127.9K
Downloads
161
Episodes
If you’re a leader tasked with generating business and org. value through ML/AI and analytics, you’ve probably struggled with low user adoption. Making the tech gets easier, but getting users to use, and buyers to buy, remains difficult—but you’ve heard a ”data product” approach can help. Can it? My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I offer you a consulting designer’s perspective on why creating ML and analytics outputs isn’t enough to create business and UX outcomes. How can UX design and product management help you create innovative ML/AI and analytical data products? What exactly are data products—and how can data product management help you increase user adoption of ML/analytics—so that stakeholders can finally see the business value of your data? Every 2 weeks, I answer these questions via solo episodes and interviews with innovative chief data officers, data product management leaders, and top UX professionals. Hashtag: #ExperiencingData. PODCAST HOMEPAGE: Get 1-page summaries, text transcripts, and join my Insights mailing list: https://designingforanalytics.com/ed ABOUT THE HOST, BRIAN T. O’NEILL: https://designingforanalytics.com/bio/
Episodes
Tuesday Aug 27, 2019
Tuesday Aug 27, 2019
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”
- Ahmer’s thoughts on Lean Coaching, Scrum Teams, and getting outside help to accelerate the design and creation of your first data products and predictive models
- The role of change management in the process of moving data products into production
- Ahmer’s two-week process to kick-start data product initiatives used at Nike
- How model accuracy isn’t as important early on as other success metrics when prototyping solutions with customers
Resources and Links
How Analytics Are Informing Change At Nike
Quotes from Today’s Episode
“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
Tuesday Aug 13, 2019
Tuesday Aug 13, 2019
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
- Why improving customer loyalty involves analyzing several metrics, not just one
- Why Bob feels the scientific method is just as important today as it’s been for hundreds of years
- The importance of repeatable results
- How prototyping early can save time and drive adoption of data science projects
- Bob’s advice on how to move data science projects forward (and one key skill he feels most business leaders lack)
- The role of the analytics translator
Resources and Links:
Quotes from Today’s Episode
“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
Tuesday Jul 30, 2019
Tuesday Jul 30, 2019
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).
- Jana’s thoughts on why some data scientists struggle with inflated expectations around AI
- Brian’s #facepalm about lipstick and data
- The power of what-if simulations and being able to remove factors from predictions
- The power of context and how Nara Logics weighs recent data vs. long-term historical data in its predictions
- How Nara Logics leverages the wiring of the brain—the connectome—to inspire the models they build and the decision support help they provide to customers
- Why AI initiatives need to consider the “AI trinity”: data, the algorithm, and the results an organization is aiming for
Resources and Links:
Quotes from Today’s Episode
“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
Tuesday Jul 16, 2019
Tuesday Jul 16, 2019
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
- Why John loves the concept of analytics “notebooks” (also a particular feature of Amplitude’s product) vs. the standard dashboard method
- Understanding relationships between metrics through “weekly learning users” who share digestible content
- John’s opinions on involving domain experts and cross-discipline teams to enable products focused on outcomes over features
- Recognizing whether your product/app is about explanatory or exploratory analytics
- How Jazz relates to business – how you don’t know what you don’t know yet
Resources and Links:
Quotes from Today’s Episode
“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
Tuesday Jul 02, 2019
Tuesday Jul 02, 2019
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
- Where CiBO’s software can be used and who would benefit from it
- Dinu’s “ride-along” experience visiting farmers and agronomists in the midwest to better understand customer needs and interactions with the tool
- What Dinu has learned about farmers’ comfort using technology
- The importance of understanding seasonality
- The challenges of designing the tool for the various users and building user interfaces based on user needs
- The biggest product challenges in the ag tech field and how CiBO handles those challenges
Resources and Links:
Quotes from Today’s Episode
“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
Tuesday Jun 18, 2019
Tuesday Jun 18, 2019
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
- How simple visualizations make a tangible difference in operational performance
- Bill’s model for the 4 different phases of analytics
- Descriptive
- Diagnostic
- Predictive
- Prescriptive
- Mistakes Bill learned early on about product dev in the IIoT analytics space
- What Bill learned from talking to customers that ended up identifying a major design flaw his team wasn’t aware of
- The value you can glean from talking to customers
- MachineWorks’ challenges with finding their market fit and aligning their product around customer’s needs
- How MachineMetrics has learned to simplify the customer’s analytics experience
Resources and Links
Quotes from Today’s Episode
“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
Tuesday Jun 04, 2019
Tuesday Jun 04, 2019
Today we are joined by the analytics “man of steel,” Steve Bartos, the Manager of the Predictive Analytics team in the steel processing division at Worthington Industries. 😉 At Worthington, Steve is tasked with strategically driving impactful analytics wider and deeper across the division and, as part of this effort, helps ensure an educated, supported, and connected analytics community. In addition, Steve also serves as a co-leader of the Columbus Tableau User Group.
