Data Governance & Trust: Fueling Analytic Performance
As the big data conversation matures, it’s starting to sound a bit familiar. Words like “governance” and “trust” are becoming more prevalent as organizations move from big data theory to practice.
Organizations see huge opportunity in big data analytics, but they’re finding that insights are often met by questions – namely, where the data came from.
David Corrigan, director of product marketing for IBM InfoSphere, recently dug into the relationships between data, trust, governance and analytics. In this short podcast, he explained why information is now more of an asset than ever before. With analytics, we can unlock a lot of data’s inherent value, but we must make sure we’re treating data right.
Basically, Corrigan explained: "When organizations don't have trust in their information, they don't act upon it.”
Data inherently has some level of uncertainty, and that level increases as you add more and more sources. Thus, when running analytics or uncovering insights, you need to understand where the data came from, and how reliable the data actually is. Is it accurate? Is it secure? These basic data governance challenges are growing more important.
As Corrigan noted, "Analytic applications are like a fun-to-drive sports car. But if you put in the wrong fuel, it's not going to perform. Information integration and governance is the right fuel for analytics."
Data lifecycle management, master data management, data masking and profiling are all vital parts of the story, and Corrigan briefly explains how each fits into the greater puzzle.
But when it comes down to it, “People decide whether or not to trust data when they use it” – something we should all keep in mind as we look at analytics applications and try to apply the insights we’ve found among our data.