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Dave Wells on data literacy

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Few people in the data industry today are better qualified to opine on data literacy than Dave Wells.

He’s had a decades-long career with data, including TDWI education director for eight years, now the education director at eLearningCurve, and most recently an industry analyst with Eckerson Group. He and I developed and taught a TDWI course on data storytelling.

He has given data literacy and related issues more thought than almost anyone else I know. I asked him for his thoughts on it.

I think data literacy has been a need starting with the introduction of self-service tools. But it hasn’t had much attention until recently. Instead, we seem to embrace every new technology as if it is magic that will solve problems. Then we’re surprised when it solves some problems and creates new issues.

We are now at the point of recognizing the new problems of self-service, including literacy.  I remember Gerry Weinberg telling me many years ago that we fool ourselves when we think we’re problem solvers. We’re really problem traders. And if we’re good at it, we trade big ones for little ones.

In any case, the new problems brought by self-service are many, including governance challenges and a lot of other stuff. But one that has taken some time to gain attention is the problem of having lots of people doing data preparation, analysis, and visualization without strong data skills.

Data literacy (as with language literacy) includes both reading and writing skills. With data, that means ability to read visualizations as well as ability to create them. (I have a half-day course about this.) It also includes the ability to evaluate the fitness of data for a particular use case, ability to judge data quality, and all of the skills needed to prepare data for analysis.

Over the past year or so I’ve been adding the concept of data coaching into my discussions of self-service and data governance. I think a culture of coaching is a realistic way to increase data literacy throughout an organization. Different individuals will have data knowledge and skill in different areas — visualization, preparation, quality, and data domain expertise. They can coach where they have skills and receive coaching where they need to build skills — sort of a collaborative approach to focusing on collective literacy as well as individual literacy.

Jordan Morrow sees data literacy taking off at Qlik

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Qlik’s director of data literacy Jordan Morrow saw validation of his program soon after he took the job: His new series of online modules, he reports, rose to four of the top 10 downloads within the first couple of months.

None of the modules were easy. Being on subjects like basic statistics, they sure weren’t shiny. Without knowing the subjects of competing modules or other details, their popularity is notable — an interesting break with the data industry’s drive toward “data democratization” and “self-service” by thrusting ever newer, shinier tools toward users’ “fingertips.”

“Without data literacy,” says the head of Qlik’s data literacy program, Jordan Morrow, “[data analysis tools are] just Excel on steroids.”

Visualized data did make a leap, but it’s still just data. “If you can’t interpret [the data], it’s nothing.” That is, there’s more value than meets the eye. Artificial intelligence will find that value — and happily for owners of capital, AI will run on fewer and fewer warm bodies.

As machines overtake quantitative decision-making, humans will be left with qualitative decisions — which have to be based in data, of course.

Question: The data industry has been talking about “data democratization” and “self service BI” for years. Why does it seem to have accelerated now? What do you think? Tell me. (If the form is too short for you, just give me a brief answer and invite me to get back to you.)

Qlik Qonnections / Qlik: serious about data literacy

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Qlik could have forgotten the tired slogans — such as “data is the new currency” — at the recent Qlik Qonnections and gone straight for the what makes Qlik stand out. That starts with their tastiest, chewiest morsel: their apparently serious campaign for data literacy.

What is data literacy? Valerie Logan, Gartner data and analytics research director, described it in the show’s best presentation: It’s “the ability to read, work with, analyze, and argue with data.”

“This is the heart,” said CEO Mike Capone in the morning session, “of what’s going to drive the future.”

[Read more…] about Qlik Qonnections / Qlik: serious about data literacy

Don’t just sit there waiting for the bus, tutor a machine

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I can’t help but think of the street person with a sign saying, “Will work for food” when I read about an experiment conducted at bus stops in Finland.

Vassilis Kostakos at the University of Oulu in Finland and colleagues put up four large LCD touchscreens in bus stops and other busy public areas. A sign invited passersby to touch — and to help train malaria-detecting software.

Was it worthwhile? Apparently it was.

They got 1200 answers over 25 days and found the accuracy of the results was comparable to those of paid workers on Mechanical Turk. Lone users were the most active, perhaps because they were free from peer pressure or distraction. These loners also created a honeypot effect, attracting more users, the researchers will tell the UbiComp conference in Zurich, Switzerland, in September.

