3 Key Questions on Data-Driven Innovation

By Michael Hendrix
January 9, 2014
General Foundation

Data fuels America’s economic engine, but few of us ever look under the hood. We’re usually content to leave its detailed workings alone while we go on consuming and producing endless streams of information. All the while, data grows in significance and value to our everyday lives.

What we don’t see is a data-driven world that’s filled with unanswered questions. While this present state may continue for a while, it becomes less tenable every time a piece of that world fails to function in the ways we expect. And the less we truly understand about what’s going on, the more likely our reactions will be tinged with fear.

The truth is that anything or anyone that requires knowledge in order to work has something at stake with data. For every company in America, good data means good business. There is much to be gained from understanding more about data-driven innovation and what it means to the world we live in.

That’s why I've assembled 3 essential questions we should be asking about data. This isn’t meant to be an exhaustive list. Rather, it should spur more thought on the nagging questions that, left unanswered, may hinder true data innovation in America and across the globe.

How do key data actors foster both trust and innovation?

Raise the topic of data today and you’re bound to receive equal portions of hope and fear in reply. There is so much data out there and just as much at stake.

What seems generally agreed upon in the public and private sectors is that engendering trust and fostering innovation represent the most important goals for dealing with data. Yet a balance must be struck, for what may inspire innovation may also be perceived as doing harm to public and private trust. Equally so, what may at first lead to trust may unnecessarily hamper innovation.

Moreover, decisions on both accounts are often made in a void of knowledge. It can be hard to know what best fuels data innovation and what success looks like. Trust remains highly personal and individual, often existing on the side only of one actor in a data transaction. And while data is sown and collected in ever-greater quantities, the actual workings of so-called “Big Data” remain cloaked in relative secrecy or beneath reams of legalese.

While many different types of data exist, it’s in the realm of personal data where this balance is most keenly seen. Personal data offers clear economic and social benefits for the user and the marketplace, together with the greatest sensitivity due to its oft-times intimate and all-knowing nature. Striking an appropriate emphasis on both trust and innovation appears to rest firstly on having open, secure, reliable, and efficient data flows.

With a reliable data foundation, we can then see how lessening privacy risks—indeed, moving beyond the privacy debate—and enhancing responsible behavior would especially deepen public trust. Innovators would have sufficient space to act while understanding the priorities and boundaries of their work.

For individuals, an understanding of data usage and potential seems of a greater priority than transparency alone, which itself is highly context-dependent in its value and relevance. This means helping average people understand how and when their data are being used as well as the implications. Gaining awareness should be a key standard for both the consumers and suppliers of data.

The private sector seems well-placed to benefit from an engaged and knowledgeable market. Getting to this point though may require the development of additional tools to allow consumers to choose and control their universe of data. Firms that are clearly data-dependent, such as Google, often hand their customers the greatest flexibility in data usage, rightfully seeing that their may not be a trade-off inherent in fostering a level of trust and innovation that comes from a knowledgeable and engaged customer base.

How can we better measure the value of data?

It’s been said that you can’t manage what you don’t measure, and that statement is certainly true with valuing data. What are data worth to its producers and consumers (and why)? There’s surprisingly little material out there on this question, especially for a question that seems so fundamental.

There’s another saying that “information is currency.” It seems clear that data are assets, but you’re never going to find it directly a firm’s balance sheet or even expressed in basic value terms. Quantifying or assessing data is not normally done in the same way that a firm would regularly valuate copyrights or brand equity, even though data represent a large and growing source of revenue. As a recent article in Forbes showed, courts and insurers are “confounded about recognizing information as a form of property.”

Understanding how to better measure the value of data is important for 3 reasons. First, it helps companies identify new avenues for growth as well as for consumers to better understand the returns they receive for their data. Second, without valuation it’s hard to quantify the return on data investments. Finally, the competitive strengths of those who know the value of the data they possess or won is immeasurable, both for people and companies.

How do we engage a non-technical public on data issues?

There needs to be a fruitful conversation on data—its use and value especially—that’s focused on engaging and informing the general public. The lack of knowledge and awareness on data issues appears to be fostering a conversation based more in fear than facts. Consumers especially see a paradox where Big Data gathers a wide range of personal and public data while operating in what appears to be secrecy.

Some have argued that this calls for an approach that goes beyond transparency and rather to one of understanding. That is, helping the average user understand how and when data are being collected, how data are being used, and what the implications of such use might be—all of these represent fruitful avenues to explore. Part of engaging well with the public likely means weaving facts into a coherent story based in language that reflects how people feel.

Benjamin Wittes of The Brookings Institution has argued that what the general public really fears is the “unjustified deployment” of their data in a way that will harm their own interests. They are not saying “leave me alone.” That makes sense considering the rapid rise in free services, which depend entirely on users willingly volunteering information. Many at least intuitively get that their information both improves their user experience while also aiding advertisers. What they have a harder time seeing is whether their negative right against what Wittes calls “databuse” is being protected. These concerns may be allayed by companies clearly, tangibly demonstrating in the delivery of the service that a user’s data are working for them, not against them.

Google’s privacy policy is a good example of this more open approach. While affirming legal standards, it also works as a basic data primer explaining what information is being gathered, how it is being used, why it is important, and who can access it. The company hands a good degree of control back to the user, allowing him or her to finely tune their own data sets.

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There are many more questions to be asked, including:

  • How is data ownership determined and what rights or liabilities does it entail?
  • How will we catch up with our workforce lag?
  • How to best balance data security with data openness?

 

In the coming weeks and months, I hope to continue exploring these questions and beginning the process of opening up dialogue on data innovation. It’s a topic far too important to leave unaddressed.