The average cost of bad data to US businesses is $15 million a year, as it hinders staff productivity and results in bad decisions. Suppose that inaccurate contact information in the CRM prevents sales reps from contacting prospects, or that sales managers base their strategic sales plans on reports created using out-of-date data.

If you’re an administrator in charge of the Salesforce data cleaning process, you’re probably wondering where to begin. Here are a few easy steps you can take to get started.

Determine what your Salesforce data cleaning project will include. To begin, run a report in Salesforce to see if you have any data quality issues. There is a lot to think about:

Duplication – There are duplicated accounts, contacts, leads, and contacts and leads. Power of One can be run using Report. Although it does not always work, you can easily export to Excel and use conditional formatting to perform a quick test to determine how many duplicates you have. A website is a good indicator for accounts, while an e-mail address is a good indicator for contacts and leads.
Data Appending – Some data is missing and needs to be filled in so that territory assignment, segmentation, and analysis can be performed. You can run different reports based on mandatory fields, or you can create a new field to calculate data completeness by counting the percentage of blank fields. For example, if you had ten mandatory fields, having three blank fields out of ten would result in a data completeness score of 70%.
Data validation – Your sales team is complaining about numerous instances of incorrect data that are slowing down their workflow. If you are tracking field changes, you can also run a report to see how many times specific fields were changed.
Cleaning Historical Data– Historical data contains bogus and test records, out-of-date contacts and leads, and inactive accounts. One way to test is to run a report with the keywords ‘test, bogus, xxx,’ and others to see how many bogus records were created.
Other Salesforce Data Cleaning Requirements – This can be tailored to your company’s needs.

 

What Next?

The following step is to prioritize. To prioritize, consider the importance of each step, the risk involved, the people who may be involved, and the time it may take to complete. For example, your analyst and Salesforce Admin can work together to resolve an industry data standardization issue. Other significant decisions may necessitate the involvement of the VPs of Sales and Marketing.

It involves the risk of deleting or merging data that is used or required by someone else in the organization, and it will necessitate a review of the entire sales team’s lists.

It is now time to decide whether you will do data cleaning in-house or outsource it.

Now that you’ve established your Salesforce data cleaning strategy, prioritized tasks, and decided how to clean your data, the next step is to back up your data before you start cleaning.

 

When you start cleaning, remember to:

Communicate with your team about the changes they will see in Salesforce and the timing of the changes
Bring in the right people to assist you with decision-making. Do you require assistance from your analysts in determining which buckets to use? Or perhaps you require marketing assistance with title segmentation? Getting the right people involved from the start will save you time later on.
Implement data governance rules and make changes to your salesforce processes to prevent issues from resurfacing six months after they were cleaned.
Keep a backup of your system in case something goes wrong, and you need to revert to a previous state.

 

Conclusion:

Effective sales data cleansing includes both business and technological data hygiene efforts. The issue of poor data quality is resolved, leading to improved accuracy of sales reports and forecasts, increased productivity of sales reps, and shorter sales cycles, when a significant amount of time and effort is devoted to keeping your sales data clean.