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.

116 - 10 Reasons Your Customers Don’t Make Time for Your Data Product Initiatives + A Big Update on the Data Product Leadership Community (DPLC)

If you’re a leader tasked with generating business and org. value through ML/AI and analytics, you’ve probably struggled with low user adoption.

Making the tech gets easier, but getting users to use, and buyers to buy, remains difficult—but you’ve heard a ”data product” approach can help. Can it?

My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I offer you a consulting designer’s perspective on why creating ML and analytics outputs isn’t enough to create business and UX outcomes.

How can UX design and product management help you create innovative ML/AI and analytical data products?

What exactly are data products—and how can data product management help you increase user adoption of ML/analytics—so that stakeholders can finally see the business value of your data?

Every 2 weeks, I answer these questions via solo episodes and interviews with innovative chief data officers, data product management leaders, and top UX professionals.

Hashtag: #ExperiencingData. 

PODCAST HOMEPAGE: 
Get 1-page summaries, text transcripts, and join my Insights mailing list:
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ABOUT THE HOST, BRIAN T. O’NEILL:
https://designingforanalytics.com/bio/

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Welcome back! In today's solo episode, I share the top five struggles that enterprise SAAS leaders have in the analytics/insight/decision support space that most frequently leads them to think they have a UI/UX design problem that has to be addressed. A lot of today's episode will talk about "slow creep," unaddressed design problems that gradually build up over time and begin to impact both UX and your revenue negatively. I will also share 20 UI and UX design problems I often see (even if clients do not!) that, when left unaddressed, may create sales friction, adoption problems, churn, or unhappy end users. If you work at a software company or are directly monetizing an ML or analytical data product, this episode is for you! 
Highlights/ Skip to 
I discuss how specific UI/UX design problems can significantly impact business performance (02:51)
I discuss five common reasons why enterprise software leaders typically reach out for help (04:39)
The 20 common symptoms I've observed in client engagements that indicate the need for professional UI/UX intervention or training (13:22)
The dangers of adding too many features or customization and how it can overwhelm users (16:00)
The issues of integrating  AI into user interfaces and UXs without proper design thinking  (30:08)
I encourage listeners to apply the insights shared to improve their data products (48:02)
Quotes from Today’s Episode
“One of the problems with bad design is that some of it we can see and some of it we can't — unless you know what you're looking for." - Brian O’Neill (02:23)
“Design is usually not top of mind for an enterprise software product, especially one in the machine learning and analytics space. However, if you have human users, even enterprise ones, their tolerance for bad software is much lower today than in the past.” Brian O’Neill - (13:04)
“Early on when you're trying to get product market fit, you can't be everything for everyone. You need to be an A+ experience for the person you're trying to satisfy.” -Brian O’Neill (15:39)
“Often when I see customization, it is mostly used as a crutch for not making real product strategy and design decisions.”  - Brian O’Neill (16:04) 
"Customization of data and dashboard products may be more of a tax than a benefit. In the marketing copy, customization sounds like a benefit...until you actually go in and try to do it. It puts the mental effort to design a good solution on the user." - Brian O’Neill (16:26)
“We need to think strategically when implementing Gen AI or just AI in general into the product UX because it won’t automatically help drive sales or increase business value.” - Brian O’Neill (20:50) 
“A lot of times our analytics and machine learning tools… are insight decision support products. They're supposed to be rooted in facts and data, but when it comes to designing these products, there's not a whole lot of data and facts that are actually informing the product design choices.” Brian O’Neill - (30:37)
“If your IP is that special, but also complex, it needs the proper UI/UX design treatment so that the value can be surfaced in such a way someone is willing to pay for it if not also find it indispensable and delightful.” - Brian O’Neill (45:02)
Links
The (5) big reasons AI/ML and analytics product leaders invest in UI/UX design help: https://designingforanalytics.com/resources/the-5-big-reasons-ai-ml-and-analytics-product-leaders-invest-in-ui-ux-design-help/ 
Subscribe for free insights on designing useful, high-value enterprise ML and analytical data products: https://designingforanalytics.com/list 
Access my free frameworks, guides, and additional reading for SAAS leaders on designing high-value ML and analytical data products: https://designingforanalytics.com/resources 
Need help getting your product’s design/UX on track—so you can see more sales, less churn, and higher user adoption? Schedule a free 60-minute Discovery Call with me and I’ll give you my read on your situation and my rec

143 - The (5) Top Reasons AI/ML and Analytics SAAS Product Leaders Come to Me For UI/UX Design Help

Welcome to a special edition of Experiencing Data. This episode is the audio capture from a live Crowdcast video webinar I gave on April 26th, 2024 where I conducted a mini UI/UX design audit of a new podcast analytics service that Chris Hill, CEO of Humblepod, is working on to help podcast hosts grow their show. Humblepod is also the team-behind-the-scenes of Experiencing Data, and Chris had asked me to take a look at his new “Listener Lifecycle” tool to see if we could find ways to improve the UX and visualizations in the tool, how we might productize this MVP in the future, and how improving the tool’s design might help Chris help his prospective podcast clients learn how their listener data could help them grow their listenership and “true fans.” On a personal note, it was fun to talk to Chris on the show given we speak every week:  Humblepod has been my trusted resource for audio mixing, transcription, and show note summarizing for probably over 100 of the most recent episodes of Experiencing Data. It was also fun to do a “live recording” with an audience—and we did answer questions in the full video version. (If you missed the invite, join my Insights mailing list to get notified of future free webinars).
 
