visualization Alone Doesn’t Fix UI/UX Design Problems in Analytical Data Products with T from Data Rocks NZ

    This week on Experiencing Data, I chat with a new kindred spirit! Recently, I connected with Thabata Romanowski—better known as "T from Data Rocks NZ"—to discuss her experience applying UX design principles to modern analytical data products and dashboards. T walks us through her experience working as a data analyst in the mining sector, sharing the journey of how these experiences laid the foundation for her transition to data visualization. Now, she specializes in transforming complex, industry-specific data sets into intuitive, user-friendly visual representations, and addresses the challenges faced by the analytics teams she supports through her design business. T and I tackle common misconceptions about design in the analytics field, discuss how we communicate and educate non-designers on applying UX design principles to their dashboard and application design work, and address the problem with "pretty charts." We also explore some of the core ideas in T's Design Manifesto, including principles like being purposeful, context-sensitive, collaborative, and humanistic—all aimed at increasing user adoption and business value by improving UX.
     
    Highlights/ Skip to:
    I welcome T from Data Rocks NZ onto the show (00:00)
    T's transition from mining to leading an information design and data visualization consultancy. (01:43)
    T discusses the critical role of clear communication in data design solutions. (03:39)
    We address the misconceptions around the role of design in data analytics. (06:54) 
    T explains the importance of journey mapping in understanding users' needs. (15:25)
    We discuss the challenges of accurately capturing end-user needs. (19:00) 
    T and I discuss the importance of talking directly to end-users when developing data products. (25:56) 
    T shares her 'I like, I wish, I wonder' method for eliciting genuine user feedback. (33:03)
    T discusses her Data Design Manifesto for creating purposeful, context-aware, collaborative, and human-centered design principles in data. (36:37)
    We wrap up the conversation and share ways to connect with T. (40:49)
    Quotes from Today’s Episode
    "It's not so much that people…don't know what design is, it's more that they understand it differently from what it can actually do..." - T from Data Rocks NZ (06:59)
    "I think [misconception about design in technology] is rooted mainly in the fact that data has been very tied to IT teams, to technology teams, and they’re not always up to what design actually does.” - T from Data Rocks NZ (07:42) 
    “If you strip design of function, it becomes art. So, it’s not art… it’s about being functional and being useful in helping people.” - T from Data Rocks NZ (09:06)
    "It’s not that people don’t know, really, that the word design exists, or that design applies to analytics and whatnot; it’s more that they have this misunderstanding that it’s about making things look a certain way, when in fact... It’s about function. It’s about helping people do stuff better." - T from Data Rocks NZ (09:19)
    “Journey Mapping means that you have to talk to people...  Data is an inherently human thing. It is something that we create ourselves. So, it’s biased from the start. You can’t fully remove the human from the data" - T from Data Rocks NZ (15:36)
     “The biggest part of your data product success…happens outside of your technology and outside of your actual analysis. It’s defining who your audience is, what the context of this audience is, and to which purpose do they need that product. - T from Data Rocks NZ (19:08)
    “[In UX research], a tight, empowered product team needs regular exposure to end customers; there’s nothing that can replace that." - Brian O'Neill (25:58)
    “You have two sides [end-users and data team]  that are frustrated with the same thing. The side who asked wasn’t really sure what to ask. And then the data team gets frustrated because the users don’t know what they want…Nobody really understood wh

    • 42 min
    138 - VC Spotlight: The Impact of AI on SAAS and Data/Developer Products in 2024 w/ Ellen Chisa of BoldStart Ventures

    138 - VC Spotlight: The Impact of AI on SAAS and Data/Developer Products in 2024 w/ Ellen Chisa of BoldStart Ventures

