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The importance of Data Quality in Dynamics CRM implementations

The importance of Data Quality in Dynamics CRM implementations



One of the most overlooked – yet essential and fundamental – key to a successful Dynamics CRM investment, is data quality.


The importance of data quality

In today’s business world where relevant information is the ultimate currency, the higher the quality and accuracy of the customer data within your CRM system, the more valuable and powerful the system is to those who rely on it.

Good and accurate data is the most important factor in determining how successful a company is in its use of Dynamics CRM.  Inaccurate data quickly leads to users not trusting the system, and so not using it as intended and finding workarounds.  It also leads to customer dissatisfaction as they start to get sub-optimal service, or sent irrelevant marketing messages.

There are many areas that affect data quality, particularly if Dynamics CRM is integrated with other systems; it is also important to have a clear data management policy which makes it clear which members of staff are responsible for which pieces of data – this is an issue which is best managed by appointed end users (as they know the data well and with the right tools and dashboards can keep it reliably accurate), yet is often wrongly left to IT.

In fact, in my experience the best combination is for IT to provide a suite of tools, some of which they run regularly, and some of which are for the end users.  There is a whole third party add-on industry providing data management tools, but in the rest of this blog, I’ll look at some of the tools provided within Dynamics CRM.


Automated data cleansing features of Dynamics CRM

Dynamics CRM has some very useful features that provide automatic data cleansing in a product.

Data cleansing technology within Dynamics CRM negates the need for manual data cleansing or integrating to third party tools, and thus frees up resources for other projects.  Using some of that resource on devising a data quality management suite within Dynamics CRM costs less than a team of data management employees, and its implementation will support typical organisational goals of increased productivity and help drive costs down.

Two of the most important features within Dynamics CRM can support automated data cleansing are duplicate detection rules and bulk deletion jobs – both features that Dynamics CRM provides as standard.

Using these, here is a simple 4 step approach I have devised over the years to provide an effective successful Data Quality Methodology for end users and IT teams:

  1. Clean and standardize all data prior to the initial data load:

Ever heard of a project where they started the data migration too late, and so decided to ‘get it into the new system and clean it there’?  I have, many times.  Don’t do it.  For one thing, it never gets cleaned.  For another, it often makes data migration difficult and takes much longer than it should.  Thirdly, why give users a new, exciting system yet the same old untrustworthy data?  It’s like delivering a new car, but covering it in mud before the customer gets it.  Instead, start data cleansing and preparation in the old system, as soon as you decide you are going to get a new system.  You can never start it early enough, and most projects start it too late.

  1. Capture clean data on entry:

On too many occasions, one of the biggest problems is that the base data set up in the system gives end users the ability to enter faulty data. It is very important to minimize this possibility in your design, and clean the data right from the start.  For example, on address entry, provide a drop-down of an already existing set of Country, County/District and Town data sources to enable the users to populate the fields with accurate data. I have seen too many cases where the system allows free text data fields available for entry for everyone in the organisation.  Even better, embed an address management software to generate an accurate and consistent complete address.  In summary, use accurate drop downs wherever sensible, and use third party tools if possible to generate or augment consistent information (e.g. automatically add demographic or business data to help profile customers).

  1. Stop bad data from entering the CRM system:

Data gets into the system in a range of ways including direct entry, batch update, or integration.  There are advanced tools that will keep data clean and maximize the potential of your Dynamics CRM system, so you can import a CSV file, standardise its layout, verify and fix it, enhance it where you can, dedupe it against the data in your CRM system, and only then update the CRM database.

This should be part of a regular data quality management exercise which is run periodically in the organisation.

  1. Check for ongoing data quality within the CRM system:

Quite a percentage of your data will be entered manually and changed continually, making on-going data quality checking critical in the long-term maintenance of high quality data – and an on-going activity.  Make sure your system design assists in automating this checking process as much as possible. Dynamics CRM can assist you greatly in this regard by running recurring custom workflows which can check for this – for instance by identifying all records that have not been user-verified for, say, a year, and then queuing an action for the owning end user to do this.

At the end of the day, the success of any Dynamics CRM implementation is dependent on its Data Quality and thus the above practices should be part of your design of your CRM systems.


Hope it helps. Happy CRMing!


For more Dynamics CRM tips from Deepesh, please visit his blog: Dynamics of Dynamics CRM.



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