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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

07/01
2013

Clearing the Data Analysis Hurdles

An overwhelming majority of companies note that analytics will become more critical to their successes in the future. But even so, business are still struggling with numerous challenges related to data analysis.

hurldes 150x150 Clearing the Data Analysis HurdlesIn fact, 96% of respondents to a recent Deloitte survey say that analytics will become more important to their organizations in the next three years.

Key findings from the survey include:

Ownership questions  20% of respondents say there’s no single executive responsible for data and analytics. But if there is such an individual, she’s most often the business unit leader (23%) or the chief financial officer (18%).

Skill scarcity – 42% say their employees do not have the needed analytics skills.

“Organizations will be slow to fully capitalize on the potential of analytics unless they are able to overcome several key barriers; data management and access to talent are the most problematic,” Deloitte notes in a statement. “At the same time nearly half (49%) of respondents assert that the greatest benefit of using analytics is that it is a key factor in better decision making capabilities.”

The big data talent gap – fueled by the scarcity of data scientists – has been well documented, but companies also need managers who can effectively work with data scientists to ensure that their quantitative work gets translated to strategic business decisions.

The good news is that there are ways managers can effectively work with data scientists on analytics projects, notes Thomas Davenport, senior adviser to Deloitte Analytics and visiting professor at Harvard Business School, in a recent article in Harvard Business Review.

“Start by thinking of yourself as a consumer of analytics,” Davenport advises. “The producers are the quants whose analyses and models you’ll integrate with your business experience and intuition as you make decisions. Your job as a data consumer – to generate hypotheses and determine whether results and recommendations make sense in a changing business environment – is therefore critically important.”

He suggests that managers enroll in an executive education course on statistics to become more data literate or work closely with data scientists or analysts in the company on several projects to learn how they work.

“Focus on the beginning and the end,” Davenport says. “Framing a problem – identifying it and understanding how others might have solved it in the past – is the most important stage of the analytical process for a consumer of big data. If you’re a non-quant, you should also focus on the final step in the process – presenting and communicating results to other executives – because it’s one that many quants discount or overlook and that you’ll probably have to take on yourself at some point.”

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