The Salesforce platform launched in 1999 and about 10 years later, it has become apparent that with the proliferation of data, ensuring data quality is paramount. As one of the first efforts to tackle this problem in the early 2010s, Salesforce introduced matching rules and duplicate rules, designed to help users identify and manage duplicate records within their Salesforce environments. Since then, multiple third-party tools have come to market, extending deduplication and other data quality capabilities although they still leverage fuzzy matching logic.
A growing number of companies that rely on vast amounts of data hosted in their Salesforce CRM –for sales, marketing, customer service and other purposes – utilize their CRMs not only as “large excel files” but as important instruments to drive key business decisions. To address these key business needs, some of the traditional data matching techniques seem to fall short. We witnessed this firsthand while utilizing some of these third-party tools, which motivated us to start DataGroomr. We were interested to see how AI, machine learning, and other scientific approaches can be leveraged to perfect data quality. We started with a machine learning approach and based on our users’ feedback, we’ve later added classic matching. Today, I want to introduce another promising method and talk about why it stands out, especially as compared to fuzzy matching and term frequency-based approaches. Meet probabilistic matching.
The challenges of data matching
Data matching is the process of identifying and linking records that refer to the same entity in a dataset or across different datasets. This is crucial because bad data in general – and duplicates in particular – eat up to 30% of a company’s resources. Accurate linkages are a must that allow you to maintain accurate customer information and ensure seamless data integrations. Traditional methods like fuzzy matching and term frequency-based approaches have been commonly used, but they have significant limitations. Let’s consider a few real-world examples.
Merging customer data across multiple data sources
Consider a retail company with multiple data sources: online store purchases, in-store transactions, and customer service interactions. Each source may record customer details slightly differently. For instance, “John Smith” might be recorded as “Jonathan Smith” in the online store, “J. Smith” in the in-store system, and “Jon Smith” in the customer service records. Without effective data matching, the company would struggle to unify these records, creating fragmented customer profiles and leading to poor customer experiences
| Retail system | First_name | Last_name |
| online store | Jonathan | Smith |
| in-store system | J. | Smith |
| customer service records | Jon | Smith |
Limitations of fuzzy matching
Fuzzy matching attempts to identify similar but not identical records by measuring the edit distance between strings. The most commonly used algorithms are designed to handle typographical errors and slight variations. Let’s consider the most popular.
1. Levenshtein distance
This algorithm calculates the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one word into another. It’s widely used for spelling correction and DNA sequence analysis.
2. Jaro-Winkler distance
This algorithm is particularly effective for short strings such as names. It measures similarity between two strings, giving more weight to the initial characters, which is useful for detecting typos and variations in names.
3. Soundex
This is a phonetic algorithm that indexes words by their sound when pronounced in English. It’s useful for matching words that sound alike but are spelled differently, such as “Smith” and “Smyth”.
4. Metaphone and Double Metaphone
These are phonetic algorithms used to encode words by their pronunciation. They are improvements over Soundex and handle a broader range of sounds and variations, making them effective for matching names.
5. N-Gram
This method breaks down a string into substrings of N length and compares the common N-grams between two strings. It is useful for finding similarities in longer texts and can handle transpositions and deletions effectively.
6. Hamming Distance
This algorithm counts the number of positions at which the corresponding characters of two strings are different. It’s used when strings are of the same length and is effective in certain binary data matching scenarios.
7. Jaccard Similarity
This coefficient measures the similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets. It is useful for comparing sets of words or characters.
All of these approaches handle slight variations very well; however, they struggle with more complex discrepancies such as transpositions, missing fields, or variations in naming conventions. This can lead to false positives and missed matches, reducing overall data quality.
Example: Consider two records for the same customer: one lists the name as “Johnathan Smith” and the other as “J. Smith”. Fuzzy matching might not recognize these as the same individual due to the significant difference in string length and content, potentially leading to data fragmentation.
