“Data is not smart in its own right; rather, it’s only smart if it drives smart decisions,” says James Kobielus, IBM’s Big Data evangelist, delivering the keynote address at the DATAVERSITY® Smart Data Online 2016 Conference. He considers his core focus, Big Data, as “a subset of the broader concept of Smart Data.”
Kobielus says that Smart Data is a strategy that sets the groundwork for smart decisions, rather than a particular type of data. “It’s a particular set of practices for how you can leverage your data for greater insights in a wider range of scenarios,” allowing smart decisions to flow organically from data that meets a range of criteria. Big Data is scalable, fast, and comprehensive, and because there are insights that are only available on a larger scale, in that respect, he says “Big Data isSmart Data.”
To be considered Smart Data, your data should enable insights that are trusted, contextual, relevant, cognitive, predictive, and consumable. Combining the attributes of Big Data with those of Smart Data can help you “harness the power of your data” and drive decisions more effectively.
What Really is Big Data?
Big Data has high volumes, travels at high velocity, and offers high variety, Kobielus says. If your data is scalable, it can provide a more powerful exploratory repository, allowing for a broader historical perspective. A higher volume provides an opportunity to see things at greater scale that you can’t see at low volumes, he says, like micro-segmentation of target populations, or fine-grained, second-by-second behavioral analysis.
He goes on to say that because Big Data moves at high velocities, it’s possible to achieve a granularity in terms of time that can’t be seen at the batch level. The more quickly data can be ingested, the more quickly questions can be posed, and the closer to real-time insights the data can provide. The more varied and comprehensive your data is, the more easily you can have a 360-degree view of any topic. By getting data from a variety of sources and formats – historical profiles, clickstream, geospatial data, social sentiment, from mobile devices, he says, it’s possible to fill in “a fine-grained portrait of who [your customers] are, what they’re doing, and what they’re likely to do.”
Kobielus remarks, “Big Data is a natural consequence of what I call ‘ravenous analytics.’” By asking more questions of the data, the natural impulse is to aggregate and correlate a broader range of data sources, and Big Data is a natural result.
Kobielus defines Smart Data as a superset of Big Data. It is the “ability to achieve big insights from trusted, contextualized, relevant, cognitive, predictive and consumable data at any scale, great or small.” He says that even small data can drive for smart decisions if it has these attributes.
The more consolidated, conformed, cleansed, consistent, and current your data is, the more likely you are to make the best decisions, he says. It stands as a single version of truth, the fourth V in the taxonomy of Big Data: Veracity. Kobielus says there’s a need for “a repository in your data environment where officially sanctioned systems of record are consolidated, after they’ve undergone a process of profiling, matching, merging, correction and augmentation. So it’s all about Data Governance and Master Data Management and working with a single version of truth.