    In this episode of Experiencing Data, I speak with Ellen Chisa, Partner at BoldStart Ventures, about what she’s seeing in the venture capital space around AI-driven products and companies—particularly with all the new GenAI capabilities that have emerged in the last year. Ellen and I first met when we were both engaged in travel tech startups in Boston over a decade ago, so it was great to get her current perspective being on the “other side” of products and companies working as a VC.  Ellen draws on her experience in product management and design to discuss how AI could democratize software creation and streamline backend coding, design integration, and analytics. We also delve into her work at Dark and the future prospects for developer tools and SaaS platforms. Given Ellen’s background in product management, human-centered design, and now VC, I thought she would have a lot to share—and she did!
    Highlights/ Skip to:
    I introduce the show and my guest, Ellen Chisa (00:00)
    Ellen discusses her transition from product and design to venture capital with BoldStart Ventures. (01:15)
    Ellen notes a shift from initial AI prototypes to more refined products, focusing on building and testing with minimal data. (03:22)
    Ellen mentions BoldStart Ventures' focus on early-stage companies providing developer and data tooling for businesses.  (07:00)
    Ellen discusses what she learned from her time at Dark and Lola about narrowing target user groups for technology products (11:54)
    Ellen's Insights into the importance of user experience is in product design and the process venture capitalists endure to make sure it meets user needs (15:50)
    Ellen gives us her take on the impact of AI on creating new opportunities for data tools and engineering solutions, (20:00)
    Ellen and I explore the future of user interfaces, and how AI tools could enhance UI/UX for end users. (25:28)
    Closing remarks and the best way to find Ellen on online (32:07)
    Quotes from Today’s Episode
    “It's a really interesting time in the venture market because on top of the Gen AI wave, we obviously had the macroeconomic shift. And so we've seen a lot of people are saying the companies that come out now are going to be great companies because they're a little bit more capital-constrained from the beginning, typically, and they'll grow more thoughtfully and really be thinking about how do they build an efficient business.”- Ellen Chisa (03: 22) 
    “We have this big technological shift around AI-enabled companies, and I think one of the things I’ve seen is, if you think back to a year ago, we saw a lot of early prototyping, and so there were like a couple of use cases that came up again and again.”-Ellen Chisa (3:42)
    “I don't think I've heard many pitches from founders who consider themselves data scientists first. We definitely get some from ML engineers and people who think about data architecture, for sure..”- Ellen Chisa (05:06)  
    “I still prefer GUI interfaces to voice or text usually, but I think that might be an uncanny valley sort of thing where if you think of people who didn’t have technology growing up, they’re more comfortable with the more human interaction, and then you get, like, a chunk of people who are digital natives who prefer it.”- Ellen Chisa (24:51)
    [Citing some excellent Boston-area restaurants!] “The Arc browser just shipped a bunch of new functionality, where instead of opening a bunch of tabs, you can say, “Open the recipe pages for Oleana and Sarma,” and it just opens both of them, and so it’s like multiple search queries at once.” - Ellen Chisa (27:22)
    “The AI wave of  technology biases towards people who already have products [in the market] and have existing datasets, and so I think everyone [at tech companies] is getting this big, top-down mandate from their executive team, like, ‘Oh, hey, you have to do something with AI now.’”- Ellen Chisa (28:37)
    “I think it’s hard to really grasp what an

    • 33 min
    137 - Immature Data, Immature Clients: When Are Data Products the Right Approach? feat. Data Product Architect, Karen Meppen

    137 - Immature Data, Immature Clients: When Are Data Products the Right Approach? feat. Data Product Architect, Karen Meppen

