Applying Machine Learning for Good Data Hygiene

There are enough distractions in today’s business economy that you shouldn’t have to also worry about what data hygiene is doing to your business. Yet, over two-thirds of participants in a Deloitte study on data accuracy estimated that the third-party data about them was at best 50 percent correct. That’s not very accurate. We know that data drives business and clean data is critical for sales and strong customer relationships.

Duplicate management is one approach to corral the inaccuracies in your CRM. But it seems like it’s never straightforward - and it should be. When it comes to Salesforce, it’s easy to become overwhelmed by the seemingly endless ways that a single account can generate duplicate records that get stored in your database. And although Salesforce provides a duplicate management function, it often falls short of what you need to eliminate duplicates completely.

Why is it so damn difficult to catch duplicates? The reason is the ever-changing parameters and conditions that need to be applied or matching rules for accurately detecting duplicates. One human, or even a team of humans, will struggle to maintain these over time. And that time is money lost for a business. A study out of MITSloan found that employees can spend up to 50 percent of their time dealing with mundane data quality tasks. 

The best approach is a smart approach, and that’s where machine learning shines. In the case of duplicate management, machine learning algorithms can actually be trained to identify and eliminate the seemingly ceaseless introduction of duplicate data before it gets into your database. I, for one, am very excited by the prospect of using machine learning to enhance data integrity and ensure that leads and contacts can be accurately portrayed and pursued by sales. That accuracy helps businesses grow.

To learn more about how to easily overcome the duplication issues in Salesforce, read our blog, Why the Salesforce Duplicate Management Function Isn’t Enough.