Experts Weigh In on Data-First Modernization
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.”
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.
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.
