Experiencing Data w/ Brian T. O’Neill

Nancy Hensley is the Chief Digital Officer for IBM Analytics, a multi-billion dollar IBM software business focused on helping customers transform their companies with data science and analytics. Nancy has over 20 years of experience working in the data business in many facets from development, product management, sales, and marketing.

Today’s episode is probably going to appeal to those of you in product management or working on SAAS/cloud analytics tools. It is a bit different than our previous episodes in that we focused a lot on what “big blue” is doing to simplify its analytics suite as well as facilitating access to those tools. IBM has many different analytics-related products and they rely on good design to make sure that there is a consistent feel and experience across the suite, whether it’s Watson, statistics, or modeling tools. She also talked about how central user experience is to making IBM’s tools more cloud-like (try/buy online) vs. forcing customers to go through a traditional enterprise salesperson.

If you’ve got a “dated” analytics product or service that is hard to use or feels very “enterprisey” (in that not-so-good way), then I think you’ll enjoy the “modernization” theme of this episode. We covered:

• How Nancy is taking a 50-year old product such as SPSS and making it relevant and accessible for an audience that is 60% under 25 years of age

Nancy Hensley on Twitter

Nancy Hensley on LinkedIn

“It’s really never about whether it’s a great product. It’s about whether the client thinks it’s great when they start using it.” –Nancy

“Every time we add to the tool, we’re effectively reducing the simplicity of everything else around it.”–Brian

“The design part of it for us is so eye-opening, because again, we’ve built a lot of best in class enterprise products for years and as we shift into this digital go-to-market, it is all about the experience…”–Nancy

“Filling in that “why” piece is really important if you’re going to start changing design because you may not really understand the reasons someone’s abandoning.”–Brian

“Because a lot of our products weren’t born in the cloud originally, they weren’t born to be digitally originally, doesn’t mean they can’t be digitally consumed. We just have to really focus on the experience and one of those things is onboarding.” –Nancy

“If they [users] can’t figure out how to jump in and use the product, we’re not nailing it. It doesn’t matter how great the product is, if they can’t figure out how to effectively interact with it. –Nancy

Dr. Puneet Batra is the Associate Director of Machine Learning at the Broad Institute, where his team builds machine learning algorithms and pipelines to help discover new biological insights and impact disease. Puneet has spent his career stitching together data-driven solutions: in location data analysis, as cofounder of LevelTrigger; in health care, as Chief Data Scientist at Kyruus; as lead Analytic Scientist at Aster Data (Acq by Teradata); and in fundamental models of particle physics, developing theories for Fermilab’s Tevatron and CERN’s Large Hadron Collider. He has held research positions at Harvard, Stanford and Columbia Universities. Puneet completed his BA at Harvard University and has a Ph.D. from Stanford University.

A friend of mine introduced me to Puneet because he was kicking off a side project using machine learning to dig into what creativity is through the lens of jazz. Since Puneet is not a musician by training, he was looking for some domain-specific knowledge to inform his experiment, and I really liked his design-oriented thinking. While jazz kicks off our conversation, we went a lot more deeply into the contemporary role of the data scientist in this episode including:

• The discussions that need to happen between users, stakeholders, and subject matter experts so teams get a clear image of the problem that actually needs to be solved.

Puneet Batra on Twitter

Puneet Batra on LinkedIn

"Sometimes, accuracy isn't the most important thing you should be optimizing for; it’s the rest of the package…if you can make that a good process, then I think you're more on the road to making users happy [vs.] trying to live in this idealized world where you never make a mistake at all."

"The question you think you're answering from the beginning probably isn't the one that you're going to stay answering the entire time and you've just got to be flexible around that."

"Even data scientists and engineers should be able to listen with empathy and ask questions. I got a good number of tips from people teaching me how to do things like that. Basically, we ask a question or basically shut up and hear what their answer is."

"I'm not really sure what creativity is. I'm not really sure if machines will ever be creative. A good experiment to try to prove that out is to try to get a machine to be as creative as possible and see where it falls flat."

Jim Psota is the Co-Founder and CTO of Panjiva, which was named one of the top 10 Most Innovative Data Science Companies in the World by Fast Company in 2018. Panjiva has mapped the global supply chain using a combination of over 1B shipping transactions and machine learning, and recently the company was acquired by S&P Global.

