How do you extract the real, unarticulated needs from a stakeholder or user who comes to you asking for AI, a specific app feature, or a dashboard?
On this episode of Experiencing Data, Cindy Dishmey Montgomery, Head of Data Strategy for Global Real Assets at Morgan Stanley, was gracious enough to let me put her on the spot and simulate a conversation between a data product leader and customer.
I played the customer, and she did a great job helping me think differently about what I was asking her to produce for me — so that I would be getting an outcome in the end, and not just an output. We didn’t practice or plan this exercise, it just happened — and she handled it like a pro! I wasn’t surprised; her product and user-first approach told me that she had a lot to share with you, and indeed she did!
A computer scientist by training, Cindy has worked in data, analytics and BI roles at other major companies, such as Revantage, a Blackstone real estate portfolio company, and Goldman Sachs. Cindy was also named one of the 2021 Notable Women on Wall Street by Crain’s New York Business.
Cindy and I also talked about the “T” framework she uses to achieve high-level business goals, as well as the importance for data teams to build trust with end-users.
In our chat, we covered:
“There’s just so many good constructs in the product management world that we have not yet really brought very close to the data world. We tend to start with the skill sets, and the tools, and the ML/AI … all the buzzwords. [...]But brass tacks: when you have a happy set of consumers of your data products, you’re creating real value.” - Cindy Dishmey Montgomery (1:55)
“The path to value lies through adoption and adoption lies through giving people something that actually helps them do their work, which means you need to understand what the problem space is, and that may not be written down anywhere because they’re voicing the need as a solution.” - Brian O’Neill (@rhythmspice) (4:07)
“I think our data community tends to over-promise and under-deliver as a way to get the interest, which it’s actually quite successful when you have this notion of, ‘If you build AI, profit will come.’ But that is a really, really hard promise to make and keep.” - Cindy Dishmey Montgomery (12:14)
“[Creating a data product for a stakeholder is] definitely something where you have to be close to the business problem and design it together. … The struggle is making sure organizations know when the right time and what the right first hire is to start that process.” - Cindy Dishmey Montgomery (23:58)
“The temporal aspect of design is something that’s often missing. We talk a lot about the artifacts: the Excel sheet, the dashboard, the thing, and not always about when the thing is used.” - Brian O’Neill (@rhythmspice) (27:27)
“Everyone should understand product. And even just creating the language of product is very helpful in creating a center of gravity for everyone. It’s where we invest time, it’s how it’s meant to connect to a certain piece of value in the business strategy. It’s a really great forcing mechanism to create an environment where everyone thinks in terms of value. And the thing that helps us get to value, that’s the data product.” - Cindy Dishmey Montgomery (34:22)
Links Referenced103 - Helping Pediatric Cardiac Surgeons Make Better Decisions with ML featuring Eugenio Zuccarelli of MIT Media Lab
102 - CDO Spotlight: The Non-Technical Roles Data Science and Analytics Teams Need to Drive Adoption of Data Products w/ Iván Herrero Bartolomé
101 - Insights on Framing IOT Solutions as Data Products and Lessons Learned from Katy Pusch
100 - Why Your Data, AI, Product & Business Strategies Must Work Together (and Digital Transformation is The Wrong Framing) with Vin Vashishta
099 - Don’t Boil the Ocean: How to Generate Business Value Early With Your Data Products with Jon Cooke, CTO of Dataception
098 - Why Emilie Schario Wants You to Run Your Data Team Like a Product Team
097 - Why Regions Bank’s CDAO, Manav Misra, Implemented a Product-Oriented Approach to Designing Data Products
096 - Why Chad Sanderson, Head of Product for Convoy’s Data Platform, is a Champion of Data UX
095 - Increasing Adoption of Data Products Through Design Training: My Interview from TDWI Munich
094 - The Multi-Million Dollar Impact of Data Product Management and UX with Vijay Yadav of Merck
093 - Why Agile Alone Won’t Increase Adoption of Your Enterprise Data Products
092 - How to measure data product value from a UX and business lens (and how not to do it)
091 - How Brazil’s Biggest Fiber Company, Oi, Leverages Design To Create Useful Data Products with Sr. Exec. Design Manager, João Critis
090 - Michelle Carney’s Mission With MLUX: Bringing UX and Machine Learning Together
089 - Reader Questions Answered about Dashboard UX Design
088 - Doing UX Research for Data Products and The Magic of Qualitative User Feedback with Mike Oren, Head of Design Research at Klaviyo
087 - How Data Product Management and UX Integrate with Data Scientists at Albertsons Companies to Improve the Grocery Shopping Experience
086 - CED: My UX Framework for Designing Analytics Tools That Drive Decision Making
085 - Dr. William D. Báez on the Journey and ROI of Integrating UX Design into Machine Learning and Analytics Solutions
084 - The Messy Truth of Designing and Building a Successful Analytics SAAS Product featuring Jonathan Kay (CEO, Apptopia)
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