Published on the 12/01/2016 | Written by Beverley Head
Data analytics is about to get a whole lot more interesting as pioneering companies work out how to empower their business executives without unleashing information anarchy…
Gartner has outlined the programme for its 2016 Business Intelligence and Analytics Summit which suggests that success will require organisations to tread a very careful path. While business users need a degree of autonomy in order to identify and apply insights from data, some form of analytics tzar will be needed to have oversight and governance of the process and data bank in order to ensure information anarchy is avoided.
This becomes increasingly important as organisations roll out data supported self-service options for users, and bring together data from multiple sources to deliver organisational insight and automated algorithm based process control. It’s going to be an issue for both private enterprise and Government entities as both look to harness so called “virtual assistants” that use technologies such as IBM’s Watson to provide online self-service options.
The advent of the automated business will also have a significant impact on the way information systems are developed according to MapR Technologies’ CEO and Cofounder John Schroeder. He said this will require a rethink in terms of architecture and infrastructure.
For the last few decades, the accepted best practice has been to keep operational and analytic systems separate, in order to prevent analytic workloads from disrupting operational processing, said Schroeder.
HTAP (Hybrid Transaction / Analytical Processing) was coined in early 2014 by Gartner to describe a new generation of data platforms that can perform both online transaction processing and online analytical processing without requiring data duplication.
“In 2016, we will see converged approaches become mainstream as leading companies reap the benefits of combining production workloads with analytics to adjust quickly to changing customer preferences, competitive pressures, and business conditions. This convergence speeds the “data to action” cycle for organisations and removes the time lag between analytics and business impact,” Schroeder added.
He also predicted that there would be a move towards more distributed data processing to support big data and analytics initiatives.
