Data Science

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Image courtesy of Flickr user Nathan Eal Photography.

Why Companies Have to Trade “Perfect Data” for “Fast Info”

Companies have been trained to think about data all wrong, say Attivio’s Ali Riaz and Sid Probstein. “Analytics don't have to be based on super-precise data,” they say. “The report doesn't have to be perfect. It needs to capture the behavior, not the totality of it."

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Management by Maxim: How Business and IT Managers Can Create IT Infrastructures

Creating a business-driven IT infrastructure requires that executives thoroughly understand their firm's strategic context. By formulating a series of business and IT maxims -- short simple statements of the business's positions -- managers can identify the IT infrastructure service suited to their company. Organizational, political, cultural, and reward system issues, as well as a lack of IT leadership, may form implementation barriers.

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Use Strategic Market Models to Predict Customer Behavior

Positioning products in a complex market is one of a company’s hardest decisions. In determining whether to combine or maintain separate product lines, Hewlett-Packard used strategic market modeling (SMM) to design “what if” scenarios and run simulations forecasting market behavior. SMM combines demographics, user needs and competitive-perception data into a database for testing alternative positioning strategies. The author describes SMM’s development and the lessons learned.

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Improve Data Quality for Competitive Advantage

ERRORS IN DATA CAN COST A COMPANY MILLIONS OF DOLLARS, ALIENATE CUSTOMERS, AND MAKE IMPLEMENTING NEW STRATEGIES DIFFICULT OR IMPOSSIBLE. The author describes a process AT&T uses to recognize poor data and improve their quality. He proposes a three-step method for identifying data-quality problems, treating data as an asset, and applying quality systems to the processes that create data.

Showing 21-31 of 31