Rosslyn Analytics Launches Enterprise Data Discovery Maturity Model; A New Framework to Help Companies Compete On Analytics

25 July 2012

Rosslyn Analytics has launched a self-assessment enterprise data discovery maturity model to help companies outperform their competition using cloud analytics. By answering a few questions about their data use, businesses will find out their maturity in data handling and what they should do to improve. They’ll then be able to extend the life of, and future-proof, their business intelligence and data visualization solutions by exploiting the value of enterprise data.

Elite companies know that the future of decision-making lies in the intelligent use of big data:

  • McKinsey estimates that 15 out of 17 sectors in the US have more data stored per company than the US Library of Congress yet are not exploiting it;
  • IBM calculates that businesses leaders that invest in giving their knowledge workers the tools to interact with data are 2.2 times more likely to outperform their peers.

“High performance companies are using analytics to unlock the insights found in data by properly organizing, analyzing and sharing data with colleagues,” said Charlie Clark, CEO of Rosslyn Analytics. “Rosslyn Analytics has developed a new enterprise data discovery maturity model because decision makers and knowledge workers have struggled for years to understand how to use technology to turn the potential of data into business value.”

Rosslyn Analytics’ data discovery maturity model shows how companies can easily leverage data to drive innovation and deliver greater cash savings, mitigate unplanned business risks and improve process efficiencies.

The industry-first maturity model also enables companies to see the potential of exploiting the value of data and to identify weak spots and areas of improvement in their analytical capabilities. The interactive maturity curve classifies organizations in one of five maturity levels (Isolator, Connector, Analyzer, Strategist, Innovator) depending on how they are currently using tools and processes to extract, categorize, enrich, analyze and collaboratively share data.

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