On today’s episode, Steve and I discuss how analytics are providing internal process improvements at Worthington. We also cover the challenges Steve faces designing effective data-rich products, the importance of the “last mile,” and how his PhD in science education shapes his work in predictive analytics.
In addition, we also talk about:
- Internal tools that Steve has developed and how they help Worthington Industries.
- Preplanning and its importance for creating a solution that works for the client.
- Using analytics to inform daily decisions, aid in monthly meetings, and assist with Kaizen (Lean) focused decisions.
- How Steve pulls out the meaningful pieces of information that can improve the division’s performance.
- How Steve tries to avoid Data-Rich and Insight-Poor customer solutions
- The importance of engaging the customer/user throughout the process
- How Steve leverages his science education background to communicate with his peers and with executives at Worthington
Resources and Links
Quotes from Today’s Episode
“Seeing the way analytics can help facilitate better decision making, doesn't necessarily come with showing someone every single question they can possibly answer, waiting for them to applaud how much time and how much energy and effort you'd saved them.” - Steve Bartos
“It's hard to talk about the influence of different machine parameters on quality if every operator is setting it up based on their own tribal knowledge of how it runs best.” - Steve Bartos
“I think bringing the question back to the user much more frequently, much sooner and at a much more focused scale has paid dividends”. - Steve Bartos
“It's getting the people that are actually going to sit and use these interfaces involved in the creation process… they should be helping you define the goals and the problems… by getting them involved, it makes the adoption process a lot easier.” - Brian O’Neill
“It's real easy to throw up some work that you've done in Tableau around a question that a manager or an executive had. It's real easy to do that. It's really difficult to do that well and have some control of the conversation, being able to say, here's what we did, here was the question, here's the day we use, here's how we analyze it and here's a suggestion where making and now let's talk about why and do that in a way that doesn't lead to an in-the-weeds session and frustration.” - Steve Bartos
Tuesday May 21, 2019
Tuesday May 21, 2019
Paul Mattal is the Director of Network Systems at Akamai, one of the largest content delivery networks in the U.S. Akamai is a major part of the backbone of the internet and on today’s episode, Paul is going to talk about the massive amount of telemetry that comes into Akamai and the various decision support tools his group is in charge of providing to internal customers. On top of the analytics aspect of our chat, we also discussed how Paul is approaching his team’s work being relatively new at Akamai.
Additionally, we covered:
- How does Paul access and use internal customer knowledge to improve the quality of applications they make?
- When to build a custom decision support tool vs. using a BI tool like Tableau?
- How does Akamai measure if their analytics are creating customer value?
- The process Paul uses with the customer to design a new data product MVP
- How Paul decides which of the many analytics applications and services “get love” when resources are constrained
- Paul’s closing advice about taking the time to design and plan before you code
Resources and Links:
Quotes from Today’s Episode
“I would say we have a lot of engagement with [customers] here. People jump to answering questions with data and they’re quick. They know how to do that and they have very good ideas about how to make sure that the approaches they take are backed by data and backed by evidence.” — Paul Mattal
“There’s actually a very mature culture here at Akamai of helping each other. Not necessarily taking on an enormous project if you don’t have the time for it, but opening your door and helping somebody solve a problem, if you have expertise that can help them.” — Paul Mattal
“I’m always curious about feedback cycles because there’s a lot of places that they start with telemetry and data, then they put technology on top of it, they build a bunch of software, and look at releases and outputs as the final part. It’s actually not. It’s the outcomes that come from the stuff we built that matter. If you don’t know what outcomes those look like, then you don’t know if you actually created anything meaningful.” — Brian O’Neill
“We’ve talked a little bit about the MVP approach, which is about doing that minimal amount of work, which may or may not be working code, but you did a minimum amount of stuff to figure out whether or not it’s meeting a need that your customer has. You’re going through some type of observation process to fuel the first thing, asset or output that you create. It’s fueled by some kind of observation or research upfront so that when you go up to bat and take a swing with something real, there’s a better chance of at least a base hit.” — Brian O’Neill
“Pretend to be the new guy for as long as you can. Go ask [about their needs/challenges] again and get to really understand what that person [customer] is experiencing, because I know you’re going to able to meet the need much better.” — Paul Mattal
Tuesday May 07, 2019
Tuesday May 07, 2019
Dr. Andrey Sharapov is a senior data scientist and machine learning engineer at Lidl. He is currently working on various projects related to machine learning and data product development including analytical planning tools that help with business issues such as stocking and purchasing. Previously, he spent 2 years at Xaxis and he led data science initiatives and developed tools for customer analytics at TeamViewer. Andrey and I met at a Predicitve Analytics World conference we were both speaking at, and I found out he is very interested in “explainable AI,” an aspect of user experience that I think is worth talking about and so that’s what today’s episode will focus on.
In our chat, we covered:
- Lidl’s planning tool for their operational teams and what it predicts.
