Artificial Intelligence vs Automation: Which is Better for Salesforce Deduplication?

While a lot of businesses are looking to streamline processes, this can be done in many different ways via automation, machine learning, and artificial intelligence. In terms of deduping your Salesforce environment, you have these same choices: rule-based deduplication, which is like automation, and a machine learning approach. In this article, we will tell you about the difference between these two approaches and why machine learning is the best way to go. First, let’s start with automation. 

What is Automation? 

Automation has been around for centuries. We can trace it back to eras like the Industrial Revolution and even earlier to medieval times when water was used in traditional milling to replace human labor in turning the millstone. This offers us an insight into what automation really is. Basically, automation is the process of machines replicating human tasks, but the machines will not have the ability to dynamically respond to any changes. Therefore, in many ways, the automation we use today serves the same purpose as it did back in the middle ages. 

If we look at the rule-based Salesforce deduplication tools, we can see this same type of automation. For example, let’s say that one of the matching rules is (Company OR Email) AND (Phone ). Instead of asking your sales reps or other Salesforce user to spot records matching the above match equation, you can simply create a rule that will do this for you. However, when we think about all of the possible fuzzy duplicates, it will be almost impossible to create a rule for every scenario. This is why machine learning is the better alternative. 

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How Does the Machine Learning Approach Work? 

Whenever you label two records as duplicates (or not) the system automatically learns from your choices and will apply the same logic to future records. For example, let’s take a look at the records below: 

To a human, it would be pretty obvious that these records are duplicates, but what exactly gives that away? Even though the names may be the same, both the name “Joe” and the last name “Smith” are very common, so technically it could be two different people. However, since they have the same phone number and email this is a bigger indication that these two are duplicates. In other words, we can say that the “Phone Number” field and the “Email” field have more weight than the fields like “First Name” and “Last Name”. 

This is how the machines learn to identify duplicates records as well since they are able to replicate the human thought process. However, it also goes beyond human computational capabilities as well. If we return to the example, above, we established that the “Email” field is more important than the “Last Name: field, but would you be able to quantify by exactly how much? Is it 3 times or 2.5? The system would not only be able to calculate something like this but apply the necessary weight to every field and dynamically adjust those as new records are added to the system. 

How Are the Machine Learning Systems Created? 

It would be useful to look at the process of creating a machine learning system as a pyramid. At the base, you have all of the data used to train the system. The data in your Salesforce environment is used as training data since the system needs to adjust to your individual situation. Therefore, when you are labeling records as either duplicates or unique, you are actually training the deduplication algorithm. Then comes the analytics stage which occurs when the system is able to manipulate the digitized data, allowing it to extract some meaningful insights. The system can now differentiate the duplicates from other records. 

Next, we come to the machine learning stage. Basically, the machine is able to take what it has learned and apply this knowledge and analysis to new data without any explicit programming. Any new records that come in will be deduped based on the field weights, string metrics, and other criteria that the system learned in the previous stage. It is worth pointing out that the learning aspect never ends. The system will continue to learn from new data and user actions and learn on the go. 

Finally, we get to the ultimate level which is AI. Even though machine learning is a big part of AI, it goes a level beyond machine learning by producing human capabilities, in our case, this would be identifying duplicates on its own. 

Why is Machine Learning the Best Approach to Deduping Salesforce? 

Machine learning is the best way to go because it does all of the work for you. There are no complex rules to set up, you don't have to standardize your data or any other configurations. Also, this approach is much more scalable. For example, let’s say that you already have 100,000 records in your Salesforce and you would like to upload a spreadsheet with 5,000 additional ones. A rule-based system would have to compare all of the incoming records with the existing ones, which is 500,000,000 comparisons. A machine learning system would take a smarter approach by blocking together records that have something in common. This could be something like the first three letters of the “First Name” name field, the same email address, and any other characteristic. 

Check out another amazing blog by Ildudkin here: A Machine Learning Approach to Deduping Salesforce

Try the Machine Learning Approach to Deduplication 

If you are tired of setting up rules or you notice that duplicates keep finding their way into your Salesforce, consider switching to the machine learning approach. It is a lot more comprehensive and it will significantly simplify your life and the job of your sales professionals.