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February 12, 2013

‘Invisible’ Analytics Get Better Big Data Results

By Marie Lingblom

The last couple of years featured lots of big numbers and discussion about the explosion of data and a theoretical gold mine of intelligence awaiting enterprises best-equipped to glean competitive information from it.

This year the conversation shifts, as Matt Arika of McKinsey & Company puts it, from strategy about what Big Data is to how it improves performance metrics in enterprise and industrial applications.

Big Data is also pegged by McKinsey and plenty of others as a key basis of competition, underpinning waves of productivity, growth and innovation — as long as the right enablers and policies are in place.

Business intelligence and analytics continues to be a top CIO investment priority, yet user surveys by Gartner show just 30 percent of potential users in an organization adopt CIO-sponsored analytics tools. Rita Sallam, research vice president at Gartner, says that’s shifting as enterprises invest in making analytics “invisible” — more consumable and accessible — to the nontraditional analytics user.

The challenge, says Sallam: Companies have far more data than people have time. The more pervasively analytics are deployed to business users, customers and consumers, the greater the impact in real time on business activities, competitiveness, innovation and productivity.

Gartner identifies three key challenges for analytics and business intelligence professionals to consider this year, along with advice on how to tackle them:

Make analytics more invisible through easy, natural language interfaces for exploring data, and via embedded analytic applications at the point of decision or action.
The friendlier, more transparent and, therefore, more invisible the analytics are to users, the more broadly they will be adopted — particularly by users that have never used BI tools or understand the impact analytics can have on business activities.

Moving toward something that’s invisible, says Gartner, will require a great deal of computing power. Begin by identifying targeted data exploration and high-value decision-making opportunities.

The growing volume of data and less time for decisions are driving companies to implement real-time operational intelligence systems that make supervisors and operations staff more effective.Virtually all the event data available to (non-machine) recipients — even news feeds, e-mail, tweets and other unstructured data — is digital so software tools can process it.

Organizations should offload event-data capture, filtering, mathematical calculations and pattern detection to real-time operational intelligence software. Where the cause and sequence of events are understood, says Gartner, leading indicators can predict situations of threat or opportunity. Where not possible, the system can improve outcomes by reducing lag time between events and responses.

Increasing competition, cost and regulatory pressures will motivate business leaders to adopt more prescriptive analytics — making business decisions smarter and more repeatable and reducing personnel costs.

Adoption of decision management software technologies is one response, as well as more sophisticated forms of these technologies.

Solutions architects should work with business analysts, subject matter experts and business managers. This approach allows a better and broad understanding that will allow computers to make decisions that are structured and repeatable. If done right, that frees time for thinking and actions that computers can’t do.

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