Imagine an oil company that doesn’t know how many barrels of oil it holds in inventory or the quality of the oil in those barrels. Or even if some of those barrels are leaking.
In the era where data is celebrated as “the new oil” and executives feel the pressure to extract maximum value from data, that is exactly where many companies find themselves. They know they are creating, accessing, capturing, and replicating vast amounts of data. Too often, however, they have little way of guaranteeing its quality and integrity. The result is a data trust gap that undermines decision-making at all levels.
A study by KPMG found that two-thirds of senior executives have either reservations about or active mistrust in their own organizations’ data and analytics.1 And that’s a picture mirrored by PwC research, which shows data owners have major concerns not just about data theft and leakage (34%) but about the intrinsic quality of data (34%) that they can access – and its integrity (31%).2
Addressing that deficit in data trust has never been more important – or complex. The security, reliability, and quality of data determines the value of the applications and services that drive and enrich business, society, and our personal lives. And because data is now increasingly generated and used by many billions of embedded IoT devices, as well as becoming the raw material for AI machine learning, establishing trust in data is vitally important.