Hi, I’m Geoff, and I’m a TED Talk skeptic.
I know TED Talks are, well, talked about endlessly. I know their reputation, and branding, precedes them. I’ve seen a few I like (this one by Barbara Amaya is phenomenal, and a great, brief introduction to the problem of human trafficking), but I just can’t get started with TED.
They all seem so formulaic. One of my coworkers sent me a video yesterday that illustrates my cynicism. In it, a comic gives a parody TED Talk where he acts out all the TED cliches… the use of slides and statistics, the confident hand gestures, the predictable personal anecdote.
So why open a post about TED Talks that deal with business intelligence software mentioning how much I dislike TED Talks? Because it means I approach them as a devil’s advocate. If I think a TED Talk is worthwhile, it’s a good sign that non-curmudgeons will like it a great deal.
These nine TED Talks will teach you a bit about business intelligence and the science behind it. I’ve also provided a list of how much time each one takes, with a corresponding amount of time out of your daily routine.
Peter Donnelly, “How juries are fooled by statistics”
In this talk, Peter Donnelly explains how a jury in an infant death case reached the wrong verdict because they were misled by statistics. Donnelly’s a skeptic about how statistics are used to motivate people, and that’s why I included him.
Statistics are big in big data. According to the American Statistical Association, they’re one of the three pillars of data science. Statistics are used to persuade decision makers and sway customers. They’re also necessary to deal with the sheer amount of data.
Given how much rides on statistics, you’ll want to listen to Donnelly’s criticisms. The devil’s advocate approach is one of the best ways to see weaknesses, which is the start of building strengths.
Time: 21:20, half of a trying treadmill workout
Kevin Novak, “Data science for entrepreneurs”
Want to know how Uber turned data into cash?
The first five minutes are a description and history of Uber, but the meat starts after that. Novak describes how Uber’s data science, or data hacking, as he calls it, made Uber’s explosion possible. Specifically, he describes how Uber solved a problem with the user interface of their surge pricing display.
What might be most useful about this is Novak’s focus on the intersection of data and design. Novak contrasts the difference between what the coders’ focus on data models, and the marketing team’s focus on the product that model creates. Good advice from someone who helped make data disrupt the transportation industry.
Time: 17:10, or a half mile walk to the metro station when you’re barely awake
Ben Wellington, “Making data mean more through storytelling”
I get a rash from phrases like “effect social change” or “change the world.” Platitudes are my kryptonite. So, I should have vomited, Exorcist-style, when I saw this talk about using data storytelling to effect social change.
I didn’t, of course (why else would I be mentioning this), because Ben Wellington’s talk on data’s transformative powers wowed me. This talk should be a Kali’s necklace of cliches. Instead, it’s a series of anecdotes you wouldn’t mind telling a friend.
In one instance, Wellington recounts how the data he collected from New York City’s open data project allowed him to find a parking spot that had generated over $55,000 in parking tickets because of a disagreement between the NYPD and the Department of Transportation. Now that’s social change.
Wellington’s lecture is a miniature master class in how to tell a story out of number crunching. Given the importance of storytelling to marketing, it’s a short hop from Wellington’s data tales to your company’s sales possibilities.
Time: 14:18, but it’s good enough that you won’t care about time
Thomas Goetz, “It’s time to redesign medical data”
The “four questions that every patient should ask” mentioned at the end of this talk could easily be applied to a business scenario. Speaker Thomas Goetz is talking about medicine, but his focus on owning your data is solid, whether that data’s about your renal health or your revenue.
Equally relevant is his breakdown of what sort of approach provides the best motivation. Though he draws his specific example from dentistry, the general revelations he makes could also apply to marketing:
When you give people specific information about their health, where they stand, and where they want to get to, where they might get to, that path… tends to work for behavior change… you start with personalized data… and then you need to connect it to their lives… There’s an emotional connection to information because it’s from us.”
“Personalized data” and “emotional connections” are the sort of things business leaders need to turn a mass of business data into genuine business intelligence.
