When you go to AppExchange to find a tool to dedupe your Salesforce environment, you are faced with an interesting choice: rule-based deduplication tools or a machine learning powered application. While both approaches have their merits, you should know how each of these methods approach deduplication before deciding which application to buy. In this article, we will take a look at how rule-based deduplication and machine learning-based tools work so you can better decide what’s right for you.
Salesforce’s Deduping Functionality Alone is Not Enough
Salesforce is obviously a powerful CRM platform, but it does not by itself effectively deduplicate your data at scale. Its built-in deduplication features are very basic — rule-based matching only — and may not handle complex scenarios such as typos, data format variations, or unstructured data. Additionally, Salesforce’s deduplication tools may lack the scalability and flexibility needed for large datasets or intricate use cases. Organizations working with data from multiple sources, such as third-party integrations or legacy systems, may find that Salesforce alone cannot address inconsistencies or overlapping records effectively. To achieve accurate and thorough deduplication, businesses usually need to supplement Salesforce with dedicated tools or methods that are tailored to handle diverse data challenges.
If you would like to know about the limitations of Salesforce’s dedupliccation functionality, we go into great detail about this in our earlier article.
How Does Rule-Based Deduplication Work?
If you’ve shopped around for a deduplication tool on AppExchange, you’ve probably seen tools like Cloudingo, DemandTools and Duplicate Check. These are some of the main players in rule-based deduplication. The way Cloudingo works is that they require you, the user, to set up filters to catch duplicates. While they do offer some prebuilt filters, it is ultimately up to you to define what a duplicate is. For example, you might initially define a duplicate asLastName+Email+Company, but you might later see that this is not enough since duplicates are still coming in. So you create another filter like LastName+Email+Company+ PhoneNumber.
DemandTools works in a similar fashion i.e., they require you to create deduplication rules or filters. They offer additional options than Cloudingo like you can create a “Winning Rule” which can win in any scenario. For example, you can set a winning rule where any record where the lead source is “website” will win and the duplicate ones will be merged into it. You have a wide selection of various criteria to create the winning rule and other filters, but the underlying principle is the same: it is ultimately up to you to design and create filters to catch duplicates.
What are the Benefits and Disadvantages of Rule-Based Deduplication?
Rule-based deduplication is a method used to identify and eliminate duplicate data based on predefined rules and criteria. On the one hand, this approach has the simplicity and certainty of controlling the deduplication process, but on the other, efficiency and scalability tend to suffer. Below, we go into some of the pluses and minuses of the rule-based approach in greater detail.
- Highly customizable: You have the control to choose any field or parameter you want and therefore have almost endless options in terms of the filters you create.
- Good for simple deduplication: Rule-based deduplication relies on clearly defined criteria, such as matching specific fields or patterns. This straightforward approach is easy to implement, requires minimal computational resources, and works effectively in scenarios with well-structured data and predictable duplication patterns.
- Provides transparency: As mentioned earlier, rule-based deduplication relies on defined criteria that are explicitly defined and easy to understand. The clarity provides users the opportunity to review, modify, and polish the rules and the deduplication process will be in accordance with particular organization needs and will prevent ambiguity.
Now that we know the benefits, let’s take a look at some of the drawbacks:
- Limited flexibility: Rule-based deduplication fails to handle complex or unstructured data because it relies on predefined rules.It may not consider all the little variations or patterns that duplicates might contain.
- Scalability issues: As data volume grows, it is difficult to manage and update a large set of rules, resulting in inefficiencies which limit the scalability of this approach.
- Susceptibility to errors: If the rules are too rigid (or are poorly defined), you run the risk of false positives (indicating something that isn’ta duplicate should be a duplicate) or false negatives (missing true duplicates).
How Does Machine Learning-Based Deduplication Work?
Deduplication using a machine learning approach is based on powerful algorithms and models tuned to learning the patterns and relationships within the data in order to detect duplicates. Unlike rule-based methods that use predefined values, machine learning approaches look through huge datasets to find similarity and variations of data points. Trained on labeled datasets identifying duplicates directly, these models can distinguish amongst barely distinguishable differences like typos, abbreviations, or context differences that might elude rule-based systems. Natural language processing (NLP), clustering algorithms to keep similar records, and deep learning models for complex data types are a few common techniques.
