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 Referenced083 -Why Bob Goodman Thinks Product Management and Design Must Dance Together to Create “Experience Layers” for Data Products
082 - What the 2021 $1M Squirrel AI Award Winner Wants You To Know About Designing Interpretable Machine Learning Solutions w/ Cynthia Rudin
081 - The Cultural and $ Benefits of Human-Centered AI in the Enterprise: Digging Into BCG/MIT Sloan’s AI Research w/ François Candelon
080 – How to Measure the Impact of Data Products…and Anything Else with Forecasting and Measurement Expert Doug Hubbard
079 - How Sisu’s CPO, Berit Hoffmann, Is Approaching the Design of Their Analytics Product…and the UX Mistakes She Won’t Make Again
078 - From Data to Product: What is Data Product Management and Why Do We Need It with Eric Weber
077 - Productizing Analytics for Performing Arts Organizations with AMS Analytics CPO Jordan Gross Richmond
076 - How Bedrock’s “Data by Design” Mantra Helps Them Build Human-Centered Solutions with Jesús Templado
075 - How CDW is Integrating Design Into Its Data Science and Analytics Teams with Prasad Vadlamani
074 - Why a Former Microsoft ML/AI Researcher Turned to Design to Create Intelligent Products from Messy Data with Abhay Agarwal, Founder of Polytopal
073 - Addressing the Functional and Emotional Needs of Users When Designing Data Products with Param Venkataraman
071 - The ROI of UX Research and How It Applies to Data Products with Bill Albert
070 - Fighting Fire with ML, the AI Incident Database, and Why Design Matters in AI-Driven Software with Sean McGregor
069 - The Role of Creativity and Product Thinking in Data Monetization with ‘Infonomics’ Author Doug Laney
068 - Why User Adoption of Enterprise Data Products Continues to Lag with International Institute for Analytics Executive VP Drew Smith
067 - Why Roche Diagnostics’ BI and Data Science Teams Are Adopting Human-Centered Design and UX featuring Omar Khawaja
066 - How Alison Magyari Used Design Thinking to Transform Eaton’s Business Analytics Approach to Creating Data Products
065 - Balancing Human Intuition and Machine Intelligence with Salesforce Director of Product Management Pavan Tumu
064 - How AI Shapes the Products of Startups in MIT’s “Tough Tech” Venture Fund, The Engine feat. General Partner, Reed Sturtevant
Create your
podcast in
minutes
It is Free
Insight Story: Tech Trends Unpacked
Zero-Shot
Fast Forward by Tomorrow Unlocked: Tech past, tech future
The Unbelivable Truth - Series 1 - 26 including specials and pilot
Acquired