15 subscribers
התחל במצב לא מקוון עם האפליקציה Player FM !
Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)
«
»
094 - The Multi-Million Dollar Impact of Data Product Management and UX with Vijay Yadav of Merck
Manage episode 332778086 series 2527129
Today I sit down with Vijay Yadav, head of the data science team at Merck Manufacturing Division. Vijay begins by relating his own path to adopting a data product and UX-driven approach to applied data science, andour chat quickly turns to the ever-present challenge of user adoption. Vijay discusses his process of designing data products with customers, as well as the impact that building user trust has on delivering business value. We go on to talk about what metrics can be used to quantify adoption and downstream value, and then Vijay discusses the financial impact he has seen at Merck using this user-oriented perspective. While we didn’t see eye to eye on everything, Vijay was able to show how focusing on the last mile UX has had a multi-million dollar impact on Merck. The conversation concludes with Vijay’s words of advice for other data science directors looking to get started with a design and user-centered approach to building data products that achieve adoption and have measurable impact.
In our chat, we covered Vijay’s design process, metrics, business value, and more:
- Vijay shares how he came to approach data science with a data product management approach and how UX fits in (1:52)
- We discuss overcoming the challenge of user adoption by understanding user thinking and behavior (6:00)
- We talk about the potential problems and solutions when users self-diagnose their technology needs (10:23)
- Vijay delves into what his process of designing with a customer looks like (17:36)
- We discuss the impact “solving on the human level” has on delivering real world benefits and building user trust (21:57)
- Vijay talks about measuring user adoption and quantifying downstream value—and Brian discusses his concerns about tool usage metrics as means of doing this (25:35)
- Brian and Vijay discuss the multi-million dollar financial and business impact Vijay has seen at Merck using a more UX driven approach to data product development (31:45)
- Vijay shares insight on what steps a head of data science might wish to take to get started implementing a data product and UX approach to creating ML and analytics applications that actually get used (36:46)
- “They will adopt your solution if you are giving them everything they need so they don’t have to go look for a workaround.” - Vijay (4:22)
- “It’s really important that you not only capture the requirements, you capture the thinking of the user, how the user will behave if they see a certain way, how they will navigate, things of that nature.” - Vijay (7:48)
- “When you’re developing a data product, you want to be making sure that you’re taking the holistic view of the problem that can be solved, and the different group of people that we need to address. And, you engage them, right?” - Vijay (8:52)
- “When you’re designing in low fidelity, it allows you to design with users because you don’t spend all this time building the wrong thing upfront, at which point it’s really expensive in time and money to go and change it.” - Brian (17:11)
- "People are the ones who make things happen, right? You have all the technology, everything else looks good, you have the data, but the people are the ones who are going to make things happen.” - Vijay (38:47)
- “You want to make sure that you [have] a strong team and motivated team to deliver. And the human spirit is something, you cannot believe how stretchable it is. If the people are motivated, [and even if] you have less resources and less technology, they will still achieve [your goals].” - Vijay (42:41)
- “You’re trying to minimize any type of imposition on [the user], and make it obvious why your data product is better—without disruption. That’s really the key to the adoption piece: showing how it is going to be better for them in a way they can feel and perceive. Because if they don’t feel it, then it’s just another hoop to jump through, right?” - Brian (43:56)
LinkedIn: https://www.linkedin.com/in/vijyadav/
113 פרקים
Manage episode 332778086 series 2527129
Today I sit down with Vijay Yadav, head of the data science team at Merck Manufacturing Division. Vijay begins by relating his own path to adopting a data product and UX-driven approach to applied data science, andour chat quickly turns to the ever-present challenge of user adoption. Vijay discusses his process of designing data products with customers, as well as the impact that building user trust has on delivering business value. We go on to talk about what metrics can be used to quantify adoption and downstream value, and then Vijay discusses the financial impact he has seen at Merck using this user-oriented perspective. While we didn’t see eye to eye on everything, Vijay was able to show how focusing on the last mile UX has had a multi-million dollar impact on Merck. The conversation concludes with Vijay’s words of advice for other data science directors looking to get started with a design and user-centered approach to building data products that achieve adoption and have measurable impact.
In our chat, we covered Vijay’s design process, metrics, business value, and more:
- Vijay shares how he came to approach data science with a data product management approach and how UX fits in (1:52)
- We discuss overcoming the challenge of user adoption by understanding user thinking and behavior (6:00)
- We talk about the potential problems and solutions when users self-diagnose their technology needs (10:23)
- Vijay delves into what his process of designing with a customer looks like (17:36)
- We discuss the impact “solving on the human level” has on delivering real world benefits and building user trust (21:57)
- Vijay talks about measuring user adoption and quantifying downstream value—and Brian discusses his concerns about tool usage metrics as means of doing this (25:35)
- Brian and Vijay discuss the multi-million dollar financial and business impact Vijay has seen at Merck using a more UX driven approach to data product development (31:45)
- Vijay shares insight on what steps a head of data science might wish to take to get started implementing a data product and UX approach to creating ML and analytics applications that actually get used (36:46)
- “They will adopt your solution if you are giving them everything they need so they don’t have to go look for a workaround.” - Vijay (4:22)
- “It’s really important that you not only capture the requirements, you capture the thinking of the user, how the user will behave if they see a certain way, how they will navigate, things of that nature.” - Vijay (7:48)
- “When you’re developing a data product, you want to be making sure that you’re taking the holistic view of the problem that can be solved, and the different group of people that we need to address. And, you engage them, right?” - Vijay (8:52)
- “When you’re designing in low fidelity, it allows you to design with users because you don’t spend all this time building the wrong thing upfront, at which point it’s really expensive in time and money to go and change it.” - Brian (17:11)
- "People are the ones who make things happen, right? You have all the technology, everything else looks good, you have the data, but the people are the ones who are going to make things happen.” - Vijay (38:47)
- “You want to make sure that you [have] a strong team and motivated team to deliver. And the human spirit is something, you cannot believe how stretchable it is. If the people are motivated, [and even if] you have less resources and less technology, they will still achieve [your goals].” - Vijay (42:41)
- “You’re trying to minimize any type of imposition on [the user], and make it obvious why your data product is better—without disruption. That’s really the key to the adoption piece: showing how it is going to be better for them in a way they can feel and perceive. Because if they don’t feel it, then it’s just another hoop to jump through, right?” - Brian (43:56)
LinkedIn: https://www.linkedin.com/in/vijyadav/
113 פרקים
כל הפרקים
×
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
ברוכים הבאים אל Player FM!
Player FM סורק את האינטרנט עבור פודקאסטים באיכות גבוהה בשבילכם כדי שתהנו מהם כרגע. זה יישום הפודקאסט הטוב ביותר והוא עובד על אנדרואיד, iPhone ואינטרנט. הירשמו לסנכרון מנויים במכשירים שונים.