Experiencing Data w/ Brian T. O’Neill

When it comes to telling stories with data, Cole Nussbaumer Knaflic is ahead of the curve. In October 2015, she wrote a best-selling book called storytelling with data: a data visualization guide for business professionals. That book led to the creation of storytelling with data, an agency that helps businesses communicate more effectively using data, and she’s since followed-up with another best-seller: storytelling with data: let’s practice! Prior to her current role, Cole served as a people analytics manager at Google, was the owner and chief consultant at Insight Analytics, and held several positions at Washington Mutual, among other positions.

In our chat, we covered:

• Why sharp communication skills are integral to telling stories with data

Designingforanalytics.com/seminar

Twitter: @Storywithdata.

Company website:  Storytellingwithdata.com

“I've always really enjoyed that space where numbers and business intersect and enjoy how we can use numbers to get to understand things better and make smarter decisions.” — Cole

“If you're the one analyzing the data, you know it best. And you're actually in a unique position to be able to derive and help others derive greater value from that data. But in order to do that, you have to be able to talk to other people about it and communicate what you've done to technical audiences and to non-technical audiences.” — Cole

“When it comes to communicating effectively with data, you can't take out the human part. That's the part where things can either succeed or fail.” — Cole

“There's no single right way to show data. Any data can be graphed a ton of different ways. And so when we're thinking about how we visualize our data, it really means stepping back and thinking about what sort of behavior we’re trying to drive in our audience. What do we want them to see in this? And then it often means iterating through different views of the data, which is also a fantastic way just to get to know your data better because different views will make observations easier or less easy to see.” — Cole

“As soon as we try to draw attention to one aspect of the data or another, it actually makes any other potential takeaways harder to see.” — Cole

“Words are very important for making our data accessible and understandable.” — Cole

“Depending on the visualization, what you're doing is you're teaching yourself not to assume that the information is necessarily clear. You're being objective. And it sounds like a dumb question, but that's kind of what I usually recommend to my clients: We need to be really objective about our assumptions about what's being communicated here and validate that.” — Brian

“The low-fidelity format—especially if you're working with a stakeholder or perhaps someone who's going to be the recipient—enables them to give you honest feedback. Because the more polished that sucker looks, the less they're going to want to give you any.” — Brian

Angela Bassa is the director of data science and head of data science and machine learning at iRobot, a technology company focused on robotics (you might have clean floors thanks to a Roomba). Prior to joining iRobot, Angela wore several different hats, including working as a financial analyst at Morgan Stanley, the senior manager of big data analytics and platform engineering at EnerNOC, and even a scuba instructor in the U.S. Virgin Islands.

Join Angela and I as we discuss the role data science plays in robotics and explore:

• Why Angela doesn’t believe in a division between technical and non-technical skill

Twitter: @AngeBassa

Angela’s Website

iRobot

Designing for Analytics Seminar

“Because these tools that we use sometimes can be quite sophisticated, it’s really easy to use very complicated jargon to impart credibility onto results that perhaps aren’t merited. I like to call that math-washing the  result.” — Angela

“Our mandate is to make sure that we are making the best decisions—that we are informing strategy rather than just believing certain bits of institutional knowledge or anecdotes or trends. We can actually sort of demonstrate and test those hypotheses with the data that is available to us. And so we can make much better informed decisions and, hopefully, less risky ones.” — Angela

“Data alone isn’t the ground truth. Data isn’t the thing that we should be reacting to. Data are artifacts. They’re breadcrumbs that help us reconstruct what might have happened.” — Angela

[When getting somebody to trust the data science work], I don’t think the trust comes from bringing someone along during the actual timeline. I think it has more to do with bringing someone along with the narrative.—Angela

“It sounds like you’ve created a nice dependency for your data science team. You’re seen as a strategic partner as opposed to being off in the corner doing cryptic work that people can’t understand.” — Brian

“When I talk to data scientists and leaders, they often talk about how technical skills are very easy to measure. You can see them on paper, you can get them in the interview. But there are these other skills that are required to do effective work and create value.” — Brian

