Experiencing Data w/ Brian T. O’Neill

David Stephenson, Ph.D., is the author of Big Data Demystified, a guide for executives that explores the transformative nature of big data and data analytics. He’s also a data strategy consultant and professor at the University of Amsterdam. In a previous life, David worked in various data science roles at companies like Adidas, Coolblue, and eBay.

Join David and I as we discuss what makes data science projects succeed and explore:

• The non-technical issues that lead to ineffective data science and analytics projects

DSI Analytics Website

Connect with David on LinkedIn

David’s book: Big Data Demystified 

On Twitter: @Stephenson_Data

“You see a lot of solutions being developed very well, which were not designed to meet the actual challenge that the industry is facing.” — David

“You just have that whole wasted effort because there wasn’t enough communication at inception.” — David

“I think that companies are really embracing agile, especially in the last few years. They’re really recognizing the value of it from a software perspective. But it’s really challenging from the analytics perspective—partly because the data science and analytics. They don’t fit into the scrum model very well for a variety of reasons.” — David

“That for me was a real learning point—to understand the hardest thing is not necessarily the most important thing.” — David

“If you’re working with marketing people, an 80% solution is fine. If you’re working with finance, they really need exact numbers. You have to understand what your target audience needs in terms of precision.” — David

“I feel sometimes that when we talk about “the business” people don’t understand that the business is a collection of people—just like a government is a collection of real humans doing jobs and they have goals and needs and selfish interests. So there’s really a collection of end customers and the person that’s  paying for the solution.” — Brian

“I think it’s always important—whether you’re a consultant or you’re internal—to really understand who’s going to be evaluating the value creation.”— Brian

“You’ve got to keep those lines of communication open and make sure they’re seeing the work you’re doing and evaluating and giving feedback on it. Throw this over the wall is a very high risk model.” — Brian

Dr. Murray Cantor has a storied career that spans decades. Recently, he founded Aptage, a company that provides project risk management tools using Bayesian Estimation and machine learning. He’s also the chief scientist at Hail Sports, which focuses on applying precision medicine techniques to sports performance. In his spare time, he’s a consulting mathematician at Pattern Computer, a firm that engineers state-of-the-art pattern recognition solutions for industrial customers.

Join Murray and I as we explore the cutting edge of AI and cover:

• Murray’s approach to automating processes that humans typically do, the role humans have in the design phase, and how he thinks about designing affordances for human intervention in automated systems

Murray Cantor on LinkedIn

New York Times Expose article on the Boeing 737 Max

New Your Times Article on the 737 Max whistleblower

“We’re in that stage of this industrial revolution we’re going through with augmenting people’s ability with machine learning. Right now it’s more of a craft than a science. We have people out there who are really good at working with these techniques and algorithms. But they don’t necessarily understand they’re essentially a solution looking for a problem.” — Murray

“A lot of design principles are the same whether or not you have AI. AI just raises the stakes.” — Murray

“The big companies right now are jumping the guns and saying they have explainable AI when they don’t. It’s going to take a while to really get there.” — Murray

“Sometimes, it’s not always understood by non-designers, but you’re not testing the people. You’re actually testing the system. In fact, sometimes they tell you to avoid using the word test when you’re talking to a participant, and you tell them it’s a study to evaluate a piece of software, or in this case a cockpit,  to figure out if it’s the right design or not. It’s so that they don’t feel like they’re a rat in the maze. In reality, we’re studying the maze.” — Brian

“Really fundamental to understanding user experience and design is to ask the question, who is the population of people who are going to use this and what is their range of capability?” – Murray

“Take the implementation hats off and come up with a moonshot vision. From the moonshot, you might find out there are these little tangents that are actually feasible increments. If you never let yourself dream big, you’ll never hit the small incremental steps that you may be able to take.” — Brian

Scott Friesen’s transformation into a data analytics professional wasn’t exactly linear. After graduating with a biology degree and becoming a pre-med student, he switched gears and managed artists in the music industry. After that, he worked at Best Buy, eventually becoming their Senior Director of Analytics for the company’s consumer insights unit. Today, Scott is the SVP of Strategic Analytics at Echo Global Logistics, a provider of technology-enabled transportation and supply chain management services. He also advises for the International Institute for Analytics.

In this episode, Scott shares what he thinks data scientists and analytics leaders need to do to become a trustworthy and indispensable part of an organization. Scott and I both believe that designing good decision support applications and creating useful data science solutions involve a lot more than technical knowledge. We cover:

• Scott’s trust equation, why it’s critical for analytics professionals, and how he uses it to push transformation across the organization

Scott Friesen on LinkedIn

“You might think  it is more like classical music, but truly great analytics are more like jazz. ” — Scott

“If I'm going to introduce change to an organization, then I'm going to introduce perceived risk. And so the way for me to drive positive change—the way for me to drive adding value to the organizations that I'm a part of—is the ability to create enough credibility and intimacy that I can get away with introducing change that benefits the organization.” — Scott