Challenges with complex variations
Fuzzy matching falls short when dealing with variations beyond simple typos. For instance, if one record spells the name “Katherine” and another uses “Catherine,” fuzzy matching may not identify them as the same person. This issue is exacerbated when dealing with international datasets where names may have different spellings or formats. Although, arguably, this can be solved fairly easily by applying basic data preparation techniques.
Shortcomings of term frequency-based approaches
Term frequency-based approaches, such as TF-IDF, rely on the frequency of terms within a dataset to determine the importance and similarity of records. These methods work well for text-heavy datasets but can falter when dealing with structured data like customer records. They often fail to account for the context and structure of the data, which leads to inaccurate matches and incomplete data integration.
The key components of TF-IDF are:
- Document Corpus: Multiple records containing the same terms and their frequencies.
- Term Frequency (TF): Calculated based on the formula: TF = (Number of times term t appears in a document) / (Total number of terms in the document)
- Inverse Document Frequency (IDF): Calculated based on the formula: IDF = log(Total number of documents / Number of documents with term t in it)
- TF-IDF Calculation: Combines TF and IDF to give the final score: TF-IDF = TF * IDF
Example: In a dataset where the term “John” appears frequently, a term frequency-based approach might overemphasize this common name, leading to potential mismatches with other unique identifiers like addresses or phone numbers. This lack of contextual awareness can result in misaligned data entries.
Overemphasis on common terms
When analyzing a dataset with common terms, term frequency-based approaches can skew the results. For instance, in a database of medical records, terms like “patient” or “diagnosis” may appear frequently. Relying solely on term frequency can lead to the overemphasis of these common terms, instead of critical data points like unique patient identifiers or specific medical conditions.
The power of probabilistic linkage
Probabilistic linkage, on the other hand, uses statistical models to estimate the likelihood that two records refer to the same entity. This approach considers multiple attributes and their interdependencies, making it more robust and accurate. Here’s what makes the probabilistic approach excel in records deduplication:
Multi-attribute comparison
Probabilistic linkage evaluates a range of attributes simultaneously, such as names, addresses, and dates of birth. This holistic view reduces the chances of false positives and improves accuracy overall.
Example: If two records have the same birthdate and address but slightly different spellings of the name (e.g., “Jon Smith” and “John Smyth”), probabilistic linkage can recognize these as likely referring to the same person based on the overall probability derived from matching multiple attributes.
| First_name | Last_name | DOB | Address |
| Jon | Smith | 10/10/1998 | 12 Center Street |
| Jon | Smyth | 10/10/1998 | 12 Center Street |
Handling missing data
Unlike deterministic methods, probabilistic linkage can handle missing or incomplete data by estimating probabilities based on available information.
Example: If a customer’s email address is missing in one record but present in another, probabilistic linkage can still accurately link these records by analyzing other available attributes, such as phone numbers and mailing addresses.
Scalability
Probabilistic models can scale efficiently with large datasets, making them ideal for enterprises dealing with millions of records.
Example: A large e-commerce company with millions of customer records across different databases can use probabilistic linkage to efficiently consolidate and clean their data, ensuring accurate customer profiles without compromising performance.
Reduction of False Positives
By considering the probability of a match rather than relying on fixed thresholds, probabilistic linkage reduces the occurrence of false positives, ensuring higher data quality.
Example: In a healthcare database, distinguishing between patients with similar names but different medical histories is critical. Probabilistic linkage minimizes the risk of conflating records to maintain data integrity.