Tom Davenport has literally written the book on analytics. Actually, several of them, to be precise. Over the course of his career, Tom has established himself as the authority on analytics and how their role in the modern organization has evolved in recent years. Tom is a distinguished professor at Babson College, a research fellow at the MIT Initiative on the Digital Economy, and a senior advisor at Deloitte Analytics. The discussion was timely as Tom had just written an article about a financial services company that had trained its employees on human-centered design so that they could ensure any use of AI would be customer-driven and valuable. We discussed their journey and:

• Why on a scale of 1-10, the field of analytics has only gone from a one to about a two in ten years time

• Website: Tomdavenport.com

“If you survey organizations and ask them, ‘Does your company have a data-driven culture?’ they almost always say no. Surveys even show a kind of negative movement over recent years in that regard. And it's because nobody really addresses that issue. They only address the technology side.” — Tom Eventually, I think some fraction of [AI and analytics solutions] get used and are moderately effective, but there is not nearly enough focus on this. A lot of analytics people think their job is to create models, and whether anybody uses it or not is not their responsibility...We don't have enough people who make it their jobs to do that sort of thing. —Tom I think we need this new specialist, like a data ethnographer, who could sort of understand much more how people interact with data and applications, and how many ways they get screwed up.—Tom I don't know how you inculcate it or teach it in schools, but I think we all need curiosity about how technology can make us work more effectively. It clearly takes some investment, and time, and effort to do it.— Tom TD Wealth’s goal was to get [its employees] to experientially understand what data, analytics, technology, and AI are all about, and then to think a lot about how it related to their customers. So they had a lot of time spent with customers, understanding what their needs were to make that match with AI. [...] Most organizations only address the technology and the data sides, so I thought this was very refreshing.—Tom “So we all want to do stuff with data. But as you know, there are a lot of poor solutions that get provided from technical people back to business stakeholders. Sometimes they fall on deaf ears. They don't get used.” — Brian “I actually had a consultant I was talking to recently who said you know the average VP/director or CDO/CAO has about two years now to show results, and this gravy train may be slowing down a little bit.“ — Brian “One of the things that I see in the kind of the data science and analytics community is almost this expectation that ‘I will be handed a well-crafted and well-defined problem that is a data problem, and then I will go off and solve it using my technical skills, and then provide you with an answer.’” — Brian

Dean Malmgren cut his teeth as a chemical and biological engineer. In grad school, he studied complex systems and began telling stories about them through the lens of data algorithms. That led him to co-found Datascope Analytics, a data science consulting company which was purchased by IDEO, a global design firm. Today, Dean is an executive director at IDEO and helps teams use data to build delightful products and experiences.

Join Dean and I as we explore the intersection of data science and design and discuss:

• Human-centered design and why it’s important to data science

Twitter: @DeanMalmgren

IDEO

Datascope

“One of the things that we learned very, very quickly, and very early on, was that designing algorithms that are useful for people involves a lot more than just data and code.” — Dean

“In the projects that we do at IDEO, we are designing new things that don’t yet exist in the world. Designing things that are new to the world is pretty different than optimizing existing processes or business units or operations, which tends to be the focus of a lot of data science teams.” — Dean

“The reality is that designing new-to-the-world things often involves a different mindset than optimizing the existing things.” — Dean

“You know if somebody rates a movie incorrectly, it’s not like you’d throw out Siskel and Ebert’s recommendations for the rest of your life. You just might not pay as much attention to them. But that’s very different when it comes to algorithmic recommendations. We have a lot less tolerance for machines making mistakes.” — Dean

“The key benefit here is the culture that design brings in terms of creating early and getting feedback early in the process, as opposed to waiting you know three, five, six, seven months working on some model, getting it 97% accurate but 10% utilized.” — Brian

“You can do all the best work in the world. But at the end of the day, if there’s a human in the loop, it’s that last mile or last hundred feet, whatever you want to call it, where you make it or break it.” — Brian

“Study after study shows that 10 to 20% of big data analytics projects and AI projects succeed. I’ve actually been collecting them as a hobby in a single article, because they keep coming out.” — Brian