When the term “big data” began surfacing, the role of the chief data officer started moving into the limelight. At first, the focus was mainly on security: how do we protect our information systems so that we don’t become the next Target? But as business leaders realized that Google and Facebook were capitalizing on big data long before some of the world’s most famous hacks (and prior to “big data” being bandied about as a new concept), companies started investing in efforts to monetize the information they already had on hand.

While the chief data officer isn’t necessarily concerned with how an organization can capitalize on its data, they fulfill the role of making sure that data is actually fit for interpretation. They’re also in charge of identifying what data is key to a company’s business, and that the right people have access to it. The job is technical—and often is aligned with the IT department—however, it’s necessary for the CDO to have a good understanding of the business in order to oversee its data effectively.

The monetization of data often involves another individual: the chief analytics officer. As the title suggests, this individual looks after interpreting a company’s data and presenting it to the organization’s leadership in such a way that final decision-makers can derive business opportunities (such as potential revenue streams, or the addition of new services) from it.

For small to medium-sized organizations, it’s tempting to combine these two roles. While there are arguments both for and against this approach, Jennifer Bellisent, principal analyst for customer insights at Forrester Research, a market research firm based in Cambridge, MA, urged business leaders not to get caught up on titles. (Bellisent prefers the term “data leadership” instead of CDO or CAO.) “Ultimately what we want companies to focus on is how data is being used,” she said. If these two data leadership roles are to be handled by one person, that individual needs to not only understand the technical aspects of data, but also how it apples to business. In other words, they need to possess the skills to show those working in the company how they can apply data to their own jobs.

 
Thomas C. Redman 
Sometimes generating revenue from existing data isn’t as complicated as business leaders may expect. “The simplest way to make money from the data is to sell it,” said Thomas C. Redman of the New Jersey-based consultancy Data Quality Solutions, and author of several books, including Getting in Front on Data, Data Driven: Profiting From Your Most Important Business Asset, and Data Quality: The Field Guide. “There is a whole cottage industry of companies that sell data, and I think that every company ought to be thinking of ways that it can sell what it has [in terms of data].” Another way to capitalize on an organization’s data is to build it into its products and services. Redman uses GPS-equipped cars as an example: when these vehicles first came out, they became more valuable to drivers because of the data the GPS technology was supplying to them.

But data is only as good as… well, the data. “You should concentrate first on quality,” Redman said. According to his research, bad data can cost a company 20 percent of its revenue because of the time and effort required to correct mistakes in the information—errors that can lead to billing issues, for example. “You just can’t do anything when people don’t trust the data. I believe that companies should start with data quality—it’ll get people involved in really good ways because everybody can contribute. It’s a big money-saver, and it will help build the capabilities you need to do other things [such as monetize data].”

Richard Heimann is a chief data scientist who, through Data Society, based in Washington, DC, gives classes to business leaders on developing data-driven strategies. Heimann is co-author (with Nathan Danneman) of Social Media Mining with R (and right now he’s writing on a book that discusses how business leaders can approach data-driven strategies, including those that may involve AI). He underlines that data within a workplace culture that isn’t open to actually learning from it—and then using this new knowledge—isn’t that effective. “I think continuous learning is important. Corporate knowledge is knowing something incompletely and answering in part, without truly understanding what it means,” he said. “There are a lot of things we know to be facts—or knew to be facts—that are no longer relevant, and we don’t update our understanding of the world as the world is changing. It often takes us a long time to update our internal schema or internal model of the world. A learning organization, I think, is just more adaptive.”

 
Richard Heimann
To illustrate his point, Heimann used chess as an example: each player is equipped with the same number of the same types of pieces at the outset of the game, and each player can trace the moves their opponent makes throughout the game. “It has a nice side effect of your knowing the probability of a particular outcome based on any movement that you might have,” he said. “The business world is anything but that. It’s dynamic—that means at any point in time your competitors could go all-in and move all of the pieces at one time … [and] you may not know that for some indeterminate amount of time.” When you think about playing chess like that, the question becomes: how many games could you actually win? “I promote thinking about: what does an adaptive strategy look like for you? Where you’re learning from the markets, from your competitors, from your customers? A lot of that is [discussed] in isolation. I promote the notion of: what does an adaptive strategy look like for you? And oftentimes I think that boils down to learning from data.”

Carolyn Heinze is a freelance writer/editor.