To watch the full audio and video recording on Crowdcast, free, head over to: https://www.crowdcast.io/c/podcast-analytics-ui-ux-design
Highlights/ Skip to:
Chris talks about using data to improve podcasts and his approach to podcast numbers  (03:06)
Chris introduces the Listener Lifecycle model which informed the dashboard design (08:17)
Chris and I discuss the importance of labeling and terminology in analytics UIs (11:00)
We discuss designing for practical use of analytics dashboards to provide actionable insights (17:05)
We discuss the challenges podcast hosts face in understanding and utilizing data effectively and how design might help (21:44)
I discuss how my CED UX framework for advanced analytics applications helps to facilitate actionable insights (24:37)
I highlight the importance of presenting data effectively and in a way that centers to user needs (28:50)
I express challenges users may have with podcast rankings and the reliability of data sources (34:24) 
Chris and I discuss tailoring data reports to meet the specific needs of clients (37:14)
Quotes from Today’s Episode
“The irony for me as someone who has a podcast about machine learning and analytics and design is that I basically never look at my analytics.” - Brian O’Neill (01:14)
“The problem that I have found in podcasting is that the number that everybody uses to gauge whether a podcast is good or not is the download number…But there’s a lot of other factors in a podcast that can tell you how successful it’s going to be…where you can pull levers to…grow your show, or engage more with an audience.” - Chris Hill (03:20)
“I have a framework for user experience design for analytics called CED, which stands for Conclusions, Evidence, Data… The basic idea is really simple: lead your analytic service with conclusions.”- Brian O’Neill (24:37)
“Where the eyes glaze over is when tools are mostly about evidence generators, and we just give everybody the evidence, but there’s no actual analysis about how [this is] helping me improve my life or my business. It’s just evidence. I need someone to put that together.” - Brian O’Neill (25:23)
“Sometimes the data doesn’t provide enough of a conclusion about what to do…This is where your opinion starts to matter” - Brian O’Neill (26:07)
“It sounds like a benefit, but drilling down for most people into analytics stuff is usually a tax unless you’re an analyst.” - Brian O’Neill (27:39)
“Where’s the source of this data, and who decided what these numbers are? Because so much of this stuff…is not shared. As someone who’s in this space, it’s not even that it’s confusing. It’s more like, you got to distill this down for me.” - Brian O’Neill (34:57)
“Your clients are probably going to glaze over at this level o

142 -  Live Webinar Recording: My UI/UX Design Audit of a New Podcast Analytics Service w/ Chris Hill (CEO, Humblepod)

In this week's episode of Experiencing Data, I'm joined by Duncan Milne, a Director, Data Investment & Product Management at the Royal Bank of Canada (RBC). Today, Duncan (who is also a member of the DPLC) gives a preview of his upcoming webinar on April 24, 2024 entitled, “Is that Data Product Worth Building? Estimating Economic Value…Before You Build It!”  Duncan shares his experience of implementing a product mindset within RBC's Chief Data Office, and he explains some of the challenges, successes, and insights gained along the way. He emphasizes the critical role of understanding user needs and evaluating the economic impact of data products—before they are built. Duncan was gracious to let us peek inside and see a transformation that is currently in progress and I’m excited to check out his webinar this month!
Highlights/ Skip to:
I introduce Duncan Milne from RBC (00:00)
Duncan outlines the Chief Data Office's function at RBC  (01:01)
We discuss data products and how they are used to improve business process (04:05)
The genesis behind RBC's move towards a product-centric approach in handling data, highlighting initial challenges and strategies for fostering a product mindset (07:26)
Duncan discusses developing a framework to guide the lifecycle of data products at RBC (09:29)
Duncan addresses initial resistance and adaptation strategies for engaging teams in a new product-centric methodology (12:04)
The scaling challenges of applying a product mindset across a large organization like RBC (22:02)
Insights into the framework for evaluating and prioritizing data product ideas based on their desirability, usability, feasibility, and viability. (26:30)
Measuring success and value in data product management (30:45)
Duncan explores process mapping challenges in banking (34:13)
Duncan shares creating specialized training for data product management at RBC (36:39)
Duncan offers advice and closing thoughts on data product management (41:38)
Quotes from Today’s Episode
“We think about data products as anything that solves a problem using data... it's helping someone do something they already do or want to do faster and better using data." - Duncan Milne (04:29)
“The transition to data product management involves overcoming initial resistance by demonstrating the tangible value of this approach." - Duncan Milne (08:38)
"You have to want to show up and do this kind of work [adopting a product mindset in data product management]…even if you do a product the right way, it doesn’t always work, right? The thing you make may not be desirable, it may not be as usable as it needs to be. It can be technically right and still fail. It’s not a guarantee, it’s just a better way of working.” - Brian T. O’Neill (15:03)
“[Product management]... it's like baking versus cooking. Baking is a science... cooking is much more flexible. It’s about... did we produce a benefit for users? Did we produce an economic benefit? ...It’s a multivariate problem... a lot of it is experimentation and figuring out what works." - Brian T. O'Neill (23:03)
"The easy thing to measure [in product management] is did you follow the process or not? That is not the point of product management at all. It's about delivering benefits to the stakeholders and to the customer." - Brian O'Neill (25:16)
“Data product is not something that is set in stone... You can leverage learnings from a more traditional product approach, but don’t be afraid to improvise." - Duncan Milne (41:38)
“Data products are fundamentally different from digital products, so even the traditional approach to product management in that space doesn’t necessarily work within the data products construct.” - Duncan Milne (41:55)
“There is no textbook for data product management; the field is still being developed…don’t be afraid to create your own answer if what exists out there doesn’t necessarily work within your context.”- Duncan Milne (42:17)
Links
Duncan’s Linkedin: https://www.linkedin.com/in/

