For many years, companies across the globe have sought to capitalise on the wonderous possibilities of data. Underpinned by technological innovation and rising digital adoption, businesses are actively exploring the boundaries of what their data can do, both for their customers and internal operations.
As mentioned in our previous report, today’s data explosion has created significant fears of missing out (“FOMO”) amongst many senior executives. In response, corporations’ data investments have surged, with global dataspecific technology spending reaching USD 474.7 billion in 2020 and projected to grow to USD 639 billion by 2024. However, we believe that many firms have gotten lost in the hype, neglecting the fundamentals of a data project in exchange for empty promises and wasted investments.
The significant wastage in data-centric projects reflects two key problems: (1) most data is left unused, untapped, or unknown to a business (i.e. “dark data”); and (2) the cost of recovering and making flawed data (i.e. “imperfect data”) ready for use is extremely high. In fact, dark data accounts for 55% of data expenditure wastage, costing companies USD 193 billion in 2020. However, this pales in comparison to the recovery costs of imperfect data.
The fact is, only 3% of data within organisations are “perfect” and ready for processing. The remaining 97% requires some form of manipulation to make it ready for use, which expends 10x more effort (and cost) as opposed to vanilla maintenance costs. We estimate that the costs of making imperfect data usable amounted to USD 1.5 trillion in 2020 alone.
Wastage stems from multiple areas, including a lack of understanding of a company’s data value chain, mismatched technology and business needs, inadequate integration of siloed data systems, and failed transformation planning. However, the vast majority of this wastage is attributed to one basic theme: the lack of a defined organisational data strategy. At its core, this consists of two complementary components: the (1) Business, Application, Information & Technology (“BAIT”) framework and; (2) a robust change management strategy.
The BAIT framework outlines steps from project formulation to execution, together with key action items. The change management strategy outlines appropriate policies and incentives to develop a data-centric culture that unifies and maximises data investments throughout the lifecycle of a data strategy project. Both components can be scaled up or down, depending on the type of project: namely, strategic, tactical, or operational. The type of project is determined by considerations such as resources, time, or infrastructure required. These components rightly consume considerable airtime and focus. But there is one centrepiece that holds an entire data strategy project together that is often neglected. And that is a fundamental business strategy.
It is our belief that most data strategy projects fail because data’s place as an integral part of the larger business is often overlooked. So many executives have been caught up in the hype behind data that they shoehorn it into a company’s operations without detailed analysis as to “why” they are doing so; or worse, do so simply to appease shareholders.
At the end of the day, technology is guided by the business’s objectives and likewise limited by it as well. A solid foundation in basic data operations must exist before data can be allowed to guide any business. Much like a language’s primary purpose is to convey ideas and facilitate communication, it must first have a solid foundation of rules and structure before being able to evolve and guide the language as a whole over time. After all, data is the real lingua franca of business.