He Who Rules The Data, Rules The World: A Brief History Of Data Governance
Data rules the world, but who rules the data? The companies that collect it? The servers that store it? The cables and satellites that transmit it? Or the laws that keep it flowing into the right hands—and away from the wrong ones?
Welcome to the world of data governance. It’s one of the most important (and overlooked) practices driving business intelligence today, but to return maximal value, it relies on one thing: smart master data management.
At its core, data governance is a set of processes that allows an enterprise to formally manage important data assets. When IT applies logical and flexible controls to those assets, the enterprise trusts that the right information is flowing to the right person at the right time.
If the process sounds simple, it’s because of years of savvy back-end systems development. A decade ago, data governance was an emerging trend in enterprise information management—unsurprising, considering the time period. E-commerce had just taken off, social media was in its infancy, and Apple was introducing the iPhone. Data was only starting to be everywhere, but it was already disruptive.
Today data is a buzzword, though the difference between “big data” and Big Data is starting to blur. Two-thirds of businesses have undergone, or are preparing to undergo, a full digital transformation, enabling them to capture every bit of customer commerce and communication. When information from marketing campaigns, customer service inquiries, and purchase histories can be tagged and encoded, data is an enterprise’s lifeblood.
What do thriving businesses do with data? Harness it for Business Intelligence (BI). According to a recent study, 81% of 400-plus senior executives from industries across the globe have had “significant” or “very significant” success with their BI programs.
Yet the executives have one big worry: that data quality and inconsistencies will impede further BI insights. When running predictive analytics, for instance, projections must be based on complete and correct data. If the data is not complete and correct then projections will only provide limited value, and can actually create negative value by falsely amplifying results across all data sets.
As the study suggests, the solution is stronger data governance. Data governance systematizes different rules for different data sets, giving different capabilities to different departments. It determines permissions to access, change, and analyze data.
