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Are you responsible for creating business impact with data products, SAAS analytics solutions, dashboards or generative AI/ML applications? Do you believe one of the biggest challenges with monetizing data products is navigating the humans in the loop—from stakeholders to users? Do you believe that a product-driven approach coupled with solid UX design is critical to ensuring that analytics and ML solutions even get used? My name is Brian T. O’Neill, and on Experiencing Data, I offer you a designer’s perspective on why simply developing ML models, dashboards, and apps—outputs—aren’t enough to drive meaningful user and business outcomes with data. Through solo episodes and interviews with data product management leaders, CDAOs, VCs, and designers, I explore how teams are integrating product-oriented methodologies and UX design to ensure that data products get used in the last mile. After all, you can’t create business value if the humans in the loop won’t use your “solution.” Whether you work in product at a B2B / SAAS analytics company, or you build internal data products for a traditional enterprise, join me as I dig into what’s working—and what isn’t. Hashtag: #ExperiencingData. PODCAST HOMEPAGE: For 1-page summaries and full text transcripts, join my Insights mailing list on the podcast homepage: https://designingforanalytics.com/ed ABOUT THE HOST, BRIAN T. O’NEILL: https://designingforanalytics.com/bio/
Episodes
Tuesday Aug 10, 2021
Tuesday Aug 10, 2021
There are many benefits in talking with end users and stakeholders about their needs and pain points before designing a data product.
Just take it from Bill Albert, executive director of the Bentley University User Experience Center, author of Measuring the User Experience, and my guest for this week’s episode of Experiencing Data. With a career spanning more than 20 years in user experience research, design, and strategy, Bill has some great insights on how UX research is pivotal to designing a useful data product, the different types of customer research, and how many users you need to talk to to get useful info.
In our chat, we covered:
- How UX research techniques can help increase adoption of data products. (1:12)
- Conducting 'upfront research': Why talking to end users and stakeholders early on is crucial to designing a more valuable data product. (8:17)
- 'A participatory design process': How data scientists should conduct research with stakeholders before and during the designing of a data product. (14:57)
- How to determine sample sizes in user experience research -- and when to use qualitative vs. quantitative techniques. (17:52)
- How end user research and design improvements helped Boston Children's Hospital drastically increase the number of recurring donations. (24:38)
- How a person's worldview and experiences can shape how they interpret data. (32:38)
- The value of collecting metrics that reflect the success and usage of a data product. (38:11)
Quotes from Today’s Episode
“Teams are constantly putting out dashboards and analytics applications — and now it’s machine learning and AI— and a whole lot of it never gets used because it hits all kinds of human walls in the deployment part.” - Brian (3:39)
“Dare to be simple. It’s important to understand giving [people exactly what they] want, and nothing more. That’s largely a reflection of organizational maturity; making those tough decisions and not throwing out every single possible feature [and] function that somebody might want at some point.” - Bill (7:50)
“As researchers, we need to more deeply understand the user needs and see what we’re not observing in the lab [and what] we can’t see through our analytics. There’s so much more out there that we can be doing to help move the experience forward and improve that in a substantial way.” - Bill (10:15)
“You need to do the upfront research; you need to talk to stakeholders and the end users as early as possible. And we’ve known about this for decades, that you will get way more value and come up with a better design, better product, the earlier you talk to people.” - Bill (13:25)
“Our research methods don’t change because what we’re trying to understand is technology-agnostic. It doesn’t matter whether it’s a toaster or a mobile phone — the questions that we’re trying to understand of how people are using this, how can we make this a better experience, those are constant.” - Bill (30:11)
“I think, what’s called model interpretability sometimes or explainable AI, I am seeing a change in the market in terms of more focus on explainability, less on model accuracy at all costs, which often likes to use advanced techniques like deep learning, which are essentially black box techniques right now. And the cost associated with black box is, ‘I don’t know how you came up with this and I’m really leery to trust it.’” - Brian (31:56)
Resources and Links:
- Bentley University User Experience Center: https://www.bentley.edu/centers/user-experience-center
- Measuring the User Experience: https://www.amazon.com/Measuring-User-Experience-Interactive-Technologies/dp/0124157815
- www.bentley.edu/uxc: https://www.bentley.edu/uxc
- LinkedIn: https://www.linkedin.com/in/walbert/
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