Successful Digital Transformation Requires Data Transformation

Whether or not an organization has launched a formal digital transformation initiative, there’s no doubt most business operations are now inseparable from the IT infrastructure on which they run. As a result, technological advances, if properly managed, can translate directly into business advances.

Many companies, however, are struggling to bridge the gap that exists between their existing IT infrastructures and practices and the value that new digital technologies make possible.

Fortunately, there is a clear way to optimize digital transformation efforts: focus on the data. Indeed, there’s a good case to be made that digital transformation is likely to fall short unless it is based on a solid foundation of “data transformation.”

In this context, data transformation doesn’t just encompass the traditional “extract, transform, load” processes of collecting, cleaning, reformatting, and storing data. It also includes the subsequent analysis and leverage of collected (or real-time) data to inform a company’s decision making, its operations, and its high-level digital transformation strategies.

Everyone agrees that the massive amounts of digital data generated by business and consumer activity represents an incredibly valuable resource – at least theoretically. In practice, however, the ever-expanding data resource is underutilized today.

In a survey of 190 U.S. executives, Accenture found that only 32% can realize tangible and measurable value from data. Even fewer – 27% – said data and analytics projects produce insights and recommendations that are highly actionable.

Without data-driven insights, digital transformation initiatives are flying blind. By contrast, organizations that make good use of data can achieve a range of benefits. According to a recent Qlik assessment, the top four areas that can achieve impressive returns on investment via data-driven transformation include:

• Deeper customer intelligence – building detailed profiles of customer needs, wants, and trends, then using that understanding to design new products, shift supply chain flows, and improve customer experiences and loyalty.
• Reimagined processes – identifying inefficient business processes and instituting optimized workflow replacements to speed cycle times, optimize the supply chain, and control costs.
• New business opportunities – spotting emerging threats, opportunities, and trends and using that knowledge to shift business priorities, enter new markets, and make other strategic decisions.
• Balanced risk and reward – simulating future market scenarios, tracking and maintaining compliance with global regulations, driving down carbon footprints, and performing other analyses to achieve maximum benefits with minimum risk.

Still, a common problem for many companies and their data scientists is the day-to-day collection, cleaning, organizing, and management of data – tasks so time-consuming that little time remains to analyze and leverage the data. Some studies have estimated that data scientists traditionally spent as much as 80% of their time on such data housekeeping tasks.

In recent years, however – even as data volumes and sources have multiplied – new generations of tools have simplified and automated many of the most time-consuming data management tasks. Other solutions can help analyze massive amounts of data, often by using machine learning and other artificial intelligence technologies.

Today’s companies may find themselves buffeted in a sea of digital transformation currents—but they can use this new-generation of data management tools to build a solid, data-based ground on which to move forward.

For information about how Qlik’s end-to-end, real-time data integration and analytics solutions can help your organization transform data into value see.