It usually involves a few steps. The data is preprocessed to deal with incongruence such as missing values or data format mismatch. Features extraction from the data are produced in order to extract key attributes that may be potential indicators of duplication, such as names, addresses, or timestamps. These features are used by machine learning models to predict duplicates of pairs or clusters of the records. As more data becomes available over time, the model can be fine-tuned in order to get better at the task and change with the changing patterns. This adaptability is one of the reasons that machine learning based deduplication is especially powerful for large scale or dynamic data sets and the ability to handle unstructured data.
What are the Benefits and Negatives of the Machine Learning-Based Approach?
Machine learning-based deduplication offers a sophisticated approach to identifying duplicates by leveraging patterns and relationships within data. While it brings significant advantages in terms of adaptability and accuracy, it also comes with challenges that organizations must consider before implementation.
The benefits of machine learning-based deduplication include:
- Adaptability: Machine learning models can learn and get better over time, enabling them to work particularly well for dynamic datasets and dynamic duplication patterns.
- Accuracy: These methods are great at finding subtle similarities and differences, differences such as misspellings, abbreviations, or even variations in contexts, differences that rule-based systems could miss.
- Scalability: With the data relationships complex, machine learning approaches are better able to handle large datasets efficiently, using resources to process relationships as they develop.
- Flexibility: Unstructured data, i.e. text or images, can benefit and appropriately trained, they can be applied to different scenarios.
- Reduction of manual effort: After training, their operation is minimally operator dependent, and the workload for manual duplicate review decreases.
Now let’s take a look at the some of the drawbacks:
- Data dependency: High quality, labeled training data is key in effective deduplication. Inaccurate predictions or biased models can be caused by poor quality datasets.
- Complexity: Creating and maintaining machine learning models is a skill within data science, which not all organizations may have access to.
- Cost: Machine learning models can be computationally expensive to develop, train, and deploy. It takes a lot of investment.
When Should You Choose Rule-Based Deduplication vs Machine Learning
Rule-based or machine learning-based deduplication strategies should be decided depending upon the complexity and scale of your data and the needs of your use case. For situations where the data structure is known and known rules are used to identify unique or duplicate records, you are safe to use rule-based deduplication. Particularly if the rules around how to de-dupe aren’t going to change over time and you’re working with a small to medium dataset, this approach can work well. Furthermore, its transparency and ease of implementation make it appropriate for organizations that have limited technical resources or are forced to comply with regulatory requirements that demand clear and auditable processes.
Machine learning based deduplication is especially well-suited for more complicated scenarios, for example in situations which involve large scale or unstructured data and where duplicates aren’t necessarily obvious. For customers with a small dataset of records in Salesforce and no dedicated Salesforce admin, rule-based deduplication can be a practical solution. Its simplicity and ease of use allow users to start deduping right away without requiring advanced technical expertise or complex rule setup, making it an efficient and cost-effective option for maintaining data quality in smaller systems.
When dealing with data that can contain all duplicate varieties (misspellings, abbreviations, irregular formats, etc.), machine learning deduplication can be a great fit since it is able to learn the patterns and adapt over time. In the case that the dataset is dynamic, with new types of ‘duplicates’ emerging frequently, the machine learning approach is also advantageous in that it can grow with evolving data patterns. Nevertheless, it demands a large upfront investment in terms of expertise, computational resources, and labeled training data and is suitable for organizations with high volumes of data and that value payoffs over the long term and accuracy over short term simplicity.
Pick a Deduplication Tools That’s Right For You
Choosing a Salesforce deduplication tool requires determining appropriate data management requirements of your business, then comparing it with the available features of the tool. Begin with evaluating the scale and complexity of your data. A basic tool for deduplication with rule- based capabilities may work fine for organizations that are dealing with small to medium size datasets or simple patterns of duplication. Most of these tools are easy to use with Salesforce, and come with a user-friendly way of managing rules – and thus are great for quick and transparent solutions. Think in terms of real-time duplicate detection, support for batch processing and simplicity as features to ensure the tool is the minimum necessary to do its job (and not too much more).
If you’re part of a large organization or handle complex, unstructured or evolving data, you’ll likely find some advantage in more advanced tools with machine learning or AI built in. Both of these can identify duplicates through complex algorithms and even make sense of typos, abbreviations and contextual differences that more traditional means might overlook. Check for cross object match, customizable deduplication fields, and solid reporting. It has to be scalable, easy to integrate with your Salesforce setup, and provide actionable insights. Lastly, evaluate the vendor’s support services, the cost of the tool against its value, and user reviews to make sure you invest in a tool that fits with your data quality goals and the resources available to you in your organization.
