Without a strong data management strategy, an organization is headed for disaster. Regretfully, based on the fundamental misunderstanding about the significance of solid data foundations, that translates to the majority of businesses.

IT managers, take note: If things continue the way they are probably going, your company will become the Titanic, and its data will be the iceberg. Need to take data management seriously if they want to avert the inevitable.

Naturally, data has been the buzzword of the last ten years, dubbed the “new oil” of the digital economy. Indeed, data has a great deal of potential to add value to your company, which makes gathering and analyzing it—a process known as data science—extremely exciting.

On the other end of the attention spectrum, however, is data management, which is far too often thought of as the domain of clerks and administrators, dull, and expensive.

Organizations must master data management, though, if they are to genuinely use data to produce value that lasts. This entails being exceptionally good at the often overlooked fields of data architecture and data governance. Data management needs to be rebranded as “joyous” work from an emotional, cultural, and psychological perspective, according to Milwaukee Bucks Vice President of Business Strategy and Analytics Sumathi Thiyagarajan.

Absence of information on data management

Ironically, despite all the talk about data, there is a dearth of data about data everywhere you look. For instance, a lot of businesses are unable to even estimate their data expenditures.

The analyst community is one of the bad guys in all of this. Subscription research companies and IT thought leadership centers have virtually given up on data management in favor of the Next Big Thing’s dopamine rush. There is a dearth of information regarding the amount of money being spent on data management as a result of this lack of analyst coverage.

At most large firms, MDM (master data management) actually stands for Major Data Mess, the consequence of over two decades of dumping data into data lakes and warehouses without a well-thought-out data strategy. IT directors will need to come up with a plan going forward to clean up what are effectively septic tanks of legacy data.

At a recent conference, the editor of a major business publication asked how many of the roughly 250 senior executives in attendance had what they considered to be a “coherent data strategy” before invoking the Chatham House rule. Seven people put up their hands.

Complexity is a factor in the overall dearth of data about data. The enterprise has numerous locations where data is spent. For example, certain business units purchase data from outside sources. A good place to start would be to compile an accurate picture of how all of the purchased data is being used and to take an enterprise-wide inventory of all the data feeds being purchased.

In actuality, a large amount of the data that is leaking out about contemporary businesses is duplicated across several sites, mislabeled, ill-defined, restricted to closed platforms, and ensnared in regional business procedures. To facilitate data asset reuse and recombination, data must be transformed to make it more liquid in the manner of an asset portfolio.

These providers estimate that between 50 and 70 percent of the CDOs’ time is spent on personnel-related matters, like data ownership in silos. Dismantling those data silos is an additional problem with data management.

Benefits of data management

The amount spent on data is estimated to be anywhere from 10% to 57% of all IT expenditures, although estimates differ greatly. A midsize institution with $5 billion in operating expenses spends more than $250 million on data across third-party data sourcing, architecture, governance, and consumption, according to McKinsey’s analysis.

And what does that benefit businesses?

This is the data-related catch-all article. Artificial intelligence is the Next Big Thing, but it can’t function at scale without accurate, consistent, and clean data. This will only make organizations’ discontent with the return on their data investments even more pronounced.