Big data means more data – from more sources, in more formats. It also involves data that is created at a more rapid pace. All of those factors make it harder to establish context. Where did this data come from? How much do you trust it? What steps were taken to correct, or massage, the data?
Protecting and security sensitive big data is necessary to ensure data is shared for new forms of analysis. Before the owners of that data will share it (yes, political silos still exist, and yes individuals still feel they own data and can say no to sharing it), they want to ensure it is
Big data is about much more than scaling, accelerating and broadening your analytic applications. Without trustworthy data to fuel it all, you can’t have much confidence in the hidden patterns that big data reveals, the decisions it drives, or the outcomes that you may or may not achieve from it
At a recent IBM event focused on building confidence in big data, a highlight was a wide-ranging discussion by a panel with very diverse backgrounds and industries. The panel comprised an industry analyst with an information governance specialty (Michele Goetz of Forrester Research), a chief data
Confidence in big data is highly variable. Some data sources have inherent uncertainty. So why shouldn’t you spend as much time as needed to make big data perfect? Time. You simply don’t have enough time to sort out every data irregularity, every ambiguity, every incomplete attribute. And for many
Confidence (aka Veracity) in Big Data is one of the central themes that has emerged recently in the Information Governance community. It is based on the growing awareness that when using Big Data there may not be the Confidence in its quality, lineage or accuracy that exists with more conventional
Openness is where the world is headed. It’s the core principle in truly agile governance of a dynamic, complex, smarter planet.
Openness means many things to many people. From a big-data perspective, we can break down the key dimensions of openness as follows:
Open platform and ecosystems: Open-
Confidence in big data is essential. Without confidence, decision-makers may not act on big data insights – which would completely negate the benefits of big data in the first place. Understanding the level of confidence in big data, and selectively improving confidence to the required level,
Most organizations today still treat data as a raw material to be mined, with industrial processes for staged production. Organizations invest millions in capturing, refining and governing the use of information as an attribute of business activity. These data attributes describe physical products
Data security rules have changed in the age of big data. The V-Force (Volume, Velocity and Variety) has changed the landscape for data processing and storage in many organizations. Organizations are collecting, analyzing and making decisions based on analysis of massive amounts of data sets from
Meet Mr. Confident and Mr. Not-So-Sure. Both are living in a big data world, but only Mr. Confident is succeeding. Mr. Not-So-Sure struggles with understanding the context, completeness and risk associated with data. On the other hand, Mr. Confident utilizes trusted insights from the growing volume
In the world of information management, we think a lot about the critical connection between good information and good decisions. After all, we know that the very best analytics, when applied to information that is inaccurate and out of date, can lead to decisions that send a business in the wrong