- The lessons learned from Andrey’s first attempt to build an explainable AI tool and other human factors related to designing data products
- What explainable AI is, and why it is critical in certain situations
- How explainable AI is useful for debugging other data models
- We discuss why explainable AI isn’t always used
- Andrey’s thoughts on the importance of including your end user in the data production creation process from the very beginning.
Also, here’s a little post-episode thought from a design perspective:
I know there are counter-vailing opinions that state that explainability of models is “over-hyped.” One popular rationalization uses examples such as how certain professions (e.g. medical practitioners) make decisions all the time that cannot be fully explained, yet people believe the decision making without necessarily expecting it to be fully explained. The reality is that while not every model or end UX necessarily needs explainability, I think there are human factors that can be satisfied by providing explainability such as building customer trust more rapidly, or helping convince customers/users why/how a new technology solution may be better than “the old way” of doing things. This is not a blanket recommendation to “always include explainability” in your service/app/UI; I think many factors come into play and as with any design choice, I think you should let your customer/user feedback help you decide whether your service needs explainability to be valuable, useful, and engaging.
Resources and Links:
Explainable AI- XAI Group (LinkedIn)
Quotes from Today’s Episode
“I hear frequently there can be a tendency in the data science community to want to do excellent data science work and not necessarily do excellent business work. I also hear how some data scientists may think, ‘explainable AI is not going to improve the model’ or ‘help me get published’ – so maybe that’s responsible for why [explainable AI] is not as widely in use.” – Brian O’Neill
“When you go and talk to an operational person, who has in mind a certain number of basic rules, say three, five, or six rules [they use] when doing planning, and then when you come to him with a machine learning model, something that is let’s say, ‘black box,’ and then you tell him ‘okay, just trust my prediction,’ then in most of the cases, it just simply doesn’t work. They don’t trust it. But the moment when you come with an explanation for every single prediction your model does, you are increasing your chances of a mutual conversation between this responsible person and the model…” – Andrey Sharapov
“We actually do a lot of traveling these days, going to Bulgaria, going to Poland, Hungry, every country, we try to talk to these people [our users] directly. [We] try to get the requirements directly from them and then show the results back to them…” – Andrey Sharapov
“The sole purpose of the tool we built was to make their work more efficient, in a sense that they could not only produce better results in terms of accuracy, but they could also learn about the market themselves because we created a plot for elasticity curves. They could play with the price and see if they made the price too high, too low, and how much the order quantity would change.” – Andrey Sharapov
Tuesday Apr 23, 2019
Tuesday Apr 23, 2019
My guest today is Gadi Oren, the VP of Product for LogicMonitor. Gadi is responsible for the company’s strategic vision and product initiatives. Previously, Gadi was the CEO and Co-Founder of ITculate, where he was responsible for developing world-class technology and product that created contextual monitoring by discovering and leveraging application topology. Gadi previously served as the CTO and Co-founder of Cloudscope and he has a management degree from Sloan MIT.
Today we are going to talk with Gadi about analytics in the context of monitoring applications. This was a fun chat as Gadi and I have both worked on several applications in this space, and it was great to hear how Gadi is habitually integrating customers into his product development process. You’re also going to hear Gadi’s interesting way of framing declarative analytics as casting “opinions,” which I thought was really interesting from a UX standpoint. We also discussed:
- How to define what is “normal” for an environment being monitored and when to be concerned about variations.
- Gadi’s KPI for his team regarding customer interaction and why it is important.
- What kind of data is needed for effective prototypes
- How to approach design/prototyping for new vs. existing products
- Mistakes that product owners make falling in love with early prototypes
- Interpreting common customer signals that may identify a latent problem needing to be solved in the application
Resources and Links:
LogicMonitor
Twitter: @gadioren
LinkedIn: Gadi Oren
Quotes from Today’s Episode
“The barrier of replacing software goes down. Bad software will go out and better software will come in. If it’s easier to use, you will actually win in the marketplace because of that. It’s not a secondary aspect.” – Gadi Oren
“…ultimately, [not talking to customers] is going to take you away from understanding what’s going on and you’ll be operating on interpolating from information you know instead of listening to the customer.” – Gadi Oren
“Providing the data or the evidence for the conclusion is a way not to black box everything. You’re providing the human with the relevant analysis and evidence that went into the conclusion and hope if that was modeled on their behavior, then you’re modeling the system around what they would have done. You’re basically just replacing human work with computer work.” — Brian O’Neill
“What I found in my career and experience with clients is that sometimes if they can’t get it perfect, they’re worried about doing anything at all. I like this idea of [software analytics] casting an opinion.” — Brian O’Neill
“LogicMonitor’s mission is to provide a monitoring solution that just works, that’s simple enough to just go in, install it quickly, and get coverage on everything you need so that you as a company can focus on what you really care about, which is your business.” — Gadi Oren