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3rd October 2012

Control data quality on salesforce – 6 key ways

Data Quality on Salesforce: Strategy

Why is Data Quality on Salesforce important?

The data we hold about our customers is one of our business’s most valuable assets – so why is it so challenging to control data quality on salesforce?

The benefits of investing in a central CRM data source in pursuit of the single customer view are soon outweighed by frustrations if data management processes are not established from the outset. Here are 6 considerations which should form an important part of the initial salesforce solution design to help you to keep your data clean…

1. Data strategy

Ensure that you have a Master Data Management (MDM) strategy in place to determine how to manage consistency across multiple data feeds.  Establish governance and accountability from the outset and aim to incorporate data quality measurement into KPIs.

2. Data structure

Limit the number of fields on each record to those which will be used and make it clear where information should be entered if it appears on multiple records. Always use picklists instead of text fields wherever possible to avoid inconsistency.

3. Data processes

Educate users on the importance of proper searching and how data will be used for analysis and communications. Make use of required fields, validation rules and field updates but don’t overdo it as frustration = haphazard data entry!  Set up reports and dashboards to indicate field population and availability of key profiling information.  Put a policy in place to follow up data issues such as bounced emails.

4. Data capture

Review all of the potential touchpoints where data is collected to ensure that there is an opportunity for customers to easily update their key contact details and for you to enrich the data you currently hold.  There is nothing wrong with collecting additional information about customers and prospects as long as you demonstrate that it adds value to them.

5. Data access

Limit field editing for critical data and use field history tracking for visibility of who, what, when of record updates. Restrict data imports to certain users with clear guidelines or control them centrally to ensure that de-duplication checks are in place.

6. Data apps

Consider integrating de-duplication app such as DupeBlocker or DupeCatcher which allow you to determine the rules to automatically match and merge duplicate leads, contacts and accounts which flow in from each data source.

It’s never too late to improve data quality by establishing new data management processes, so if you are already experiencing frustrations, you may benefit from a data review to find out what improvements can be made.

 

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