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Last Tuesday I met with the Vice President of a local non-profit.  She was looking for help with her organization’s CRM initiative and I figured that like so many non-profits, her organization was struggling to find the resources needed to invest in a decent system.I was wrong.

This non-profit has lots of CRM technology, multiple systems in fact.  They have tons of executive-level support, a clear understanding of what they need from their systems, and a willing and able staff of professionals to support their effort.  But they also have lots of poorly structured, duplicate, and dirty data that is compromising the value of the reports the system is generating.

Many Customer Relationship Management experts have written about the factors that are critical to the success of CRM projects.  Nearly all of these identify “data quality” as a critical element.  In 2004 the Harvard Business Review wrote a now-classic article called “CRM Done Right” that discusses data quality issues in detail.  In 2009 I wrote a blog post outlining six reasons why CRM user acceptance is still a problem, with data quality listed as one of the key reasons.

Today CRM deployments are much smoother than they used to be.  Companies have learned from the mistakes of the past.  People are smarter.  Processes are better.  The issues outlined in the Harvard Business Review don’t resonate quite as clearly as they did in 2004.

But data quality is still a thorny problem that is still not easily solved.  Why?

First, despite new data migration and scrubbing tools that can cleanse data automatically, keeping data quality high is still a largely manual process for most companies.  Because we rely on humans to keep data current and because people are busy and forgetful, data quality remains relatively low.

Second, companies are not proactively addressing the cost of poor data quality.  In some cases they invest too little and in other cases far too much, to get their data “perfect.”Third, not enough thought is going into designing business processes that generate high-quality data as a by-product of everyday activities.  Instead, CRM data update is treated as a chore to perform after the fact.

Any company with a CRM system that contains legacy data needs to ask the following questions:

1. How much historical data do we really need to keep and at what level of detail?
2. Is our data accurate enough to guide meaningful business decisions?  If not, can we fix the data or should we get rid of it?
3. Are we spending time and money generating data that is either not used, or not accurate enough to be meaningful?
4. How timely does our data need to be?  What is the additional cost of generating real-time or near real-time data?

If my non-profit friend asks these questions and then surveys her organization to find out why existing business processes are not yielding the data she needs to make solid business decisions, I suspect the result will be roadmap.  And that roadmap will show her how to move forward to improve data quality...and more importantly...the quality of decisions being made in her organization.