Education Data: Lessons Learned

A recent Reuters story described a new national database of student information. Reportedly built at a cost of $100 million, and backed by prestigious non-profits such as the Bill and Melinda Gates Foundation and the Carnegie Corporation, the aim of the project is to build a standardized database of information on all students in the country, grades K-12. No, this is not aggregate data. This is detailed, specific information on every student that can include such information as grades, learning disabilities, hobbies and interests. Surely this database doesn’t include student names and other identifiers you say. But in fact it does. And that’s the point. It’s also why this database is so exciting to so many companies in the education market. The goal is to jump-start technology-driven individualized learning for students.

According to the article, school administrators have long (and legally) maintained all sorts of data on students for educational purposes. And, as you would suspect, every school did things a little differently. They collected different data elements and held them in different formats in different locations. So if you were marketing educational technology to schools that tried to personalize the learning experience, you faced a painful data interface challenge for every new school you sold. Seeing a real impediment to growth for cutting-edge educational technology, several big foundations jumped. And rather than just developing a data standard which would take decades to gain widespread adoption, they invested to actually build a single database. Participation by schools is voluntarily and (currently) free, but lots of incentives have been created to spur participation.

We can draw a few fascinating lessons and trends from this initiative.

First, we see a wonderful acknowledgement of what I modestly call Perkins’ Law: no organization will voluntarily build and maintain a database if it is outside their core competencies and there is a viable alternative to doing so. The commercial data publishing business is really built around this law: data publishers succeed because people want the data, but don’t want to collect or maintain it themselves.

Second, we see another great example of a “data pipe,” where one organization provides data that developers can tap into via APIs to build applications driven by that data. The data provider seeks to become an information utility, while dozens or even hundreds of different developers can identify and mine niche opportunities faster and better than any single data publisher. This is a relatively young model, but it’s quickly gaining a following.

Third, valuable data is more often than not sensitive data as well. As this database hits the radar of parents and civil liberties advocates, the inevitable questions around privacy and security are being asked. And the answers to date, according to the article, do not seem particularly robust or reassuring. The non-profit managing the database makes all the appropriate noises about protecting the data, while at the same time the database exists in large part to benefit commercial entities. While the goal of the database is laudable, we have a classic example of a database that will likely succeed only with strong governance and privacy policies. This is something that commercial data publishers will need to become attentive to in years to come.

It’s a fascinating initiative, and one where we can all learn by example.

Got Klout?

Imagine a business based on a mash-up of social media, analytics and ratings. And that's exactly where a company called Klout plays.

Klout exists to assess your social media importance. Using advanced algorithms, it looks at how active you are in social media, how big your audience is, how influential are the people in your audience, and the impact of your social media activity. All this gets rolled up in a Klout score - a number from 1 to 100.

If this sounds like nothing more than an interesting academic research exercise, you might be surprised. Klout reportedly has over 5,000 large companies tapping into its database to determine who really matters online. Uses are varied and fascinating. PR companies use Klout to assess whether or not to personally engage with someone who has made a negative online comment about a client. Marketers are creating customized pitches to those with the highest Klout scores in the hopes of engaging with them and getting them to talk to their audiences about their products. And this is just the tip of the iceberg in terms of potential applications. Consider, for example, that Klout has already built a connector to Salesforce.com.

In terms of potential applications, some are cutting edge, but not all are necessarily positive. There are numerous reports floating around of people applying for jobs and being rejected due to low Klout scores. Some hotels reportedly will look up your Klout score at check-in, and provide free upgrades to those with high scores, presumably in the hopes of favorable online mentions. Similarly, Cathay Pacific airlines will make its San Francisco frequent flier lounge available to anyone with a high Klout score - regardless of what airline they are flying. The objective again is favorable mentions.

Implications? What we may be seeing is a devolution in advertising where marketers move to a bottoms-up approach to distributing their messages, with the hope that they can achieve powerful and cost-effective reach by having a small group of individuals amplify their brands and their messages for them. This could have serious impact on those that make money today by aggregating fixed audiences.

Of course, as the rewards for having social influence grow, so too will the number of people gaming the system to improve their scores to reap all these upgrades, free samples and attention. As these activities accelerate, social media measurement could end up getting so polluted and undependable that it becomes too difficult to isolate true influencers, likely a fatal blow to this innovative new marketing approach. Alternatively, Klout, like Google, could try to keep the game going by regularly tweaking its algorithms to maintain its value. But as we add the wisdom of algorithms to the wisdom of crowds, are we really getting any smarter?