Thomas Davenport, the author of Competing on Analytics, writes that Analytics 1.0 was focused on Business Intelligence derived from internal systems. Analytics 2.0 leveraged user generated data and clustering technologies to spot trends and preferences. Analytics 3.0 includes all of the above and adds machine generated data while democratizing analytics consumption and extending from IT to line of business and even customers.
MIT's Andrew McAfee studied 330 public companies and found that "the more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results. In particular, companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors. This performance difference remained robust after accounting for the contributions of labor, capital, purchased services, and traditional IT investment. It was statistically significant and economically important and was reflected in measurable increases in stock market valuations."
After almost two decades in the Business Intelligence and Data Warehouse space, we know that merely having the right answer isn't enough. To act on the data, data authors and consumers both need a base understanding of which data products are meaningful, where they came from, what they mean. A new wave of self-service BI tools can empower line of business with ad hoc capabilities while increasing their confidence and communication with IT data offerings.
Our Data Fluency Assesment