What you don’t know about data management could kill your business
In reality MDM (master data management) means Major Data Mess at most large firms, the end result of 20-plus years of throwing data into data warehouses and data lakes without a comprehensive data strategy. Moving forward IT leaders are going to have to find some way to clean up what are essentially legacy data septic tanks.
At a recent conference, the editor of a major business publication invoked Chatham House rule prior to asking the approximately 250 senior executives in the room how many had what they considered a “coherent data strategy”? Seven individuals raised their hands.
Contributing to the general lack of data about data is complexity. There are many places in the enterprise where data spend happens. Individual business units buy data from third parties, for example. Taking enterprise-wide inventory of all the data feeds being purchased and getting an accurate picture of how all that purchased data is being put to use would be a good first step.
The reality is that a significant portion of the data sloshing about modern enterprises is replicated in multiple locations, poorly classified, idiosyncratically defined, locked in closed platforms, and trapped in local business processes. Data needs to be made more liquid in the way of an asset portfolio — that is, transformed to ease data asset reuse and recombination.
I conducted a survey of major cloud providers asking where the chief data officers they were working with were spending their time. Anywhere between 50% to 70% of the CDOs’ time is being spent on people issues, such as ownership of data in silos, according to those providers. Breaking down those data silos is yet another data management issue.
The payoff of data management
What we do know is that investments in data are substantial. Estimates vary widely, with data spend being pegged at anywhere between 10% and 57% of total IT budgets. Based on its analysis, McKinsey has concluded that a midsize institution with $5 billion of operating costs spends more than $250 million on data across third-party data sourcing, architecture, governance, and consumption.