Time: 16:33, half of a sitcom episode
Wingham Rowan, “A new kind of job market”
How can data help with hiring? What can it do for employees who need non-traditional, flexible work hours?
Wingham Rowan believes data about worker availability can help make a “marketplace for spare hours.” This marketplace could offer opportunities to those workers unavailable for the traditional 9-to-5 shift. The same marketplace could also unlock millions of pounds of untapped opportunities (with a name as remarkably British as Wingham Rowan, did you think I’d say dollars?).
The potential of Rowan’s idea is that it shows workers, investors and entrepreneurs when, and where, to enter a market. Though this talk is a few years old, his general observations about how data can be a dowsing rod for untapped potential are still solid. Better yet? This lecture suggests how data can become work opportunities.
Time: 12:20, an overly leisurely trip to the restroom
Rajiv Maheswaran, “The math behind basketball’s wildest moves”
If basketball’s of more interest to you than business intelligence, you’ll like this talk. If you want pithy examples of what algorithms and machine learning are, you’ll like Rajiv Maheswaran’s talk even more.
Algorithms are necessary to the functioning of any BI software, and machine learning has been called “the new BI.” Googling those terms is useful, but a little dull. Learning about them in the context of pick-and-roll’s and Ray Allen’s clutch 3 pointer in the 2013 NBA Finals, however, is fun.
This talk is cool. It’s not directly about business, but apply what Maheswaran’s data about players to, say, foot traffic in a retail establishment, and you have the start of a strategy for how to drive in store traffic.
Time: 12:08, or the time it takes to drive about half a block during D.C. rush hour
Ruben Vendeventer, “Data overload: raising the information generation”
Ruben Vendeventer’s talk is pitched towards students, but it’s useful for business people too. The value in Vandeventer’s talk is his orientation towards data and the information age. For example? He conceptualizes our knowledge of DNA as data points. This kind of reorientation is a solid start for any learner’s relationship the data behind business intelligence.
This is a valuable talk for a broad, global view of how data’s transforming business and the rest of society. Vandeventner’s thesis can be summed up by his statement that enough data could have solved the question of which came first, the chicken or the egg. That may be a tad optimistic for my tastes, but this talk’s still worth a look.
Time: 21:13, or a very meandering sermon
Sendhil Mullainathan’s “Solving social problems with a nudge”
Sendhil Mullainathan wants to make data make a difference.
Data, on its own, can’t make you do something. Data can tell you what you should do, but actually doing it’s another matter. He says, “Convincing people to do something… is not an act of information: ‘Let’s give them the data, and when they have data they’ll do the right thing.’ It’s more complex than that.” Getting people to do that right thing is what Mullainathan’s talk is about.
How is this relevant to your business? Mullainathan discusses how to bridge that gap between giving information and getting results. Though his principal focus is on public health, he highlights several instances where the right piece of data, used in the right way, was the difference between a sale and a lost customer.
If you want to turn your data into marketing deliverables—like the utility company example in the last five minutes—you’ll want to look into Mullainathan’s talk.
Time: half a (30 min.) lunch break
Jer Thorp, “The weight of data”
This talk is great, and not just because Jer Thorp sounds like the name of a Star Wars character (note: not an insult; I have measured out my life in Boba Fett t-shirts). This lecture is stellar because it frames the story of how data can tell our stories.
Jer Thorpe’s project, openpaths.cc, tracked (willing) participants’ iPhone location data, his own included. His take on his own data’s story—matching each location with his career journey, like the first Thai restaurant he went to in a new town, or where he met his girlfriend—is marketing that nearly writes itself. Where do your workers travel? What was going on when they were there? What were they doing in that (hopefully sanctioned) trip to Milwaukee?
They’re the sort of personal details that could humanize otherwise humdrum material. They’re also the sort of details that the people at openpaths are interested in.
Time: 17:28, or a Guinness-record plank session. Probably.
Talk to me
Are there any TED Talks on business intelligence I missed? Let me know in the comments below!
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