    This week, I'm chatting with Karen Meppen, a founding member of the Data Product Leadership Community and a Data Product Architect and Client Services Director at Hakkoda. Today, we're tackling the difficult topic of developing data products in situations where a product-oriented culture and data infrastructures may still be emerging or “at odds” with a human-centered approach. Karen brings extensive experience and a strong belief in how to effectively negotiate the early stages of data maturity. Together we look at the major hurdles that businesses encounter when trying to properly exploit data products, as well as the necessity of leadership support and strategy alignment in these initiatives. Karen's insights offer a roadmap for those seeking to adopt a product and UX-driven methodology when significant tech or cultural hurdles may exist.
    Highlights/ Skip to:
    I Introduce Karen Meppen and the challenges of dealing with data products in places where the data and tech aren't quite there yet (00:00)
    Karen shares her thoughts on what it's like working with "immature data" (02:27)
    Karen breaks down what a data product actually is (04:20)
    Karen and I discuss why having executive buy-in is crucial for moving forward with data products (07:48)
    The sometimes fuzzy definition of "data products." (12:09)
    Karen defines “shadow data teams” and explains how they sometimes conflict with tech teams (17:35)
    How Karen identifies the nature of each team to overcome common hurdles of connecting tech teams with business units (18:47)
    How she navigates conversations with tech leaders who think they already understand the requirements of business users (22:48)
    Using design prototypes and design reviews with different teams to make sure everyone is on the same page about UX (24:00)
    Karen shares stories from earlier in her career that led her to embrace human-centered design to ensure data products actually meet user needs (28:29)
    We reflect on our chat about UX, data products, and the “producty” approach to ML and analytics solutions (42:11) 
    Quotes from Today’s Episode
    "It’s not really fair to get really excited about what we hear about or see on LinkedIn, at conferences, etc. We get excited about the shiny things, and then want to go straight to it when [our] organization [may not be ] ready to do that, for a lot of reasons." - Karen Meppen (03:00)
    "If you do not have support from leadership and this is not something [they are]  passionate about, you probably aren’t a great candidate for pursuing data products as a way of working." - Karen Meppen (08:30)
    "Requirements are just friendly lies." - Karen, quoting Brian about how data teams need to interpret stakeholder requests  (13:27)
    "The greatest challenge that we have in technology is not technology, it’s the people, and understanding how we’re using the technology to meet our needs." - Karen Meppen (24:04)
    "You can’t automate something that you haven’t defined. For example, if you don’t have clarity on your tagging approach for your PII, or just the nature of all the metadata that you’re capturing for your data assets and what it means or how it’s handled—to make it good, then how could you possibly automate any of this that hasn’t been defined?" - Karen Meppen (38:35)
    "Nothing upsets an end-user more than lifting-and-shifting an existing report with the same problems it had in a new solution that now they’ve never used before." - Karen Meppen (40:13)
    “Early maturity may look different in many ways depending upon the nature of  business you’re doing, the structure of your data team, and how it interacts with folks.” (42:46) 
    Links 
    Data Product Leadership Community https://designingforanalytics.com/community/
    Karen Meppen on LinkedIn: ​​https://www.linkedin.com/in/karen--m/
    Hakkōda, Karen's company, for more insights on data products and services:https://hakkoda.io/ 

    • 44 min
    136 - Navigating the Politics of UX Research and Data Product Design with Caroline Zimmerman

    136 - Navigating the Politics of UX Research and Data Product Design with Caroline Zimmerman

    This week I’m chatting with Caroline Zimmerman, Director of Data Products and Strategy at Profusion. Caroline shares her journey through the school of hard knocks that led to her discovery that incorporating more extensive UX research into the data product design process improves outcomes. We explore the complicated nature of discovering and building a better design process, how to engage end users so they actually make time for research, and why understanding how to navigate interdepartmental politics is necessary in the world of data and product design. Caroline reveals the pivotal moment that changed her approach to data product design, as well as her learnings from evolving data products with the users as their needs and business strategies change. Lastly, Caroline and I explore what the future of data product leadership looks like and Caroline shares why there's never been a better time to work in data.
    Highlights/ Skip to:
    Intros and Caroline describes how she learned crucial lessons on building data products the hard way (00:36)
    The fundamental moment that helped Caroline to realize that she needed to find a different way to uncover user needs (03:51)
    How working with great UX researchers influenced Caroline’s approach to building data products (08:31)
    Why Caroline feels that exploring the ‘why’ is foundational to designing a data product that gets adopted (10:25)
    Caroline’s experience building a data model for a client and what she learned from that experience when the client’s business model changed (14:34)
    How Caroline addresses the challenge of end users not making time for user research (18:00)
    A high-level overview of the UX research process when Caroline’s team starts working with a new client (22:28)
    The biggest challenges that Caroline faces as a Director of Data Products, and why data products require the ability to navigate company politics and interests (29:58)
    Caroline describes the nuances of working with different stakeholder personas (35:15)
    Why data teams need to embrace a more human-led approach to designing data products and focus less on metrics and the technical aspects (38:10)
    Caroline’s closing thoughts on what she’d like to share with other data leaders and how you can connect with her (40:48)
    Quotes from Today’s Episode
    “When I was first starting out, I thought that you could essentially take notes on what someone was asking for, go off and build it to their exact specs, and be successful. And it turns out that you can build something to exact specs and suffer from poor adoption and just not be solving problems because I did it as a wish fulfillment, laundry-list exercise rather than really thinking through user needs.” — Caroline Zimmerman (01:11)
    “People want a thing. They’re paying for a thing, right? And so, just really having that reflex to try to gently come back to that why and spending sufficient time exploring it before going into solution build, even when people are under a lot of deadline pressure and are paying you to deliver a thing [is the most important element of designing a data product].” – Caroline Zimmerman (11:53)
    “A data product evolves because user needs change, business models change, and business priorities change, and we need to evolve with it. It’s not like you got it right once, and then you’re good for life. At all.” – Caroline Zimmerman (17:48)
    “I continue to have lots to learn about stakeholder management and understanding the interplay between what the organization needs to be successful, but also, organizations are made up of people with personal interests, and you need to understand both.” – Caroline Zimmerman (30:18)
    “Data products are built in a political context. And just being aware of that context is important.” – Caroline Zimmerman (32:33)
    “I think that data, maybe more than any other function, is transversal. I think data brings up politics because, especially with larger organizations, there

