Salesforce data analytics provides you valuable information on your sales and sales team’s performance. If you are using any of Salesforce’s other tools you can also report on data quality using data.com, marketing performance from Pardot operations, partner performance and the list just goes on.
At the core of any reporting is the data quality. Data analytics would not be possible without proper data. Therefore, data should be de-duped, normalized, standardized, collected and of course monitored.
How do you establish a high confidence for your salesforce data analytics? Ironically, you can do so by building a data quality dashboard or the most sophisticated way is to build a data quality scorecard.
You can monitor the health of your Salesforce, by identifying the following:
- % of your database with missing data to measure data completeness
- How current your salesforce instance is, by looking at % of your data which was recently created or updated.
- The average time it takes for a lead to get to closed won. And what data is collected at every-point.
The above is just a starting point for your reporting. Depending on the size and set up of your salesforce, you may need to create many different metrics to monitor the data quality.
For those companies that have implemented a data quality score-card you may wish to look at your data by sales rep, company division, type of data structure (customer, lead stage, qualified stage, partner data, marketing data, etc…), by date created (to see if quality is improving) and by source of data.
Depending on how bad your data is, you may want to implement a data quality bias when running your reporting or dashboard. Especially for funnel analysis.
It is advisable to clean your Salesforce instance on a yearly basis. As the task takes a long time and puts a strain on resources, you can outsource the task to a data cleaning company such as StrategicDB in order to have it de-duped, normalized and cleaned in a matter of weeks and a fraction of the cost of doing it in-house.