Streaming technologies have been around for years, but as Felix Liao recently blogged, the numbers and types of use cases that can take advantage of these technologies have now increased exponentially. I've blogged about why streaming is the most effective way to handle the volume, variety and velocity of big data. That's because it provides a faster way to gain business insights from big data than what traditional store-it-first-analyze-it-later approaches are typically capable of delivering.
I believe that it is. As I see it, using analytics to perform a rapid data quality assessment is one of the biggest overlaps between analytics and data quality – other than analytical models being better with better data. Indeed, this approach to assessing data quality has become a necessity now that we have so many data sources within and outside of the enterprise for business users to consider.
Even when you employ a reusable set of data management processes to manage data where it lives so that data quality rules are consistently applied across all data sources, it doesn’t change the fact that some sources will have higher data quality levels than others.