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Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)
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144 - The Data Product Debate: Essential Tech or Excessive Effort? with Shashank Garg, CEO of Infocepts (Promoted Episode)
Manage episode 420718776 series 2938687
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/
106 פרקים
Manage episode 420718776 series 2938687
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/
106 פרקים
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1 176 - (Part 2) The MIRRR UX Framework for Designing Trustworthy Agentic AI Applications 29:52

1 175 - The MIRRR UX Framework for Designing Trustworthy Agentic AI Applications (Part 1) 28:51

1 174 - Why AI Adoption Moves at the Speed of User Trust Irina Malkova on Lessons Learned Building Data Products at Salesforce 47:50

1 173 - Pendo’s CEO on Monetizing an Analytics SAAS Product, Avoiding Dashboard Fatigue, and How AI is Changing Product Work 43:49

1 172 - Building AI Assistants, Not Autopilots: What Tony Zhang’s Research Shows About Automation Blindness 44:24

1 171 - Who Can Succeed in a Data or AI Product Management Role? 50:04

1 170 - Turning Data into Impactful AI Products at Experian: Lessons from North American Chief AI Officer Shri Santhnam (Promoted Episode) 42:33

1 169 - AI Product Management and UX: What’s New (If Anything) About Making Valuable LLM-Powered Products with Stuart Winter-Tear 1:01:05

1 168 - 10 Challenges Internal Data Teams May Face Building Their First Revenue-Generating Data Product 38:24

1 167 - AI Product Management and Design: How Natalia Andreyeva and Team at Infor Nexus Create B2B Data Products that Customers Value 37:34

1 166 - Can UX Quality Metrics Increase Your Data Product's Business Value and Adoption? 26:12

1 165 - How to Accommodate Multiple User Types and Needs in B2B Analytics and AI Products When You Lack UX Resources 49:04

1 164 - The Hidden UX Taxes that AI and LLM Features Impose on B2B Customers Without Your Knowledge 45:25

1 163 - It’s Not a Math Problem: How to Quantify the Value of Your Enterprise Data Products or Your Data Product Management Function 41:41

1 162 - Beyond UI: Designing User Experiences for LLM and GenAI-Based Products 42:07

1 161 - Designing and Selling Enterprise AI Products [Worth Paying For] 34:00

1 160 - Leading Product Through a Merger/Acquisition: Lessons from The Predictive Index’s CPO Adam Berke 42:10

1 159 - Uncorking Customer Insights: How Data Products Revealed Hidden Gems in Liquor & Hospitality Retail 40:47

1 158 - From Resistance to Reliance: Designing Data Products for Non-Believers with Anna Jacobson of Operator Collective 43:41

1 157 - How this materials science SAAS company brings PM+UX+data science together to help materials scientists accelerate R&D 34:58

1 156-The Challenges of Bringing UX Design and Data Science Together to Make Successful Pharma Data Products with Jeremy Forman 41:37

1 155 - Understanding Human Engagement Risk When Designing AI and GenAI User Experiences 55:33

1 154 - 10 Things Founders of B2B SAAS Analytics and AI Startups Get Wrong About DIY Product and UI/UX Design 44:47

1 153 - What Impressed Me About How John Felushko Does Product and UX at the Analytics SAAS Company, LabStats 57:31

1 152 - 10 Reasons Not to Get Professional UX Design Help for Your Enterprise AI or SAAS Analytics Product 53:00

1 151 - Monetizing SAAS Analytics and The Challenges of Designing a Successful Embedded BI Product (Promoted Episode) 49:57

1 150 - How Specialized LLMs Can Help Enterprises Deliver Better GenAI User Experiences with Mark Ramsey 52:22

1 149 - What the Data Says About Why So Many Data Science and AI Initiatives Are Still Failing to Produce Value with Evan Shellshear 50:18

1 148 - UI/UX Design Considerations for LLMs in Enterprise Applications (Part 2) 26:36

1 147 - UI/UX Design Considerations for LLMs in Enterprise Applications (Part 1) 25:46

1 146 - (Rebroadcast) Beyond Data Science - Why Human-Centered AI Needs Design with Ben Shneiderman 42:07

1 145 - Data Product Success: Adopting a Customer-Centric Approach With Malcolm Hawker, Head of Data Management at Profisee 53:09