141 - How They’re Adopting a Producty Approach to Data Products at RBC with Duncan Milne

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 what the problem is. There’s a lot of assumptions happening t

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, something new as promised at the beginning of the year. Today, I’m exploring the world of embedded analytics with Zalak Trivedi from Sigma Computing—and this is also the first approved Promoted Episode on the podcast. In today’s episode, Zalak shares his journey as the product lead for Sigma’s embedded analytics and reporting solution which seeks to accelerate and simplify the deployment of decision support dashboards to their SAAS companies’ customers. Right there, we have the first challenge that Zalak was willing to dig into with me: designing a platform UX when we have multiple stakeholder and user types. In Sigma’s case, this means Sigma’s buyers, the developers that work at these SAAS companies to integrate Sigma into their products, and then the actual customers of these SAAS companies who will be the final end users of the resulting dashboards.  also discuss the challenges of creating products that serve both beginners and experts and how AI is being used in the BI industry.  
 
Highlights/ Skip to:
I introduce Zalak Trivedi from Sigma Computing onto the show (03:15)
Zalak shares his journey leading the vision for embedded analytics at Sigma and explains what Sigma looks like when implemented into a customer’s SAAS product . (03:54)
Zalak and I discuss the challenge of integrating Sigma's analytics into various companies' software, since they need to account for a variety of stakeholders. (09:53)
We explore Sigma's team approach to user experience with product management, design, and technical writing (15:14)
Zalak reveals how Sigma leverages telemetry to understand and improve user interactions with their products (19:54)
Zalak outlines why Sigma is a faster and more supportive alternative to building your own analytics (27:21)
We cover data monetization, specifically looking at how SAAS companies can monetize analytics and insights (32:05)
Zalak highlights how Sigma is integratingAI into their BI solution (36:15)
Zalak share his customers' current pain points and interests (40:25) 
We wrap up with final thoughts and ways to connect with Zalak and learn more about Sigma (49:41) 
Quotes from Today’s Episode
"Something I’m really excited about personally that we are working on is [moving] beyond analytics to help customers build entire data applications within Sigma. This is something we are really excited about as a company, and marching towards [achieving] this year." - Zalak Trivedi (04:04)
“The whole point of an embedded analytics application is that it should look and feel exactly like the application it’s embedded in, and the workflow should be seamless.” - Zalak Trivedi (09:29) 
“We [at Sigma] had to switch the way that we were thinking about personas. It was not just about the analysts or the data teams; it was more about how do we give the right tools to the [SAAS] product managers and developers to embed Sigma into their product.” - Zalak Trivedi (11:30) 
“You can’t not have a design, and you can’t not have a user experience. There’s always an experience with every tool, solution, product that we use, whether it emerged organically as a byproduct, or it was intentionally created through knowledge data... it was intentional” - Brian O’Neill (14:52) 
“If we find that [in] certain user experiences,people are tripping up, and they’re not able to complete an entire workflow, we flag that, and then we work with the product managers, or [with] our customers essentially, and figure out how we can actually simplify these experiences.” - Zalak Trivedi (20:54)
“We were able to convince many small to medium businesses and startups to sign up with Sigma. The success they experienced after embedding Sigma was tremendous. Many of our customers managed to monetize their existing data within weeks, or at most, a couple of months, with lean development teams of two to three developers and a few business-side personnel, generating seven-figure income streams from that.” - Zalak Trivedi

116 - 10 Reasons Your Customers Don’t Make Time for Your Data Product Initiatives + A Big Update on the Data Product Leadership Community (DPLC)

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