“I categorize the analytic pursuit into three fundamental activities: The first is to observe, the second is to relate, and the third is to predict. ” — Scott

“It's not enough to just understand the technology part and how to create great models. You can get all that stuff right and still fail in the last mile to deliver value.” — Brian

“I tend to think of this is terms of what you called ‘intimacy.’ I don’t know if you equate that to empathy, which is really understanding the thing you are talking about from the perspective of the other person. When we do UX research, the questions themselves are what form this intimacy. An easy way to do that is by asking open-ended questions that require open-ended answers to get that person to open up to you. ” — Brian

John Purcell has more than 20 years of experience in the technology world. Currently, he’s VP of Products at CloudHealth, a company that helps organizations manage their increasingly complex cloud infrastructure effectively. Prior to this role, he held the same position at SmartBear Software, makers of application performance monitoring solutions. He’s also worn several hats at companies like LogMeIn and Red Bend Software.

In today’s episode, John and I discuss how companies are moving more and more workloads to the cloud and how John and his team at CloudHealth builds a platform that makes it easy for all users—even non-technical ones—to analyze and manage data in the cloud and control their financial spending. In addition to exploring the overall complexity of using analytics to inform the management of cloud environments, we also covered:

• How CloudHealth designs for multiple personas from the financial analyst to the DevOps operator when building solutions into the product

Twitter: @PurcellOutdoors

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“I think that’s probably where the biggest point of complexity and the biggest point of challenge is for us: trying to make sure that the platform is flexible enough to be able to inject datasets we’ve never seen before and to be able to analyze and find correlations between unknown datasets that we may not have a high degree of familiarity with—so that we can generate insight that’s actionable, but deliver it in a way that’s [easy for anyone to understand].” — John

“My core philosophy is that you need UX at the table early and at every step along the way as you’re contemplating product delivery, and I mean all the way upstream at the strategic phase, at the identification of what you want to go tackle next including product strategy, pain identification, persona awareness, and who are we building for—all the way through solving the problem, what should the product be capable of, and user validation.” — John

“in the cloud, we’re just at the very early stages of [automation based on analytics] from a pure DevOps point of view. We’re still in the world of show me your math. Show me why this is the recommendation you’re making.” — John

“When making decisions using data, some IT people don’t like the system taking action without them being involved because they don’t trust that any product would be smart enough to make all the right decisions, and they don’t want applications going down.” — Brian

“I think the distinction you made between what I would call user interface design, which is the surface layer, buttons, fonts, colors, all that stuff often gets conflated in the world of analytics as being, quote ‘design.’ And as I think our audience is hearing from John here, is that it [design] goes much beyond that. It can get into something like, ‘how do you design a great experience around API documentation? Where’s the demo code? How do I run the demo?’ All of that can definitely be designed as well.” — Brian

“I hear frequently in my conversations with clients and people in the industry that there are a lot of data scientists who just want to use the latest models, and they want to work on model quality and predictive accurateness, etc. But they’re not thinking about how someone is going to use this model to make a decision, and whether will there be some business value created at the end.” — Brian

Ahmer Inam considers himself an evangelist of data science who’s been “doing data science since before it was called data science. With more than 20 years of leadership experience in the data science and analytics field at companies including Nike and Cambia health, Ahmer knows a thing or two about what makes data science projects succeed—and what makes them fail.

In today’s episode, Ahmer and I discuss his experiences using design thinking and his “human-centered AI” process to ensure that internal analytics and data science initiatives actually produce usable, useful outputs that turn into business value. Much of this was formed while Ahmer was a Senior Director and Head of Advanced Analytics at Nike, a company that is known as a design-mature organization. We covered:

• The role of empathy in data science teams and how it helps data people connect with non-technical users who may not welcome “yet another IT tool”

How Analytics Are Informing Change At Nike

LinkedIn

“Build data products with the people, for the people…and bring a sense of vulnerability to the table.” — Ahmer

“What I have seen is that a lot of times we can build models, we can bring the best of the technologies on optimal technology it’s in the platforms, but in the end, if the business process and the people are not ready to take it and use it, that’s where it fails.” — Ahmer

“If we don’t understand people in the process, essentially, the adoption is not going to work. In the end, when it comes to a lot of these data science exercises or projects or development of data products, we have to really think about it as a change management exercise and nothing short of that.” — Ahmer

“Putting humans at the center of these initiatives drives better value and it actually makes sure that these tools and data products that we’re making actually get used, which is what ultimately is going to determine whether or not there’s any business value—because the data itself doesn’t have any value until it’s acted upon.” — Brian

“One of these that’s been stuck in my ear like an earworm is that a lot of the models fail to get to production still. And so this is the ongoing theme of basically large analytics projects, whether you call it big data analytics or AI, it’s the same thing. We’re throwing a lot of money at these problems, and we’re still creating poor solutions that end up not doing anything.” — Brian

“I think the really important point here is that early on with these initiatives, it’s important to figure out, What is going to stop this person from potentially engaging with my service?” — Brian