    • 44 min
    135 - “No Time for That:” Enabling Effective Data Product UX Research in Product-Immature Organizations

    135 - “No Time for That:” Enabling Effective Data Product UX Research in Product-Immature Organizations

    This week, I’m chatting with Steve Portigal, who is the Principal of Portigal Consulting and the Author of Interviewing Users. We discuss the changes that prompted him to release a second version of his book 10 years after its initial release, and dive into the best practices that any team can implement to start unlocking the value of data product UX research. Steve explains that the key to making time for user research is knowing what business value you’re after, not simply having a list of research questions. We then role-play through some in-depth examples of real-life experiences we’ve seen from both end users and leadership when it comes to implementing a user research strategy. Thhroughout our conversation, we come back to the idea that even taking imperfect action towards doing user research can lead to increased data product adoption and business value. 
    Highlights/ Skip to:
    I introduce Steve Portigal, Principal of Portigal Consulting and Author of Interviewing Users (00:38)
    What changes caused Steve to release a second edition of his book (00:58)
    Steve and I discuss the importance of understanding how to conduct effective user research (03:44)
    Steve explains why it’s crucial to understand that the business challenge and the research questions are two different things (08:16)
    Brian and Steve role-play a common scenario that comes up in user research, and Steve explains an optimal workflow for user research (11:50)
    The importance of provocation in performing user research (21:02)
    How Steve would handle a situation where a member of leadership is preventing research being done with end users (24:23)
    Why a consultative approach is valuable when getting buy-in for conducting user research (35:04)
    Steve shares some of the major benefits of taking imperfect action towards starting user research (36:59)
    The impact and value even easy wins in user research can have (42:54)
    Steve describes the exploratory nature of user research and how to maximize the chance of finding the most valuable insights (46:57)
    Where you can connect with Steve and get a copy of v2 of his book, Interviewing Users (49:35)
    Quotes from Today’s Episode
    “If you don’t know what you’re doing, and you don’t know what you should be investing effort-wise, that’s the inexperience in the approach. If you don’t know how to plan, what should we be trying to solve in this research? What are we trying to learn? What are we going to do with it in the organization? Who should we be talking to? How do we find them? What do we ask them? And then a really good one: how do we make sense of that information so that it has impact that we can take away?” — Steve Portigal (07:15)
    “What do people get [from user research]? I think the chance for a team to align around something that comes in from the outside.” – Steve Portigal (41:36)
    On the impact user research can have if teams embrace it: “They had a product that did a thing that no one [understood], and they had to change the product, but also change how they talked about it, change how they built it, and change how they packaged it. And that was a really dramatic turnaround. And it came out of our research, but [mostly] because they really leaned into making use of this stuff.” – Steve Portigal (42:35)
    "If we knew all the questions to ask, we would just write a survey, right? It’s a lower time commitment from the participant to do that. But we’re trying to get at what we don’t know that we don’t know. For some of us, that’s fun!" – Steve Portigal (48:36)
    Links
    Interviewing Users (use code DATA20 to get 20% off the list price): https://rosenfeldmedia.com/books/interviewing-users-second-edition/
    Personal website: https://portigal.com
    Publisher website: https://rosenfeldmedia.com
    LinkedIn: https://www.linkedin.com/in/steveportigal/
     

    • 52 min
    134 - What Sanjeev Mohan Learned Co-Authoring “Data Products for Dummies”

