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 Referenced024 - How Empathy Can Reveal a 60%-Accurate Data Science Solution is a Solid Customer Win with David Stephenson, Ph.D.
023 - Balancing AI-Driven Automation with Human Intervention When Designing Complex Systems with Dr. Murray Cantor
022 - Creating a Trusted Data Science Team That Is Indispensable to the Business with
021 - Turning Complex Cloud IT Data Into Useful Decision Support Info with John Purcell of
020 - How Human-Centered Design Increases Engagement with Data Science Initiatives
019 - The Non-Technical (Human!) Challenges that Can Impede Great Data Science Solutions
018 - The Business Value of Showing the “Why” in AI Models with Jana Eggers (CEO, Naralogics)
017 - John Cutler on Productizing Storytelling Measuring What Matters & Analytics Product Management
016 - Farming with Data: How Advanced Analytics are Transforming the Agriculture Industry with Dinu Ajikutra
015 – Opportunities and Challenges When Designing IoT Analytics Experiences for the Industrial & Manufacturing Industries with CEO Bill Bither
014 - How Worthington Industries Makes Predictive Analytics Useful from the Steel Mill Floor to the Corner Office with Dr. Stephen Bartos
013 - Paul Mattal (Dir. of Network Systems, Akamai) on designing decision support tools and analytics services for the largest CDN on the web
012 - Dr. Andrey Sharapov (Data Scientist, Lidl) on explainable AI and demystifying predictions from machine learning models for better user experienc...
011 - Gadi Oren (VP Product, LogicMonitor) on analytics for monitoring applications and looking at declarative analytics as “opinions”
010 - Carl Hoffman (CEO, Basis Technology) on text analytics, NLP, entity resolution, and why exact match search is stupid
009 - Nancy Hensley (Chief Digital Officer, IBM Analytics) on the role of design and UX in modernizing analytics tools as old as 50 years
008 - Dr. Puneet Batra (Assoc. Director, Machine Learning at Broad Institute of MIT and Harvard) on aligning data science with biz objectives, user re...
007 -Jim Psota (CTO & Co-Founder, Panjiva/S&P Global) on designing a meaningful SAAS analytics product for the global supply chain
006 - Julien Benatar (PM for Pandora's data service, Next Big Sound) on analytics for musicians, record labels and performing artists
005 - Jason Krantz (Dir. of Biz Analytics/Insights, Weil-McClain) on centering analytics around internal customers
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