Explore UX considerations for Large Language Models (LLMs) in enterprise applications. Learn about the importance of quality data, balance between creativity and accuracy, and handling hallucinations. Discover how AI and LLMs open doors for fresh visioning work. Hear Brian's take on LLMs in enterprise software. Reflect on using LLMs for trip insurance shopping and the role of AI as an assistant in decision-making.

Exploring the challenges and importance of user experience design when deploying Large Language Models (LLMs) in enterprise applications. Topics include FOMO driving LLM initiatives, UX considerations, challenges with LLM UIs, measuring UX outcomes, and the need for careful benchmarks. The podcast also discusses the immature space of LLM UI/UX design and the mindset needed for integrating LLMs into enterprise software.

Ben Shneiderman, a HCI expert, discusses human-centered AI design with Brian T. O’Neill. Topics include safety in AI systems, independent oversight, human control, research impact on user experiences, explainable AI interfaces, and Ben's upcoming book on human-centered AI.

Malcolm Hawker, Head of Data Management at Profisee, discusses the importance of a customer-centric approach in data products. He emphasizes empathy, understanding customer needs, and developing business skills for data experts. Malcolm also highlights the benefits of a product-oriented approach to ML and analytics, addressing the UX question for adoption and business value, and the concept of data culture.

Welcome to another curated, Promoted Episode of Experiencing Data!  In episode 144, Shashank Garg, Co-Founder and CEO of Infocepts, joins me to explore whether all this discussion of data products out on the web actually has substance and is worth the perceived extra effort. Do we always need to take a product approach for ML and analytics initiatives? Shashank dives into how Infocepts approaches the creation of data solutions that are designed to be actionable within specific business workflows—and as I often do, I started out by asking Shashank how he and Infocepts define the term “data product.” We discuss a few real-world applications Infocepts has built, and the measurable impact of these data products—as well as some of the challenges they’ve faced that your team might as well. Skill sets also came up; who does design? Who takes ownership of the product/value side? And of course, we touch a bit on GenAI.     Highlights/ Skip to Shashank gives his definition of data products  (01:24) We tackle the challenges of user adoption in data products (04:29) We discuss the crucial role of integrating actionable insights into data products for enhanced decision-making (05:47) Shashank shares insights on the evolution of data products from concept to practical integration (10:35) We explore the challenges and strategies in designing user-centric data products (12:30) I ask Shashank about typical environments and challenges when starting new data product consultations (15:57) Shashank explains how Infocepts incorporates AI into their data solutions (18:55) We discuss the importance of understanding user personas and engaging with actual users (25:06) Shashank describes the roles involved in data product development’s ideation and brainstorming stages (32:20) The issue of proxy users not truly representing end-users in data product design is examined (35:47) We consider how organizations are adopting a product-oriented approach to their data strategies (39:48) Shashank and I delve into the implications of GenAI and other AI technologies on product orientation and user adoption (43:47) Closing thoughts (51:00)     Quotes from Today’s Episode “Data products, at least to us at Infocepts, refers to a way of thinking about and organizing your data in a way so that it drives consumption, and most importantly, actions.” - Shashank Garg (1:44) “The way I see it is [that] the role of a DPM (data product manager)—whether they have the title or not—is benefits creation. You need to be responsible for benefits, not for outputs. The outputs have to create benefits or it doesn’t count. Game over” - Brian O’Neill (10:07) We talk about bridging the gap between the worlds of business and analytics... There's a huge gap between the perception of users and the tech leaders who are producing it." - Shashank Garg (17:37) “IT leaders often limit their roles to provisioning their secure data, and then they rely on businesses to be able to generate insights and take actions. Sometimes this handoff works, and sometimes it doesn’t because of quality governance.” - Shashank Garg  (23:02) “Data is the kind of field where people can react very, very quickly to what’s wrong.”  - Shashank Garg (29:44) “It’s much easier to get to a good prototype if we know what the inputs to a prototype are, which include data about the people who are going to use the solution, their usage scenarios, use cases, attitudes, beliefs…all these kinds of things.” - Brian O’Neill (31:49) “For data, you need a separate person, and then for designing, you need a separate person, and for analysis, you need a separate person—the more you can combine, I don’t think you can create super-humans who can do all three, four disciplines, but at least two disciplines and can appreciate the third one that makes it easier.” - Shashank Garg (39:20) “When we think of AI, we’re all talking about multiple different delivery methods here. I think AI is starting to become GenAI to a lot of non-data people. It’s like their—everything is GenAI.” -  Brian O'Neill (43:48)     Links Infocepts website: https://www.infocepts.ai/ Shashank Garg on LinkedIn: https://www.linkedin.com/in/shashankgarg/  Top 5 Data & AI initiatives for business success: https://www.infocepts.ai/downloads/top-5-data-and-ai-initiatives-to-drive-business-growth-in-2024-beyond/

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 recommendations to get ahead:https://designingforanalytics.com/services/

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 of data because it’s not helping them make any decision about what to change.”- Brian O’Neill (37:53) Links Watch the original Crowdcast video recording of this episode Brian’s CED UX Framework for Advanced Analytics Solutions Join Brian’s Insights mailing list

Duncan Milne, Data Strategist at RBC, discusses transitioning to a product mindset for data products. He outlines challenges, strategies, and the importance of understanding user needs. Highlights include developing a framework, overcoming resistance, and scaling challenges in a large organization like RBC.

Thabata Romanowski aka T from Data Rocks NZ discusses applying UX design in analytical data products, overcoming design misconceptions, and promoting user adoption through purposeful, context-sensitive, collaborative, and humanistic design. They explore the importance of clear communication, user research, and involving end users in the design process.

Explore challenges in designing embedded analytics, catering to multiple stakeholders in SAAS companies. Learn about monetizing SAAS analytics, AI integration in BI industry, and the importance of user experience in data products. Discover strategies for successful embedded BI products and the role of Microsoft Excel in analytics product usage.