How does the implementation of an enterprise wide data and analytics strategy

Although the ability to manage torrents of data has become crucial to companies’ success, most organizations remain badly behind the curve. More than 70% of employees have access to data they should not. Data breaches are common, rogue data sets propagate in silos, and companies’ data technology often isn’t up to the demands put on it.

In this article, the authors describe a framework for building a robust data strategy that can be applied across industries and levels of data maturity. The framework will help managers clarify the primary purpose of their data, whether “defensive” or “offensive.” Data defense is about minimizing downside risk: ensuring compliance with regulations, using analytics to detect and limit fraud, and building systems to prevent theft. Data offense focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction.

Using this approach, managers can design their data-management activities to support their company’s overall strategy.

To remain competitive, companies must wisely manage quantities of data. But data theft is common, flawed or duplicate data sets exist within organizations, and IT is often behind the curve.

Companies need a coherent strategy that strikes the proper balance between two types of data management: defensive, such as security and governance, and offensive, such as predictive analytics.

Regardless of its industry, a company’s data strategy is rarely static; typically, a chief data officer is in charge of ensuring that it dynamically adjusts as competitive pressures and overall corporate strategy shift.

More than ever, the ability to manage torrents of data is critical to a company’s success. But even with the emergence of data-management functions and chief data officers (CDOs), most companies remain badly behind the curve. Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all. More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data. Data breaches are common, rogue data sets propagate in silos, and companies’ data technology often isn’t up to the demands put on it.

A version of this article appeared in the May–June 2017 issue (pp.112–121) of Harvard Business Review.

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An enterprise analytics strategy assists organizations in determining which tools and approaches they will need to work with these large data sets and extract valuable insights that can be used to make business decisions. It needs to be able to change over time to meet evolving business needs.

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An enterprise analytics strategy assists organizations in determining which tools and approaches they will need to work with these large data sets and extract valuable insights that can be used to make business decisions. It needs to be able to change over time to meet evolving business needs.

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Please log in or register to answer this question.

An enterprise analytics strategy assists organizations in determining which tools and approaches they will need to work with these large data sets and extract valuable insights that can be used to make business decisions. It needs to be able to change over time to meet evolving business needs.

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