No one doubts that digital data holds great value. “Like they say, data is the new oil,” says Will Lassalle (@wlassalle), Chief Information Officer at JLS Technology USA. Few would disagree with this assessment.
As critical resources go, however, data differs from oil in a number of important ways. For starters, unlike oil, digital data is far from a limited resource. Indeed, it is expanding at an exponential rate, as significant percentages of the world’s activities – both business and consumer – take place within the digital realm.
Another key difference: as more oil enters the market, its value plunges. The reverse is true for data. The more data companies collect, analyze, and leverage, the more value it can deliver.
That said, “value” can be a vague concept, with meanings and measurements that vary considerably among different organizations. Chief Technology Officer Brian E. Thomas (@DivergentCIO), asks, for example, “Is the data valuable because it needs to be secured (via regulations such as HIPAA and PCI), or is it the highly rich consumer data that companies are striving to make business shifts based on purchasing trends?”
“Before you can determine value,” he adds, “you need to have structured and mature data governance and data analytics programs. That way there is a collaborative process for classifying and using that data based on its criteria.”
Some observers recommend taking an even broader perspective before attempting to assess the value of their data. “The most valuable data is the ‘right’ data,” says Gene De Libero (@GeneDeLibero), Chief Strategy Officer and Head of Consulting at GeekHive. “But you can’t get to ‘right’ unless you’ve taken the time to define a unified business, technical, and data roadmap.
“What problems are you trying to solve?,” he continues. “What are your desired outcomes? Answering questions like these will help you define the roadmap, surface the ‘right’ data, and establish value.”
“One major challenge is that it is not always clear what data will be critical to solving your problem,” says Joe DosSantos (@JoeDosSantos), Chief Data Officer at Qlik. “The world is awash with data that is of varying level of value based on the problem you are trying to solve. The most advanced analytics organizations will create ways to rapidly catalog and access new data that might be important, iterate quickly, and fail fast if necessary. The result will be an accelerated path to separating out the data wheat from the data chaffe.”
Qualifying and Quantifying Data’s Value
Once an organization has answers to these types of strategic questions, it can move on to tackle the challenges posed by “the famous Vs” – data volume, variety, and velocity – says Linda Grasso (@LindaGrass0), Chief Operations Officer at Digital Business Innovation Srl. However, she says, unlike these other Vs, value doesn’t have to be a quantitative measure.
“Value can be determined in a qualitative way,” says Grasso. This can be done via “a deep analysis of what are the key data the enterprise should harness in order to get a profitable return, [and that depends] on the business model of each organization.”
That’s not to say there aren’t incentives, and means, to quantify the value of digital data. “I use a structured approach [by] aggregating a few components of our data’s’ value – intrinsic, derivative, and algorithmic,” says Lassalle at JLS Technology USA. “I use this approach to assess the true value, placing a real dollar amount on what data is worth to help organizations manage the risk around data as their most important asset.”
Qualitative or quantitative, the value of data isn’t static. As data ages, for example, its value can wane. Conversely, real-time data is often extremely valuable, as is data supplemented with complementary data from other sources.
Most people tend to focus on, and put value on, the data that emerges from their own operations or other familiar sources, notes Mark Thiele (@mthiele10), CEO at Edgevana. “The opportunity for gaining value from data grows considerable when it is combined with data outside the direct sphere of influence of any of the contributing individuals or groups,” he says.
A Spectrum of Use Cases
At a fundamental level, “Every company has to evaluate data with an eye to how it’s interpreted and how it could drive positive change across the enterprise,” says Technology Journalist Jeff Cutler (@JeffCutler). But he believes that one category of data is especially valuable. “I would propose that data pertinent to operational efficiency be rated most important to any business,” Cutler says.
Tony Flath (@TmanSpeaks), Enterprise Account Executive at Shaw Communications, agrees that high-value data use cases can include operational activities such as reducing unplanned downtime and reducting inventory cost. He also notes that other use cases, including improving customer retention and boosting product cross-sell, are more customer focused. Ultimately, he says, “we map or attribute the financial value of the operational and business use cases back to the supporting data sources.”
For Larry Larmeu (@LarryLarmeu), Enterprise Strategist at Accenture, using data to optimize operational processes has value, but with limitations. “There’s only so much juice you can squeeze out of an orange,” he cautions.
By comparison, “Consumer behavior data is considered extremely valuable because it gives insight into what people want,” he says. That insight, in turn, “allows you to target products and services to their needs, promoting top-line growth.”
As they seek to better understand the behaviors and wants of their customers, companies are trying to maximize the value of data by tapping machine learning and other artificial intelligence technologies, notes Scott Schober (@ScottBVS), President and CEO of Berkeley Varitronics Systems, Inc. Companies such as Amazon are able to leverage these technologies on a massive scale, he says.
Amazon can “efficiently collect data on a billion customers’ behaviors and utilize machine learning to better predict what their customer would likely want to watch or buy next,” Schober says. “As more people fulfill this predictive model, the algorithm becomes smarter.”
Data’s Value Poses Risks
In today’s digitally dependent world, of course, there is a downside to the growing value of data: cyber threats. In addition to calculating how they can leverage data for their benefit, organizations must also assess and mitigate the risks associated with unauthorized data access, corruption, loss, or exposure.
“For some organizations, regulatory and legal risks associated with storing data will be at the top of the [risk] rankings,” says Kayne McGladrey (@kaynemcgladrey), Chief Information Security Officer at Pensar Development. “For others, the reputational damages associated with a data breach will claim the top spot.”
From a security perspective, the most valuable data is that that constitutes the “crown jewels of an organization,” says Matthew Hackling (@mhackling), Director at Ronin Security Consulting Pty Ltd.
To identify and secure these data jewels, “The first step is to walk through the organisation’s value chain of key business processes and identify the datasets required to support those processes,” Hackling says. “Then you find out in which crown jewel applications these datasets reside, where they are hosted, and by whom.”
Arguably, the highest value data is that which is both central to a company’s success and is also a prime target for cyber attacks. A good example of such “dual value” data is Coca-Cola’s “secret formula.”
“The formula for Coke allows them to sell a product with terrific sales and margins,” says Wayne Sadin (@waynesadin), a board governance fellow at the National Association of Corporate Directors. At the same time, notes Hackling, the formula is a data crown jewel that demands the highest level of security protection.
At the end of the day, there is no single way to assess which data is the most valuable to any given organization – or to outsiders. For companies at a loss to make such assessments, Sadin suggests a simple rule: “Find out what data your board of directors uses to make its decisions,” he says. “You can bet that data is valuable to your organization!”
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