11 Best Practices for Business Intelligence

 
 
By Dennis McCafferty  |  Posted 02-02-2016 Email
 
 
 
 
 
 
 
 
 
 

Interest in business intelligence (BI) is surging, as big data is expected to explode into a $50 billion market in 2015—nearly doubling its current size. Why not, when BI supports so many business-critical functions, such as analytics, business performance management, text mining and predictive analytics. And while BI presents virtually limitless potential with regard to transforming immense volumes of data into organization-benefiting intelligence, there are many ways for these projects to fail. To help prevent this, we're presenting the following 11 best practices for BI. They were adopted from a number of online resources, primarily those posted at—appropriately enough—Business Intelligence Best Practices, an online collaborative and interactive forum for the BI and data warehousing community. The resulting takeaways cover everything from BI project funding to solution performance to user acceptance. What's key, experts say, is to form an ongoing partnership with business, so the resulting BI solutions are embraced as easy-to-use and strategically relevant. For more about Business Intelligence Best Practices, click here

 
 
 
 
 
Dennis McCafferty is a freelance writer for Baseline Magazine.

 
 
 
 
 
 

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