Experiencing Data w/ Brian T. O’Neill

Dr. Bob Hayes, will be the first to tell you that he’s a dataphile. Ever since he took a stats course in college in the 80s, Bob’s been hooked on data. Currently, Bob is the Research and Analytics Director at Indigo Slate. He’s also the president of Business over Broadway, a consultancy he founded in 2007.In a past life, Bob served as Chief Research Officer at Appuri and AnalyticsWeek, Chief Customer Officer at TCELab, and a contributing analyst at Gleanster, among many other roles.

In today’s episode, Bob and I discuss a recent Kaggle survey that highlighted several key non-technical impediments to effective data science projects. In addition to outlining what those challenges are and exploring potential solutions to them, we also covered:

• The three key skills successful data science teams have

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IndigoSlate

“I’ve always loved data. I took my first stats course in college over 30 years ago and I was hooked immediately. I love data. Sometimes I introduce myself as a dataholic. I love it.” — Bob

“I’m a big fan of just kind of analyzing data, just getting my hands on data, just exploring it. But that can lead you down a path of no return where you’re just analyzing data just to analyze it. What I try to tell my clients is that when you approach a data set, have a problem that you’re trying to solve. The challenge there I think it stems from the fact that a lot of data science teams don’t have a subject matter expert on the team to pose the right questions.” — Bob

“The three findings that I found pretty interesting were, number one, a lack of a clear question to be answering or a clear direction to go in with the available data. The second one was that data science results were not used by the business decision makers. And the third one was an inability to integrate findings into the organization’s decision making processes.” — Brian

“It makes you wonder,‘if you didn’t have a good problem to solve, maybe that’s why [the findings] didn’t get used in the first place.’” — Brian

“That part isn’t so much the math and the science. That’s more the psychology and knowing how people react. Because you’re going to have certain business stakeholders that still want to kind of shoot from the hip and their experience. Their gut tells them something. And sometimes that gut is really informed.” — Brian

“If executives are looking at data science and AI as a strategic initiative, it seems really funny to me that someone wouldn’t be saying, ‘What do we get out of this? What are the next steps?’ when the data teams get to the end of a project and just moves on to the next one.” — Brian

Jana Eggers, a self-proclaimed math and computer nerd, is CEO of Nara Logics, a company that helps organizations use AI to eliminate data silos and unlock the full value of their data, delivering predictive personalized experiences to their customers along the way. The company leverages the latest neuroscience research to model data the same way our brains do. Jana also serves on Fannie Mae’s digital advisory board, which is tasked with finding affordable housing solutions across the United States. Prior to joining Nara Logics, Jana wore many different hats, serving as CEO of Spreadshirt, and General Manager of QuickBase at Intuit, among other positions. She also knows about good restaurants in PDX!

In today’s episode, Jana and I explore her approaches to using AI to help enterprises make interesting and useful predictions that drive better business outcomes and improve customer experience. In addition to discussing how AI can help strengthen personalization and support smarter decision making, we also covered:

• The power of showing the whys when providing predictions (i.e., explainable AI or XAI).

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“We have a platform that is really built for decision support. How do you go from having […]20 to having about 500 to 2,000 decision factors coming in? Once we get that overload of information, our tool is used to help people with those decisions. And yes, we’re using a different approach than the traditional neural net, which is what deep learning is based on. While we use that in our tool, we’re more on the cognitive side. […]I’ve got a lot of different signals coming in, how do I understand how those signals relate to each other and then make decisions based on that?” — Jana

“One of the things that we do that also stands us apart is that our AI is transparent—meaning that when we provide an answer, we also give the reasons why that is the right answer for this context. We think it is important to know what was taken into account and what factors weigh more heavily in this context than other contexts.” — Jana

“It is extremely unusual—and I can even say that I’ve never really seen it—that people just say, Okay, I trust the machine. I’m comfortable with that. It knows more than me. That’s really unusual. The only time I’ve seen that is when you’re really doing something new and no one there has any idea what it should be.” — Jana

“With regards to tech answering “why,” I’ve worked on several monitoring and analytics applications in the IT space. When doing root cause analysis, we came up with this idea of referring to monitored objects as being abnormally critical and normally critical. Because at certain times of day, you might be running a backup job and so the IO is going crazy, and maybe the latency is higher. But the IO is supposed to be that way at that time. So how do you knock down that signal and not throw up all the red flags and light up the dashboard when it’s supposed to be operating that way? Answering “why” is difficult. ” — Brian

“We’ve got lipstick, we’ve got kissing. I’m going to get flagged as ‘parental advisory’ on this episode in iTunes probably. ;-)” — Brian

“You can’t just live in the closet and do your math and hope that everyone is going to see the value of it. Anytime we’re building these complex tools and services —what I call human-in-the-loop applications–you’re probably going to have to go engage with other humans, whether it’s customers or your teammates or whatever.” — Brian

John Cutler is a Product Evangelist for Amplitude, an analytic platform that helps companies better understand users behavior, helping to grow their businesses. John focuses on user experience and evidence-driven product development by mixing and matching various methodologies to help teams deliver lasting outcomes for their customers. As a former UX researcher at AppFolio, a product manager at Zendesk, Pendo.io, AdKeeper and RichFX, a startup founder, and a product team coach, John has a perspective that spans individual roles, domains, and products.

