While the value of historical and descriptive analytics persists, the balance has been tipped towards more predictive and prescriptive analytics, according to a new report from Machina Research.
For many decades, enterprises have solidly built their knowledge, strategic insights and processes around well-established approaches to data management and analytics.
Terms such as ETL (extract-transform-load), data warehouses, data-marts and business intelligence became solid ground on which to build strategic and business approaches and decisions.
With millions of connected devices providing real-time data about the physical world as it is, data management and analytics processes have been inundated with new requirements and opportunities.
Machina Research said business and strategic decisions are being augmented with highly operational and predictive/prescriptive analytics, shifting the ground from “look at what happened” to “what may happen” and how best to address those potential scenarios.
“One of the more significant developments as part of, and in parallel to, developments in IoT, is the approach of two different ‘waves’ in data management—Big Data and Fast Data,” said Emil Berthelsen, principal analyst at Machina Research.
“Both are characterized by scale and speed, and the combination or aggregation of these two waves have led to significant changes and new requirements on data management technologies,” said Berthelsen.
He said the landscape of IoT data and analytics is certainly evolving and will include a new age of machine learning, augmented insights and managed autonomy, as well as a new set of enabling technologies and data governance tools.