    134 - What Sanjeev Mohan Learned Co-Authoring “Data Products for Dummies”

    In this episode, I’m chatting with former Gartner analyst Sanjeev Mohan who is the Co-Author of Data Products for Dummies. Throughout our conversation, Sanjeev shares his expertise on the evolution of data products, and what he’s seen as a result of implementing practices that prioritize solving for use cases and business value. Sanjeev also shares a new approach of structuring organizations to best implement ownership and accountability of data product outcomes. Sanjeev and I also explore the common challenges of product adoption and who is responsible for user experience. I purposefully had Sanjeev on the show because I think we have pretty different perspectives from which we see the data product space.
    Highlights/ Skip to:
    I introduce Sanjeev Mohan, co-author of Data Products for Dummies (00:39)
    Sanjeev expands more on the concept of writing a “for Dummies” book   (00:53)
    Sanjeev shares his definition of a data product, including both a technical and a business definition (01:59)
    Why Sanjeev believes organizational changes and accountability are the keys to preventing the acceleration of shipping data products with little to no tangible value (05:45)
    How Sanjeev recommends getting buy-in for data product ownership from other departments in an organization (11:05)
    Sanjeev and I explore adoption challenges and the topic of user experience (13:23)
    Sanjeev explains what role is responsible for user experience and design (19:03)
    Who should be responsible for defining the metrics that determine business value (28:58)
    Sanjeev shares some case studies of companies who have adopted this approach to data products and their outcomes (30:29)
    Where companies are finding data product managers currently (34:19)
    Sanjeev expands on his perspective regarding the importance of prioritizing business value and use cases (40:52)
    Where listeners can get Data Products for Dummies, and learn more about Sanjeev’s work (44:33)
    Quotes from Today’s Episode
    “You may slap a label of data product on existing artifact; it does not make it a data product because there’s no sense of accountability. In a data product, because they are following product management best practices, there must be a data product owner or a data product manager. There’s a single person [responsible for the result]. — Sanjeev Mohan (09:31)
    “I haven’t even mentioned the word data mesh because data mesh and data products, they don’t always have to go hand-in-hand. I can build data products, but I don’t need to go into the—do all of data mesh principles.” – Sanjeev Mohan (26:45)
    “We need to have the right organization, we need to have a set of processes, and then we need a simplified technology which is standardized across different teams. So, this way, we have the benefit of reusing the same technology. Maybe it is Snowflake for storage, DBT for modeling, and so on. And the idea is that different teams should have the ability to bring their own analytical engine.” – Sanjeev Mohan (27:58)
    “Generative AI, right now as we are recording, is still in a prototyping phase. Maybe in 2024, it’ll go heavy-duty production. We are not in prototyping phase for data products for a lot of companies. They’ve already been experimenting for a year or two, and now they’re actually using them in production. So, we’ve crossed that tipping point for data products.” – Sanjeev Mohan (33:15)
    “Low adoption is a problem that’s not just limited to data products. How long have we had data catalogs, but they have low adoption. So, it’s a common problem.” – Sanjeev Mohan (39:10)
    “That emphasis on technology first is a wrong approach. I tell people that I’m sorry to burst your bubble, but there are no technology projects, there are only business projects. Technology is an enabler. You don’t do technology for the sake of technology; you have to serve a business cause, so let’s start with that and keep that front and center.” – Sanjeev Mo

    • 46 min

Customer Reviews

5.0 out of 5
36 Ratings

36 Ratings

CJG, Data Analyst ,

Only listen if you actually want people to use your data products

I can not stress enough how useful this podcast is. Every episode, every interview, helps illuminate ways to ensure our users are delighted at both our data insights and the ease of getting to those insights. I especially appreciate how this podcast tackles thorny challenges like how to define success metrics for data products and what sorts of environments help data folks thrive. These are discussions we need to be having!

Data PM ,

Great podcast!

In my current company and my prior company (both earlier stage healthcare startups), I often feel like I’m on an island. But, every time I listen to Experiencing Data, I feel so uplifted, gaining new perspectives and validation in some of the challenges I experience in my day to day work.

Will nickname not taken angel ,

Informative and well designed

This is an excellent podcast for anyone interested in making data more useful and less stressful, with great conversations about design and analytics.

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