Frustrated by Poor Data

We hear this concern way more than we would like to hear it from new clients.  When we start peeling back the layers of the CRM data quality issues, we frequently see the same issues at the root of their problems. People are usually very excited to jump right in and start using Dynamics CRM.  While this enthusiasm is terrific, without the proper controls in place, CRM data quality issues begin to surface very quickly.

There is good news for those suffering from CRM data quality issues. While there is no magic wand that can make your problems disappear, there are steps you can take to begin addressing those issues.

Here are some tips to fix data quality issues

Understand the lifecycle of your data.

All data has a beginning and likely an end.  What gets often overlooked is what happens to the data after it is created.  Before you can ensure data quality, you must understand what happens to your data after its created.  How do your business processes support (or damage) data quality?  Who is responsible for data as it moves through your processes?  All of these questions must be answered and understood to control data quality.

Improve data quality at its source.

Identify the source for all data in your CRM.  Look for opportunities to improve the quality of data at its source. Once the data is populated in your CRM, its very expensive and time-consuming to correct quality issues.

Who owns your data?

Accountability for data quality begins when the data is created and must be maintained throughout the full life cycle of the data.  If no one is responsible for the data, quality begins to erode very rapidly.

Too many fields.

I once had a client that had several hundred fields on their opportunity.  Over the years, the requests for new fields had spun out of control.  All were approved, and in a short time, people forgot the original purposes for the fields.  When we reviewed utilization, out of tens of thousands of records, usually the number occupied for these fields numbered in the hundreds.  The ultimate cost for this is a major impact on user satisfaction.  Users are overwhelmed and ultimately reject the system.  Resist the temptation to over-do it when adding fields.

Process supports data and data supports process.

Process and data are dependent upon each other.  If your data cannot be trusted, then the processes that rely on that data are as deficient,  Too often we see a disconnect between data and process.  Poorly designed controls allow bad data to seep into the system, which corrupts the process dependent upon that data.  Analyze all data controls throughout all processes to ensure they are appropriate and safeguard your data.

Develop a data plan for data quality issues

Summarize all of your findings in your data plan.  Where does your data originate, ownership of the data, definitions for all fields, and data integrity processes to ensure your data maintains its integrity throughout its lifecycle.