1 144 - The Data Product Debate: Essential Tech or Excessive Effort? with Shashank Garg, CEO of Infocepts (Promoted Episode) 52:38

1 143 - The (5) Top Reasons AI/ML and Analytics SAAS Product Leaders Come to Me For UI/UX Design Help 50:01

1 142 - Live Webinar Recording: My UI/UX Design Audit of a New Podcast Analytics Service w/ Chris Hill (CEO, Humblepod) 50:56

1 141 - How They’re Adopting a Producty Approach to Data Products at RBC with Duncan Milne 43:49

1 140 - Why Data Visualization Alone Doesn’t Fix UI/UX Design Problems in Analytical Data Products with T from Data Rocks NZ 42:44

1 139 - Monetizing SAAS Analytics and The Challenges of Designing a Successful Embedded BI Product (Promoted Episode) 51:02

1 138 - VC Spotlight: The Impact of AI on SAAS and Data/Developer Products in 2024 w/ Ellen Chisa of BoldStart Ventures 33:05

1 137 - Immature Data, Immature Clients: When Are Data Products the Right Approach? feat. Data Product Architect, Karen Meppen 44:50

1 136 - Navigating the Politics of UX Research and Data Product Design with Caroline Zimmerman 44:16

1 135 - “No Time for That:” Enabling Effective Data Product UX Research in Product-Immature Organizations 52:47

1 134 - What Sanjeev Mohan Learned Co-Authoring “Data Products for Dummies” 46:52


1 132 - Leveraging Behavioral Science to Increase Data Product Adoption with Klara Lindner 42:56

1 131 - 15 Ways to Increase User Adoption of Data Products (Without Handcuffs, Threats and Mandates) with Brian T. O’Neill 36:57

1 130 - Nick Zervoudis on Data Product Management, UX Design Training and Overcoming Imposter Syndrome 48:56

1 129 - Why We Stopped, Deleted 18 Months of ML Work, and Shifted to a Data Product Mindset at Coolblue 35:21

1 128 - Data Products for Dummies and The Importance of Data Product Management with Vishal Singh of Starburst 53:01

1 127 - On the Road to Adopting a “Producty” Approach to Data Products at the UK’s Care Quality Commission with Jonathan Cairns-Terry 36:55

1 126 - Designing a Product for Making Better Data Products with Anthony Deighton 47:38

1 125 - Human-Centered XAI: Moving from Algorithms to Explainable ML UX with Microsoft Researcher Vera Liao 44:42

1 124 - The PiCAA Framework: My Method to Generate ML/AI Use Cases from a UX Perspective 21:51

1 123 - Learnings From the CDOIQ Symposium and How Data Product Definitions are Evolving with Brian T. O’Neill 27:17

1 122 - Listener Questions Answered: Conducting Effective Discovery for Data Products with Brian T. O’Neill 33:46

1 121 - How Sainsbury’s Head of Data Products for Analytics and ML Designs for User Adoption with Peter Everill 39:40

1 120 - The Portfolio Mindset: Data Product Management and Design with Nadiem von Heydebrand (Part 2) 41:35

1 119 - Skills vs. Roles: Data Product Management and Design with Nadiem von Heydebrand (Part 1) 37:12

1 118 - Attracting Talent and Landing a Role in Data Product Management with Kyle Winterbottom 49:23

1 117 - Phil Harvey, Co-Author of “Data: A Guide to Humans,” on the Non-Technical Skills Needed to Produce Valuable AI Solutions 39:39

1 116 - 10 Reasons Your Customers Don’t Make Time for Your Data Product Initiatives + A Big Update on the Data Product Leadership Community (DPLC) 45:56

1 115 - Applying a Product and UX-Driven Approach to Building Stuart’s Data Platform with Osian Jones 45:19

1 114 - Designing Anti-Biasing and Explainability Tools for Data Scientists Creating ML Models with Josh Noble 42:05

1 113 - Turning the Weather into an Indispensable Data Product for Businesses with Cole Swain, VP Product at tomorrow.io 38:53

1 112 - Solving for Common Pitfalls When Developing a Data Strategy featuring Samir Sharma, CEO of datazuum 35:18

1 111 - Designing and Monetizing Data Products Like a Startup with Yuval Gonczarowski 33:15

1 110 - CDO Spotlight: The Value and Journey of Implementing a Data Product Mindset with Sebastian Klapdor of Vista 32:52