Vassilis says such displays could [also] be used to tap local knowledge, such as the best place to walk a dog, the meaning of some confusing signage, or what bands are playing in town.

The researchers promised nothing to participants, of course. But just wait for this to catch the eye of alert transit administrators, and for transit districts to adopt it.

Where there’s machine learning there’s money. Starving public transit should have some of it, especially if it works for food.

Who pays for smart cities?

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This post was originally published by TDWI (The Data Warehousing Institute) as a Flashpoint feature on January 12, 2018.

One recent sunny morning there was a traffic jam on Cottage Street, a narrow street within view of my house, aptly named for its width. Commuters heading toward the nearby bridge over San Francisco Bay found themselves inching forward after their “smart” navigation devices had sent them on a shortcut.

The trouble with “smart” tech is that it starts out stupid. Users and neighborhoods suffer inconvenience to train developers’ algorithms—which is one part of to a common question about smart cities: “Who pays?” After all, most cities can’t afford the out-of-pocket expense for the fancy new technology. Instead, cities find other ways to get it and many of those involve compromise.

Financial capital does seem to be available. Smart Cities Financing Guide, published by the Smart Cities Council in 2015, lists 31 potential sources in three broad categories. Two of them put money under city control. Government-based funding includes, for example, general obligation bonds. These raise money from private bond buyers and are paid back mostly by taxes. A second category is called “development extractions,” which show up as tap fees, charged for new utility hookups, and other direct fees.

A third category—public-private partnerships—complicates the whole “who pays?” matter. In such arrangements, vendors provide technology in exchange for market presence (a fertile ground on which develop their technology) and often ownership of the data that results.

Navigating Competing Agendas

Naturally, the public and private sides of the bargaining table come with conflicting priorities. The private side has to satisfy regulatory and funding requirements and perhaps also a few broad mandates from top city leadership. One unspoken mandate is to keep the citizens happy. Citizen groups, at least the effective ones, persistently and perhaps dramatically advocate their various agendas. Better street lighting, better transit, and better protections for privacy and other causes exert steady pressure. Now and then, an ad hoc uprising may object to letting modern travel-direction apps clog horse-and-buggy-era streets.

On the other side of the table, technology vendors want a place to perfect technology, reap data, and build reputations. “[Technology vendors] believe that this will be a big market,” said Todd Walter, in Internet of Things marketing at Teradata, itself an interested vendor. “They all want to sell their stuff to the projects beyond the pilots and proofs of concept.” Vendors have already gone on their own to build or schedule such projects as “smart buildings” in which analytics finely tunes heating, cooling, and other basic functions; “smart” utility grids; integration of interagency emergency systems; data sharing among taxis and public transit; on-street video security; and video toll booths.

The two sides can seem farther apart than a mere table’s breadth. “When public and private sectors get together,” said Peeter Kivestu, Teradata director of travel industry solutions and marketing, “the public sector thinks they’re sitting across from a bunch of sophisticated hustlers who’re going to rip them off. The private sector thinks they have a bunch of public sector guys who can’t be trusted to achieve consistency because of the political and policy flows that go on.”

The Winding Road to Infrastructure ROI

Some vendors avoid any such involvements. Some telecommunication carriers, for example, are funding their own upgrades. So far, it looks like their networks will be the backbone of “smart.”

“[Smart systems] are very chatty,” said Dennis Duckworth, a product marketing and strategy consultant who’s recently worked with telecommunication carriers. “You take those maps and there are millions and millions and millions of [bits transmitted], and they’re taking up a lot of aggregate bandwidth.” During the long retrofit for beefier networks, there will be no shutting down. The rolling-upgrade will be expensive, and the carriers have to monetize the new, beefier networks somehow.

That payback, explained Duckworth, may well start not directly from the carriers but through appliance purchases by consumers. New, so-called “smart refrigerators” will signal to a mother ship, “Hey, my ice maker is faulty. Send a new one!” For that, the consumer will pay a little bit extra even when a “dumb” refrigerator might do just as well. Even the everyday water pipe will come with sensors built in, said Duckworth, whether you can use them or not.

Why do the carriers go to the trouble? They want the data, explains Duckworth. They’ve been watching Google and Facebook reap data that now flows over the telcos’ lines. The carriers want to play, too.

As any streetwise city dweller knows, those who pay call the shots. The only difference is that now the answer to “who pays” may also tell you who controls the technology and the data.