Jim has spoken about artificial intelligence and entrepreneurship at Harvard Business School, MIT, and The White House, and at numerous academic and industry conferences. He also serves on the World Economic Forum’s Working Group for Artificial Intelligence and has done Ph.D. research in computer science at MIT. Some of the topics we discuss in today’s episode include:

• What Jim learned from starting Panjiva from a data-first approach

Jim Psota

Jim Psota on Twitter

Panjiva

“When you’re dealing with essentially resolving 1.5 billion records, you could think of that you need to compute 1.5 billion squared pairs of potential similarities.”

“It’s much more fulfilling to be building for a person or a set of people that you’ve actually talked to… The engineers are going to develop a much better product and frankly be much happier having that connection to the user.”

“We have crossed a pretty cool threshold where a lot of value can be created now because we have this nice combination of data availability, strong algorithms, and compute power.”

“In our case and many other company’s cases, taking third-party data, no matter where you’re getting your data, there’s going to be issues with it, there’s going to be delays, format changes, granularity differences.”

“As much as possible, we try to use the tools of data science to actually correct the data deficiency or impute or whatever technique is actually going to be better than nothing, but then say this was imputed or this is reported versus imputed…then over time, the user starts to understand if it’s gray italics [the data] was imputed, and if it’s black regular text, that’s reported data, for example.”

We’re back with a special music-related analytics episode! Following Next Big Sound’s acquisition by Pandora, Julien Benatar moved from engineering into product management and is now responsible for the company’s analytics applications in the Creator Tools division. He and his team of engineers, data scientists and designers provide insights on how artists are performing on Pandora and how they can effectively grow their audience. This was a particularly fun interview for me since I have music playing on Pandora and occasionally use Next Big Sound’s analytics myself. Julien and I discussed:

• How Julien’s team accounts for designing for a huge range of customers (artists) that have wildly different popularity, song plays, and followers

Julien Benatar on Twitter

Next Big Sound website

Next Big Sound blog

The Data Pyramid model

"I really hope we get to a point where people don’t need to be data analysts to look at data."

"People don’t just want to look at numbers anymore, they want to be able to use numbers to make decisions."

"One of our goals was to basically check every artist in the world and give them access to these tools and by checking millions of artists, it allows us to do some very good and very specific benchmarks"

“The way it works is you can thumb up or thumb down songs. If you thumb up a song, you’re giving us a signal that this is something that you like and something you want to listen to more. That’s data that we give back to artists.”

“I think the great thing today is that, compared to when Next Big Sound started in 2009, we don’t need to make a point for people to care about data. Everyone cares about data today.”

Jason Krantz is the Director of Business Analytics & Insights for the 135-year old company, Weil McLain and Marley Engineered Products. While the company is responsible for helping keeping homes and businesses warm, Jason is responsible for the creation and growth of analytical capabilities at Weil McLain, and was recognized in 2017 as a “Top 40 Under 40” in the HVAC industry. I’m not surprised given his posts on LinkedIn; Jason seems very focused on satisfying his internal customers and ensuring that there is practical  business value anchoring their analytics initiatives. We talked about:

• How Jason’s team keeps their data accessible and relevant to the issue they need to solve for their customer.

Jason Krantz on LinkedIn

“You realize that small quick wins are very effective because, at its core, it’s really important to get executive buy-in.”

“I’m a huge fan of simplicity. As analytics pros, we could very easily make very complex, very intricate models, and just, ‘Oh, look at how smart we are.’ It doesn’t help our customers. …we only use about two or three different visual types and we use mostly the exact same visual set-up. I can train a sales rep for probably five minutes on all of our reporting because if you understand one, you’re going to understand everything. That gets to the theme again of just simplicity. Don’t over complicate, keep it simple, keep it clean.”

“…To get buy-in, you really got to have your business case, even to your internal customers, really dialed in. If you just bring them a bunch of crap, that’s how you’re going to lose credibility. They’re going to be like, “I don’t have the time to waste with you,” even though we’re trying to be helpful.”

“What my team and I do is we really help companies weaponize their data assets.”