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Data time machine - A then and now comparison to data quality challenges

As organizations we are facing exactly the same problem with data today that we faced over 15 years ago, the quite uncomfortable experience of presenting less than accurate reports to the C-level forced us to take immediate steps to manually clean up our CRM system and scrub our data. This required an enormous amount of patience, resources and commitment as we lacked the specific data quality and enrichment tools available today. Had we had this white paper back then, we would have saved a lot of time, money, headaches and embarrassment.

Thu Nov 16 10:47:00 EST 2017
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As I read the Forbes Insights Whitepaper: The Data Differentiator - How Improving Data Quality Improves Business, I am struck by the fact that as organizations we are facing exactly the same problem with data today that we faced over 15 years ago;

Data accuracy is a challenge.  We can’t always trust what we think the data is telling us.

Back in 2002, sitting with our CEO, COO, Head of Sales and the business analyst, we reviewed marketing influenced billings reports and lead data.  Our goal was to use hard data and analytics to make better business decisions rather than rely on gut instinct.  Like all marketers, we wanted to better understand the ROI that our programs were producing and present those results in a meaningful way.   After presenting charts on the results from various marketing campaigns, the discussion led to the overall quality of the data behind the analysis and our level of confidence around it.

“Can we really trust this report knowing that the data in our CRM system has a lot of duplicates?”(No)

“Does our product data connect to our transaction data in an accurate manner?”  (No)

“Do the points of contact in our CRM represent our target customer? (No – procurement).” 

Needless to say, we were deflated.  That lack of confidence around the data being presented threw the entire meeting into a tail spin. We went from being innocent bystanders to real victims of bad data.  We started to wonder what impact poor data quality caused us - missed opportunities, lost revenue, customer churn and possibly reputational damage. 

Today, years later, companies everywhere still struggle with this same problem every day – overall confidence in data is lacking and there is apprehension in making decisions because of it.  According to KPMG’s “2016 Global CEO Outlook,” 84% of CEOs are concerned about the quality of data they’re basing decisions on —and when there’s a lack of trust in data quality, confidence in the results it provides is quickly eroded.

Our quite uncomfortable experience of presenting less than accurate reports to the C-level forced us to take immediate steps to manually clean up our CRM system and scrub our data.  This required an enormous amount of patience, resources and commitment as we lacked the specific data quality and enrichment tools available today. Had we had this white paper back then, we would have saved a lot of time, money, headaches and embarrassment. 

The good news for companies experiencing similar pain is that better data quality is within reach. In fact, not only can you have a true single view of a customer, you can enhance the data to make it more meaningful and relevant to your business objectives.  By enriching your data, you go further than customer 360 and understand the relationships between people, places and how they’re connected. Joe Francica, Managing Director of Location Intelligence at Pitney Bowes, says it best in this paper.  “If your company puts a premium on correcting addresses, location and other contextual data, your information will be inherently better and you’ll have a unique competitive advantage”.

I am hoping this white paper can save its readers from the hard lessons we had to learn.  Heed the advice - you can have clean, high quality, enhanced data today. You just need to make it a priority and know where to start.  These resources are a great first step.

Read the white paper

Learn from the Experts:  Replay the webinar The Data Differentiator: How Improving Data Quality Can Improve Your Business.