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An article from MIT entitled "Big Data, Analytics and the Path From Insights to Value" explains how organizations use data and analytics to create competitive advantage and become top performers. 

 

Aspirational, using analytics to justify actions, primarily in finance, operations and marketing.  Key obstacles include lack of understanding of how to obtain value from analytics, executive sponsorship, and the culture of hoarding information.  These organizations are limited in their ability to gather, aggregate and analyze information, and frequently make decisions based on intuition rather than robust data and rigorous analysis.

  

Experienced, using analytics increasingly for strategy and business development.  Their primary use of analytics is for revenue growth, and major limitations are lack of technical skills for the analytics, as well as lack of effective data governance and ownership policies.  They have developed processes for capturing in analyzing data, but have limited capabilities to share it on a wide scale.

  

Transformed, using analytics to prescribe actions rather than to justify them.  These organizations use their analytical capabilities to create a competitive advantage over others who can’t extract knowledge from their data, or act as quickly upon it.  The major limitations of these organizations are lack of management bandwidth to competing priorities, and accessibility of the data continues to be an issue.  However, they have developed strong abilities to capture and analyze the data, as well as to share it. 

 

Specific Steps in Implementing Analytics 

First, don’t start with the data.  Instead, start with an organizational challenge that can be solved by improved analytics.  Make sure that the analytics achieve a specific business purpose, such that “quick win” can be demonstrated. 


Target the specific data necessary to meet this challenge, using the most readily available data.  Limit the scope of data collection, if necessary, in order to produce a timely result.  More comprehensive approaches that assemble large masses of data and spend significant time cleaning it up often lose momentum before results can be achieved. 


Don’t try to use yesterday’s technologies to present to tomorrow’s results.  Data visualizations, such as dashboards and scorecards, simulations, and other techniques that enhance visualization of analytical results can pay big dividends in creating value.


Also, don’t attempt to replace your existing analytics capabilities with new ones.  As new capabilities are developed, existing one should continue to be supported.

 

Finally, while great successes can come in selecting specific areas having the most potential for benefit from analytics, don’t lose track of the big picture. 

 

Make sure you know how each piece of the analytic system fits together, by developing information governance policies data architectures, and analytical tool kits that will be commonly applied throughout the organization. 

See: http://bit.ly/LeHR7N

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Tags: Adoption, Analytics, Data, Stages, of

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