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Lyndsay Wise

Hi and welcome to my blog! I look forward to bringing you weekly posts about what is happening in the world of BI, CDI and marketing performance management.

About the author >

Lyndsay is the President and Founder of WiseAnalytics, an independent analyst firm specializing in business intelligence, master data management and unstructured data. For more than seven years, she has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Lyndsay conducts regular research studies, consults, writes articles and speaks about improving the value of business intelligence within organizations. She can be reached at lwise@wiseanalytics.com.

Editor's Note: More articles and resources are available in Lyndsay's BeyeNETWORK Expert Channel. Be sure to visit today!

General data quality

There are many organizations on the right track. They possess an understanding that information represents a great asset to the organization at large. The better the quality of that data, the more people can rely on it to do their jobs effectively and make decisions that are in the organization’s best interests. Although not always easy, the road to data quality is worthwhile. Sometimes hard to justify because there aren’t always direct tradeoffs that can be immediately measured, but the benefits tied to business are obvious when evaluated on a broader level. These include, but are not limited to:

  • better insights into customers
  • employee trust in data
  • the ability to save marketing dollars and not having to worry about targeting the same people/households multiple times
  • standardized part numbers/SKUs
  • better able to connect the dots between information access points
  • greater partner and supplier relationships 
  • insights into operations that can be used to ensure better planning

In essence, data supports everything we do within our organizations and how we choose to face the world. The quality of that data reflects how successful we can be and how accurate we can be. Without valid and accurate data, financials may be inaccurate, it can be impossible to properly target potential customers, and there can be as many versions of data as there are people in the company.

Data quality within BI

Because BI’s value lies in the ability to see what isn’t obvious and does so by creating correlations between disparate data, maintaining data quality is the only way to ensure success. Luckily more solution providers are starting to make this easier by providing simple functionality within their data integration processes. But organizations can’t rely on their software vendor of choice (whether for BI or for their transactional data) to get it right for them. Organizations should develop their own set of data quality standards for their BI solutions. This requires the ability to fix data at the source, or ensure that a cleansing process exists that can be automated with assigned responsibility so that issues can be addressed in a timely fashion. 

Although today the focus on data quality is still seen as optional, in the future it won’t be. Not only is the information management industry converging due to the general overlap of data related concepts, but managing data effectively over time will become a key priority as organizations begin to merge and manipulate many disparate sources that are stored in a variety of formats. The only way to ensure accurate analysis of all of these increasing complexities is by standardizing the information assets themselves.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies or opinions.

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Posted September 25, 2013 3:27 PM
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