Experiencing Data w/ Brian T. O’Neill

Ganes Kesari is the co-founder and head of analytics and AI labs at Gramener, a software company that helps organizations tell more effective stories with their data through robust visualizations. He’s also an advisor, public speaker, and author who talks about AI in plain English so that a general audience can understand it. Prior to founding Gramener, Ganes worked at companies like Cognizant, Birlasoft, and HCL Technologies serving in various management and analyst roles.

Join Ganes and I as we talk about how design, as a core competency, has enabled Gramener’s analytics and machine learning work to produce better value for clients. We also touched on:

• Why Ganes believes the gap between the business and data analytics organizations is getting smaller

Gramener.com

Ganes Kesari on Twitter: @Kesaritweets

Ganes Kesari on LinkedIn: https://www.linkedin.com/in/ganes-kesari/

“People tend to have some in-house analytics capability. They’re reaching out for design. Then it’s more of where people feel that the adoption hasn’t happened. They have that algorithm but no one understands its use. And then they try to buy some license or some exploratory visualization tools and they try their hand at it and they’ve figured out that it probably needs a lot more than some cute charts or some dashboards. It can’t be an afterthought. That’s when they reach out.” — Ganes

“Now a lot more enquiries, a lot more engagements are happening centrally at the enterprise level where they have realized the need for data science and they want to run it centrally so it’s no longer isolated silos.” — Ganes

“I see that this is a slightly broader movement where people are understanding the value of data and they see that it is something that they can’t avoid or they can’t prioritize it lower anymore.“ — Ganes

“While we have done a few hundred consulting engagements and help with bespoke solutions, there is still an element of commonality. So that’s where we abstracted some of those, the common or technology requirements and common solutions into our platform.” — Ganes

“My general perception is that most data science and analytics firms don’t think about design as a core competency or part of analytics and data science—at least not beyond perhaps data visualization.” —Brian

“I was in a LinkedIn conversation today about this and some comments that Tom Davenport had made on this show a couple of episodes ago. He was talking about how we need this type of role that goes out and understands how data is used and how systems and software are used such that we can better align the solutions with what people are doing. And I was like, ‘amen.’ That’s actually not a new role though; it’s what good designers do!” — Brian

Joost Zeeuw is a data scientist and product owner at Pacmed, a data-driven healthcare and AI  startup in Amsterdam that combines medical expertise and machine learning to create stronger patient outcomes and improve healthcare experiences. He’s also taught a number of different subjects—like physics, chemistry, and mathematics—at Lyceo, an online education service, and Luzac College in the Netherlands.

Join Brian and Joost as they discuss the role of design and user experience within the context of providing personalized medical treatments using AI. Plus:

• The role data has in influencing doctors’ decisions—without making the decisions

• Joost on LinkedIn

“Pacmed in that has a three-fold mission, which is, first of all, to try to make sure that every single patient gets the treatment that has proven to work for him or her based on prior data analysis. And next to that we say, ‘well, if an algorithm can learn all these awesome insights generated by thousands and thousands of doctors, then a doctor using one of those products is also very capable of learning more and more things from the lessons that are incorporated in this algorithm and this product.’ And finally, healthcare is very expensive. We are trying to maximize the efficiency and the effectiveness of that spend by making sure everybody gets a treatment that has the highest probability of working for him or her.” — Joost

“Offering a data product like this is really another tool in that toolbox that allows the doctor to pierce through this insane amount of complexity that there is in giving care to a patient.” — Joost

“Before designing anything, we ask ourselves this: Does it fit into the workflow of people that already have maybe one of the most demanding jobs in the world?” — Joost

“There’s a very big gap between what is scientifically medically interesting and what’s practical in a healthcare system.” — Joost

“When I talk about design here, I’m talking kind of about capital D design. So product design, user experience, looking at the whole business and the outcomes we’re trying to drive, it’s kind of that larger picture here.” — Brian

“I don’t think this is ‘normal’ for a lot of people coming from the engineering side or from the data science side to be going out and talking to customers, thinking about like how does this person do their job and how does my work fit into you know a bigger picture solution of what this person needs to do all day, and what are the health outcomes we’re going for? That part of this product development process is not about data science, right? It’s about the human factors piece, about how does our solution fit into this world.” — Brian

“I think that the impact of bringing people out into the field—whatever that is, that could be a corporate cubicle somewhere, a hospital, outside in a farm field—usually there’s a really positive thing that happens because I think people are able to connect their work with an actual human being that’s going to potentially use this solution. And when we look at software all day, it’s very easy to disconnect from any sense of human connection with someone else.” — Brian

“If you’re a product owner or even if you’re more on the analytics side, but you’re responsible for delivering decision support, it’s really important to go get a feel for what people are doing all day.” — Brian

Di Dang is an emerging tech design advocate at Google and helped lead the creation of Google’s People + AI Guidebook. In her role, she works with product design teams, external partners, and end users to support the creation of emerging tech experiences. She also teaches a course on immersive technology at the School of Visual Concepts. Prior to these positions, Di worked as an emerging tech lead and senior UX designer at POP, a UX consultant at Kintsugi Creative Solutions, and a business development manager at AppLift. She earned a bachelor of arts degree in philosophy and religious studies from Stanford University.

Join Brian and Di as they discuss the intersection of design and human-centered AI and:

• Why a data science leader should care about design and integrating designers during a machine-learning project, and the impacts when they do not

Twitter: @Dqpdang

Di Dang’s Website

Di Dang on LinkedIn

People + AI Guidebook

“Even within Google, I can’t tell you how many times I have tech leaders, engineers who kind of cock an eyebrow at me and ask, ‘Why would design be involved when it comes to working with machine learning?’” — Di

“Software applications of machine learning is a relatively nascent space and we have a lot to learn from in terms of designing for it. The People + AI Guidebook is a starting point and we want to understand what works, what doesn’t, and what’s missing so that we can continue to build best practices around AI product decisions together.” — Di

“The key value proposition that design brings is we want to work with you to help make sure that when we’re utilizing machine learning, that we’re utilizing it to solve a problem for a user in a way that couldn’t be done through other technologies or through heuristics or rules-based programming—that we’re really using machine learning where it’s most needed.” — Di

“A key piece that I hear again and again from internal Google product teams and external product teams that I work with is that it’s very, very easy for a lot of teams to default to a tech-first kind of mentality. It’s like, ‘Oh, well you know, machine learning, should we ML this?’ That’s a very common problem that we hear. So then, machine learning becomes this hammer for which everything is a nail—but if only a hammer were as easy to construct as a piece of wood and a little metal anvil kind of bit.” — Di

“A lot of folks are still evolving their own mental model around what machine learning is and what it’s good for. But closely in relation—because this is something that I think people don’t talk as much about maybe because it’s less sexy to talk about than machine learning—is that there are often times a lot of organizational or political or cultural uncertainties or confusion around even integrating machine learning.” — Di

“I think there’s a valid promise that there’s a real opportunity with AI. It’s going to change businesses in a significant way and there’s something to that. At the same time, it’s like go purchase some data scientists, throw them in your team, and have them start whacking stuff. And they’re kind of waiting for someone to hand them a good problem to work on and the business doesn’t know and they’re just saying, ‘What is our machine learning strategy?’ And so someone in theory hopefully is hunting for a good problem to solve.” — Brian

“Everyone’s trying to move fast all the time and ship code and a lot of times we focus on the shipping of code and the putting of  models into production as our measurement—as opposed to the outcomes that come from putting something into production.” — Brian

“The difference between the good and the great designer is the ability to merge the business objectives with ethically sound user-facing and user-centered principles.” — Brian