Key Advantages of Probabilistic vs Fuzzy Matching
| Advantage | Probabilistic Matching | Fuzzy Matching |
| Multi-Attribute Comparison | Considers multiple attributes simultaneously to determine the likelihood of a match | Typically matches records based on a single attribute or simple string similarity measures |
| Handling Missing Data | Can link records even when some attributes are missing by estimating probabilities from available data | Often fails to match records if critical data fields are missing or incomplete |
| Scalability | Efficiently scales with large datasets, maintaining performance across millions of records | Performance can degrade with very large datasets due to simplistic matching algorithms |
| Reduction of False Positives | Uses probability thresholds to minimize false positives, leading to more accurate and reliable matches | Prone to higher rates of false positives, often confusing similar but distinct records |
Real-world applications and benefits
Probabilistic deduplication offers a range of benefits across multiple industries, especially for those where ensuring high data quality and accuracy is of utmost importance. Here are some examples of how different sectors can leverage this approach:
Healthcare and life sciences
In healthcare, maintaining a single 360 degree view of patient records is crucial for delivering effective care and ensuring patient safety. Probabilistic linkage can help healthcare providers consolidate patient records from different sources, such as hospitals, clinics, and pharmacies.
Example: A patient named “Jane Doe” may have records in multiple healthcare systems. Probabilistic linkage can match her records despite variations in name spellings, making sure her medical history is complete and accessible to healthcare providers.
Financial services
Financial institutions often deal with large volumes of customer data from various sources, including banking transactions, credit reports, investment portfolios and so forth. Things like compliance and prompt customer service are usually at the forefront there.
Example: A bank may have multiple records for a customer named “Robert Johnson” across its checking, savings, and credit card systems. Probabilistic linkage can unify these records, providing a comprehensive view of the customer’s financial activities and improving service delivery.
Retail and e-commerce
Retailers and e-commerce companies rely on accurate customer data to personalize marketing campaigns, manage inventory, and enhance customer experiences.
Example: An online retailer can use probabilistic linkage to merge customer records from online purchases, in-store transactions, and loyalty programs. This unified view enables targeted marketing, personalized recommendations, and improved customer satisfaction.
DataGroomr’s approach
At DataGroomr, we utilize AI-driven matching, and with the addition of probabilistic linkage, we are set to deliver an unrivaled data quality solution. Our application seamlessly integrates with Salesforce to provide real-time data cleaning and deduplication. By equipping our users with the ability to leverage multiple advanced algorithms, we help ensure that your data is not only accurate but also actionable.
Example: A Salesforce user managing customer interactions can rely on DataGroomr to automatically detect and merge duplicate records, ensuring that sales and support teams have a single, accurate view of each customer. This leads to improved customer service, better sales tracking, and more effective marketing campaigns.
Technical insights into probabilistic linkage
Probabilistic linkage leverages advanced statistical techniques and machine learning algorithms to estimate the likelihood of records matching. The Expectation-Maximization (EM) algorithm is commonly used in probabilistic linkage to handle missing data and optimize the match probabilities. This iterative algorithm alternates between estimating the missing data (Expectation step) and optimizing the model parameters (Maximization step).
Example: If a record is missing a phone number, the EM algorithm estimates the likely value based on other available attributes. It then adjusts the model parameters to maximize the likelihood of correct matches, improving overall accuracy.
Addressing common concerns
Handling large datasets is another critical factor. Probabilistic linkage must efficiently handle large datasets without compromising performance. We designed DataGroomr’s platform with scalability in mind, so that we can provide fast and accurate results even when your data scales up to millions of records.
A large multinational corporation uses DataGroomr to link customer records from various regional offices, ensuring a unified global customer database without performance degradation.
The future of data matching
As data volumes continue to grow, the demand for accurate and efficient data matching will only increase. At DataGroomr, we provide probabilistic linkage as an additional option offering flexibility to our customers in how they want to identify duplicates to maintain top-tier data quality. Organizations that stop innovating risk becoming obsolete. That’s why we are committed to continuously refining our approaches and techniques and to listening to your feedback.
For businesses seeking to unify and harmonize siloed data, probabilistic linkage offers a superior alternative to traditional methods. At DataGroomr, we are committed to perfecting data quality through innovative AI solutions. Embrace the future of data management with us.