1 109 - The Role of Product Management and Design in Turning ML/AI into a Valuable Business with Bob Mason from Argon Ventures 32:43

1 108 - Google Cloud’s Bruno Aziza on What Makes a Good Customer-Obsessed Data Product Manager 50:43

1 107 - Tom Davenport on Data Product Management and the Impact of a Product Orientation on Enterprise Data Science and ML Initiatives 42:52

1 106 - Ideaflow: Applying the Practice of Design and Innovation to Internal Data Products w/ Jeremy Utley 44:14

1 105 - Defining “Data Product” the Producty Way and the Non-technical Skills ML/AI Product Managers Need 41:53

1 104 - Surfacing the Unarticulated Needs of Users and Stakeholders through Effective Listening 44:12

1 103 - Helping Pediatric Cardiac Surgeons Make Better Decisions with ML featuring Eugenio Zuccarelli of MIT Media Lab 42:33

1 102 - CDO Spotlight: The Non-Technical Roles Data Science and Analytics Teams Need to Drive Adoption of Data Products w/ Iván Herrero Bartolomé 35:05

1 101 - Insights on Framing IOT Solutions as Data Products and Lessons Learned from Katy Pusch 39:11

1 100 - Why Your Data, AI, Product & Business Strategies Must Work Together (and Digital Transformation is The Wrong Framing) with Vin Vashishta 45:08

1 099 - Don’t Boil the Ocean: How to Generate Business Value Early With Your Data Products with Jon Cooke, CTO of Dataception 48:28

1 098 - Why Emilie Schario Wants You to Run Your Data Team Like a Product Team 39:41

1 097 - Why Regions Bank’s CDAO, Manav Misra, Implemented a Product-Oriented Approach to Designing Data Products 35:22

1 096 - Why Chad Sanderson, Head of Product for Convoy’s Data Platform, is a Champion of Data UX 37:36

1 095 - Increasing Adoption of Data Products Through Design Training: My Interview from TDWI Munich 16:50

1 094 - The Multi-Million Dollar Impact of Data Product Management and UX with Vijay Yadav of Merck 46:02

1 093 - Why Agile Alone Won’t Increase Adoption of Your Enterprise Data Products 47:16

1 092 - How to measure data product value from a UX and business lens (and how not to do it) 34:46

1 091 - How Brazil’s Biggest Fiber Company, Oi, Leverages Design To Create Useful Data Products with Sr. Exec. Design Manager, João Critis 31:24

1 090 - Michelle Carney’s Mission With MLUX: Bringing UX and Machine Learning Together 31:43

1 089 - Reader Questions Answered about Dashboard UX Design 48:16

1 088 - Doing UX Research for Data Products and The Magic of Qualitative User Feedback with Mike Oren, Head of Design Research at Klaviyo 42:26

1 087 - How Data Product Management and UX Integrate with Data Scientists at Albertsons Companies to Improve the Grocery Shopping Experience 37:36

1 086 - CED: My UX Framework for Designing Analytics Tools That Drive Decision Making 27:57

1 085 - Dr. William D. Báez on the Journey and ROI of Integrating UX Design into Machine Learning and Analytics Solutions 44:42

1 084 - The Messy Truth of Designing and Building a Successful Analytics SAAS Product featuring Jonathan Kay (CEO, Apptopia) 39:56

1 083 -Why Bob Goodman Thinks Product Management and Design Must Dance Together to Create “Experience Layers” for Data Products 33:08

1 082 - What the 2021 $1M Squirrel AI Award Winner Wants You To Know About Designing Interpretable Machine Learning Solutions w/ Cynthia Rudin 37:55

1 081 - The Cultural and $ Benefits of Human-Centered AI in the Enterprise: Digging Into BCG/MIT Sloan’s AI Research w/ François Candelon 36:45

1 080 – How to Measure the Impact of Data Products…and Anything Else with Forecasting and Measurement Expert Doug Hubbard 46:00

1 079 - How Sisu’s CPO, Berit Hoffmann, Is Approaching the Design of Their Analytics Product…and the UX Mistakes She Won’t Make Again 36:02

1 078 - From Data to Product: What is Data Product Management and Why Do We Need It with Eric Weber 40:46

1 077 - Productizing Analytics for Performing Arts Organizations with AMS Analytics CPO Jordan Gross Richmond 42:35
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