In today’s episode, John and I discuss how productizing storytelling in analytics applications can be a powerful tool for moving analytics beyond vanity metrics. We also covered the importance of understanding customers’ jobs/tasks, involving cross-disciplinary teams when creating a product/service, and:

• John and Amplitude’s North Star strategy and the (3) measurements they care about when tracking their own customers’ success

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Amplitude

Designing for Analytics

“It’s like you know in your heart you should pair with domain experts and people who know the human problem out there and understand the decisions being made. I think organizationally, there’s a lot of organizational inertia that discourages that, unfortunately, and so you need to fight for it. My advice is to fight for it because you know that that’s important and you know that this is not just a pure data science problem or a pure analytics problem. There’s probably there’s a lot of surrounding information that you need to understand to be able to actually help the business.” – John

“We definitely ‘dogfood’ our product and we also ‘dogfood’ the advice we give our customers.” – John

“You know in your heart you should pair with domain experts and people who know the human problem out there and understand the decisions being made. […] there’s a lot of organizational inertia that discourages that, unfortunately, and so you need to fight for it. I guess my advice is, fight for it, because you know that it is important, and you know that this is not just a pure data science problem or a pure analytics problem.” – John

“It’s very easy to create assets and create code and things that look like progress. They mask themselves as progress and improvement, and they may not actually return any business value or customer value explicitly. We have to consciously know what the outcomes are that we want.” – Brian

“We got to get the right bodies in the room that know the right questions to ask. I can smell when the right questions aren’t being asked, and it’s so powerful” – Brian

“Instead of thinking about what are all the right stats to consider, [I sometimes suggest teams] write in plain English, like in prose format, what would be the value that we could possibly show in the data.’ maybe it can’t even technically be achieved today. But expressing the analytics in words like, ‘you should change this knob to seven instead of nine because we found out X, Y, and Z happened. We also think blah, blah, blah, blah, blah, and here is how we know that, and there’s your recommendation.’ This method is highly prescriptive, but it’s an exercise in thinking about the customer’s experience.” – Brian

Today we are joined by Dinu Ajikutira, VP of Product at CiBO Technologies. CiBO Technologies was founded in 2015. It was created to provide an objective, scientifically-driven insights in support of farmland economics. Dinu is currently leading an effort to productize what I found to be some very impressive analytically-driven simulation capabilities to help farmers and agronomists. Specifically, CiBO’s goal is to provide a software service that uses mapping and other data to predictively model a piece of land’s agricultural value –before crops are ever planted. In order to build a product that truly meets his customer needs, Dinu goes the extra mile–in one case, 1000 miles– to literally meet his customers in the field to understand their pain points.

On this episode, Dinu and I discuss how CiBO will help reduce farmers’ risk, optimize crop yields, and the challenges of the agriculture industry from a data standpoint. We also discussed:

• Farmers’ interactions with data analytics products and how to improve their trust with those products

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Experiencing Data Podcast

“CiBO was built on a mission of enabling sustainable agriculture, and we built this software platform that brings weather, soil, topography, and agronomic practices in combination with simulation to actually digitally grow the plant, and that allows us to explain to the users why something occurs, what if something different had happened, and predict the outcomes of how plants will perform in different environments.” — Dinu Ajikutira

“The maturity of the agricultural industry [with regards] to technology is in its early stages, and it’s at a time when there is a lot of noise around AI,machine learning and data analytics. That makes it very complicated, because you don’t know if the technology really does what it claims to do, and there is a community of potential users that are not used to using a high-tech technology to solve their problems.” — DInu Ajikutira

“In agriculture, the data is very sparse, but with our software we don’t need all the data. We can supplement data that is missing, using our simulation tools, and be able to predict weather outcomes that you have not experienced in the past.” — Dinu Ajikutira

“To add clarity, you need to add information sometimes, and the issue isn’t always the quantity of the information; it’s how it’s designed.I’ve seen this repeatedly where there are times if you properly add information and design it well, you actually bring a lot more insight.” – Brian O’Neill

“Sometimes the solution is going to be to add information, and if you’re feeling like you have a clutter problem, if your customers are complaining about too much information, or that’s a symptom usually that the design is wrong. It’s not necessarily that that data has no value. It may be the wrong data.” — Brian O’Neill

Bill Bither, CEO and Co-Founder of MachineMetrics, is a serial software entrepreneur and a manufacturing technology leader. He founded and bootstrapped Atalasoft to image-enable web applications which led to a successful exit in 2011 to Kofax. In 2014, he co-founded MachineMetrics to bring visibility and predictability to the manufacturing floor with an Industrial IoT analytics platform that collects data from machines. This data is used to benchmark performance, drive efficiency, improve equipment uptime, and enable automation.

Today, join us as we discuss the various opportunities and challenges in the complex world of industrial IoT and manufacturing. Bill and I discuss the importance of visualizations and its relationship to improving efficiency in manufacturing, how talking to machine operators help add context to analytics data and even inform UI/UX decisions, as well as how MachineMetrics goes about making the telemetry from these machines useful to the operators.

We also covered:

• How improving a customer’s visibility into CNC machines helped reveal accurate utilization rates and improved efficiency

• Descriptive

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MachineMetrics

“We have so much data, but the piece that really adds enormous value is human feedback.” — Bill

“Simplicity is really hard. It takes time because it requires empathy and it requires going in and really getting into the head or the life of the person that’s gonna use your tool. You have to understand what’s it like being on a shop floor running eight different CNC machines. If you’ve never talked to someone, it’s really hard to empathize with them.” — Brian

“In all the work that we do, in adding more intelligence to the product, it’s just making the experience simpler and simpler.” — Bill

“You don’t have to go in and do great research; you can go in and just start doing research and learn on the way. It’s like going to the gym. They always tell you, ‘It doesn’t matter what exercise you do, just go and start.’ …then you can always get better at making your workout optimal.” — Brian

“It’s really valuable to have routine visits with customers, because you just don’t know what else might be going on.” — Brian

“The real value of the research is asking ‘why’ and ‘how,’ and getting to the root problem. That’s the insight you want. Customers may have some good design ideas, but most customers aren’t designers. … Our job is to give people what they need.” — Brian