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 Referenced123 - Learnings From the CDOIQ Symposium and How Data Product Definitions are Evolving with Brian T. O’Neill
122 - Listener Questions Answered: Conducting Effective Discovery for Data Products with Brian T. O’Neill
121 - How Sainsbury’s Head of Data Products for Analytics and ML Designs for User Adoption with Peter Everill
120 - The Portfolio Mindset: Data Product Management and Design with Nadiem von Heydebrand (Part 2)
119 - Skills vs. Roles: Data Product Management and Design with Nadiem von Heydebrand (Part 1)
118 - Attracting Talent and Landing a Role in Data Product Management with Kyle Winterbottom
117 - Phil Harvey, Co-Author of “Data: A Guide to Humans,” on the Non-Technical Skills Needed to Produce Valuable AI Solutions
116 - 10 Reasons Your Customers Don’t Make Time for Your Data Product Initiatives + A Big Update on the Data Product Leadership Community (DPLC)
115 - Applying a Product and UX-Driven Approach to Building Stuart’s Data Platform with Osian Jones
114 - Designing Anti-Biasing and Explainability Tools for Data Scientists Creating ML Models with Josh Noble
113 - Turning the Weather into an Indispensable Data Product for Businesses with Cole Swain, VP Product at tomorrow.io
112 - Solving for Common Pitfalls When Developing a Data Strategy featuring Samir Sharma, CEO of datazuum
111 - Designing and Monetizing Data Products Like a Startup with Yuval Gonczarowski
110 - CDO Spotlight: The Value and Journey of Implementing a Data Product Mindset with Sebastian Klapdor of Vista
109 - The Role of Product Management and Design in Turning ML/AI into a Valuable Business with Bob Mason from Argon Ventures
108 - Google Cloud’s Bruno Aziza on What Makes a Good Customer-Obsessed Data Product Manager
107 - Tom Davenport on Data Product Management and the Impact of a Product Orientation on Enterprise Data Science and ML Initiatives
106 - Ideaflow: Applying the Practice of Design and Innovation to Internal Data Products w/ Jeremy Utley
105 - Defining “Data Product” the Producty Way and the Non-technical Skills ML/AI Product Managers Need
104 - Surfacing the Unarticulated Needs of Users and Stakeholders through Effective Listening
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