Most companies recognize the potential for data insights to improve customer experience, better direct marketing strategies, create new products and services, and optimize operations, among myriad compelling use cases. “If you need outsiders to tell you your data is valuable, you’re living in the wrong century,” says Wayne Sadin , an independent advisor and former CIO/CTO/CDO.
Data is even more valuable during this pandemic period, when economies are volatile, markets are uncertain, and businesses face unprecedented challenges that underscore the need for intelligent insights to guide strategic decision-making. “The pandemic has already accelerated many organizations’ digital transformation programs, and in many cases, data has emerged as an invaluable component of the successes of the modern-day enterprise,” notes Sridhar Iyengar, managing director at Zoho. “Those businesses which are not already leaning on data insights risk being left behind.”
Where to start?
The first step to driving a data-first modernization strategy? Defining and seeking clarity on how to value data as an asset. This should be followed up by formulation of a comprehensive data strategy, advises Alvin Foo, co-founder of DAOventures. As part of this process, organizations need to take a full inventory of data, using that exercise to identify both short- and long-term opportunities. “Some data will help with operational improvements; other data will help with innovation and market-facing activities,” explains Jonathan Reichental, a best-selling author and professor.
Key to the process is creating a decentralized data store where data consumers can access a real-time, shared view of the data when they need it, in a format that makes sense for their work. “Reducing the time, complexity, and cost of moving data to centralized locations creates the speed and scale organizations and their people need in order to effectively tap into the potential value of data,” says Gene DeLibero, chief strategy officer at GeekHive.com.
All aboard for metrics
Tristan Pollock, host of community at CTO.ai, is among the many data experts advising companies to devote time to creating appropriate benchmarks and metrics specific to a company’s strategic objectives — an exercise many organizations have overlooked as they build out data analytics initiatives.
One simple way to build organizational momentum around the process is to identify the most critical data asset and estimate the direct costs the organization would incur if that data were unavailable even for a single hour during the normal business day. As part of the calculation, adjust costs upwards based on applicable regulatory or statutory penalties that would be levied if the data were disclosed, taking into account other costs associated with potential civil or criminal lawsuits or perhaps even the cost of reputational harm.
“This will vary by industry and size of business,” notes Kayne McGladrey, cybersecurity strategist at Ascent Solutions. “A social media company losing control of their content for an hour has a very different risk profile than a manufacturing company being unable to manufacture products.”
Most data initiatives that fail do so for one of three common reasons: There’s a disconnect in terms of what data is necessary to meet business objectives, the data that’s extracted can’t be applied to the business problem as is without great expense, or the general user base isn’t taught how to apply the data.
But there’s good news: Those issues can be mitigated if organizations take an outside-in approach to formulating a data strategy. “The real value of data is measured by its utility to the stakeholders, not the data generators,” explains Frank Cutitta, CEO and founder of Health Tech Decisions Lab. “Simply bombarding people with data not only decreases value but also increases what may be described as ‘datanoia’ and a distrust of future data initiatives.”
In fact, experts caution against becoming overly fixated on the amount of the data. What’s important is not the quantity but rather the quality of the data and the set of tools that enables business users to leverage it, according to tech influencer Elitsa Krumova. Sarah Ramsingh, quantum computing engineer, agrees that not all data serves equally and advises companies to strategize on both macro and micro goals to zero in on what’s important.
“No amount of data matters if it’s not providing a measurable impact,” adds Howard Getson, CEO of Capitalogix. “It needs to be measurable, understandable, and pertinent to your business goals, or it’s a distraction.”
Harvesting data that’s no longer needed can also open the door to disaster in the event of a data breach. “Your data can be your best friend or worst enemy,” says Nick Gonzales, PR manager at Zscaler. “Your business could be hanging onto a trove of dark data that you might not even know exists.”
Others, such as Steven M. Prentice, a speaker and author, disagree. They argue that any type of data must be prized as currency, given that it can be continually repurposed, which makes its utility long-lasting. “Companies must discard the notion that an expired credit card number or the email address of an ex-employee is dead and useless,” Prentice claims. “The real value of data is that it is permanently useful to everyone who comes in contact with it.”
Tapping today’s technologies
In either case, technologies such as artificial intelligence (AI) and machine learning (ML) can provide a framework for evaluating data’s utility and, by extension, its value. Connecting business metrics to model metrics helps manage expected returns on AI projects and leads to a stronger understanding of data’s value, says Vin Vashishta, an AI strategist and chief data scientist.
AI also helps filter out redundant data, which increases its overall value to the organization. “The more unique data is in terms of scarcity, the less likely that competitors can also utilize or sell that data to others, making it more valuable,” contends Scott Schober, president, and CEO of Berkeley Varitronics Systems.
Beyond data strategy and metrics, it’s important to put the tools, processes, training, and feedback mechanisms in place so people can tap into the data for analysis and reporting, contends Will Kelly, technical marketing manager for a container startup. Developing a pipeline that automates and connects various data sources will ensure proper ingestion and refinement. This process is never fully complete but, rather, should be viewed as a continuous process as data sources change or new ones are added, advises Mike D. Kail, a CTO.
Although all of these practices are essential for realizing data’s true value, companies must also understand that societal expectations have changed and that data must play a role in doing social good. “Society is no longer willing to see corporations using data to become more profitable regardless of negative impacts,” says Debra Ruh, CEO of Ruh Global IMPACT and co-founder and chairwoman of Billion Strong. “Expectations that corporations use data to make the world a better