An Overview of Data Matching

So, what exactly is data matching? Simply put, it is the process of comparing and linking data from different sources to identify and establish relationships between them. This could involve matching customer information from various databases, merging duplicate records, or even linking data from external sources to enrich existing datasets.

Think of data match as a function that attempts to answer questions like:

👉 Is John Smith the same person as Jon Smiths? (identity resolution) 

👉 Is the name spelled as Mary Jones or Marie Jones? (typos)

👉 Do we have more than one record of Mary Jones across different data sets? (duplicate data)

👉 How many entries in the database point to Mary Jones? (record linkage)

For business users, understanding the basics of data matching is essential to get answers to these questions. It empowers them to take ownership of the data they work with and make informed decisions based on reliable information.

Additionally, it allows them to collaborate effectively with technical teams, as they can communicate their data requirements and expectations more clearly.

On the other hand, technical users play a crucial role in implementing and maintaining data match solutions. They are responsible for selecting the right tools and technologies, configuring matching algorithms, and ensuring the accuracy and efficiency of the matching process. By leveraging advanced data match solutions, technical teams can streamline operations, reduce manual effort, and improve overall data quality.

To accelerate data-driven goals, both business and technical teams need to work hand in hand. Business users should actively participate in defining data matching rules and criteria, as they possess valuable domain knowledge. Technical users, on the other hand, should provide guidance and support to business users, ensuring that their data requirements are met effectively.

In the next section, we’ll briefly go over how data matching works. If you’re a developer, you can skip this section and move on to the fourth section where we show you how to use a data match solution to find duplicates or merge records within minutes.

Data match is a function supported by algorithms derived from mathematical models. Three common algorithms that form the basic foundations of most data match algorithms are: 

a. Fuzzy Matching 

Fuzzy matching allows for easy matching of semi-structured data and records that do not have exact matching attributes. Text strings like names and addresses use fuzzy techniques such as Soundex for same-sounding names, or Levenshtein Edit Distance for differences in spellings. 

For example, the edit distance between the strings Catherine and Katherine is “1” because only one edit operation, the substitution of C for K is necessary to transform Catherine into Katherine.

The main problem with fuzzy data matching is that it can sometimes mistakenly identify things as matches (false positives) or miss real matches (false negatives). This happens because data can be similar or unclear, making it harder to match things accurately.

Therefore, careful consideration and validation are necessary when employing fuzzy data matching to ensure the reliability and accuracy of the results.

b. Exact Matching 

In this technique, you want results that show exact matches. Unlike fuzzy matching, exact matching doesn’t take into account similarity, instead, it looks for cells with the exact characters. 

For example, to match zip codes or postal codes between your database and the USPS database, use exact matching to identify duplicates. 

However, a problematic limitation of exact matching is its inability to handle data inconsistencies or variations. Since exact matching relies on strict criteria of identical values, even minor differences or errors can lead to missed matches. For example, a typographical error, a slight variation in formatting, or the use of abbreviations can result in failed matches, comprising the overall quality of a database.

c. Numeric Matching

Numeric matching deals only with numbers. It’s great for matching phone numbers or postal codes that contain only numbers. 

Similar to exact matching, numeric data matching has precision issues. It relies heavily on the accuracy and consistency of numeric values. However, when dealing with large datasets or complex calculations, rounding errors or inconsistencies in decimal places can occur. These small discrepancies can lead to mismatches or inaccurate results.

Apart from the above, other data match algorithms include:

• Soundex: A phonetic algorithm that encodes names and words into a four-character code based on their pronunciation. It is used for matching similar-sounding names.

• Jaccard Index: measures the similarity between two sets by calculating the size of their intersection divided by the size of their union. It is used in text analysis and set matching.

• Token-Based Matching: involves breaking text into tokens (e.g., words or n-grams) and comparing these tokens for similarity. It’s often used in text and string matching.

• N-gram Matching: N-gram matching involves breaking text into overlapping sequences of N characters or words. It is used to find similarities in text data.

• Smith-Waterman Algorithm: This algorithm is used for local sequence alignment of strings. It finds the optimal local alignment between two strings, taking into account gaps and substitutions.

If you’d like to get more details on data match algorithms, we recommend reading Peter Christen’s authoritative book on Data Matching: Concepts and Techniques.

The book gives a very easy-to-understand overview on:

• The complete data-matching process including blocking & indexing techniques
• Detailed step-by-step on how to clean and deduplicate data
• Strategies for record linkage and entity resolution
• Specialized topics like privacy and real-time matching

Enjoy the read!

We will not discuss the technical process of data matching at the moment as there are different ways to go about it. Some professionals use programming languages like Python or Java to create customized data match scripts, while others use Excel VLookUp functions to match and sort the data.

However, understanding the basic process of data matching can help you decide on the type of results you want from a match exercise, and what kind of tool, or approach you would want to use to get the desired result.

As a basic overview, here’s a common data match process that most businesses use:

✅ Define the scope of the data matching project:

Like with most data-driven projects, you must first identify what you want from the data. Do you want to simply identify and remove duplicates in a customer database? Or want to gain valuable insights for a marketing campaign?

For example:

To identify your top 100 loyal customers over the past five years, you would match your customer database with your sales database to extract the information. You require names, addresses, email addresses, and phone numbers from both databases to match the data.

✅  Prepare the data with data cleaning activities:

Unless you’ve had a dedicated resource to keep your organizational data clean, chances are your data is dirty, messy, and has inconsistencies.

For example:

To match customer data, you must begin by standardizing contact names, removing odd characters from data fields, and ensuring data formats (such as naming a city as New York City instead of NYC) are uniform. Optimizing for uniformity and consistency improves match result outcomes and prevents false positives and negatives.

✅ Select a matching algorithm

As discussed above, there are a variety of data-matching algorithms available, each with its own strengths and weaknesses. The type of algorithm to use depends on the match goal.

For example:

To match first and last names, you can use a fuzzy match, and once you’ve resolved duplicate contacts. To identify duplicates by phone numbers, an exact match will be a better option as it will count exact characters.

✅ Review the match results

A person who knows the context of the data must review the match results to prevent false negatives and positives from affecting the interpretation of the match.

For example:

The system might flag two customer entries, ‘John Smith’ and ‘John S. Smith,‘ as duplicates because of similar names. However, a person with contextual knowledge would recognize that these are different individuals and should not be merged as duplicates, thereby, preserving the accuracy of the database.”

✅ Merge, Purge, or Set Master Records

This is the final stage of the data match process. Once you have the desired results, you can decide to merge two similar entries of one entity into a single record – for example, John Smith may have a work address and a home address that you would want to merge into a single record.

For Example: 

When it’s all done and classified as matches or non-matches, you can select the final records and export them as a master record!

Sounds complex doesn’t it?

We won’t lie.

Data matching is a complex process, which is why, we recommend using automated data match software compared to using Excel or match scripts. It does take several rounds of fine-tuning and evaluating match results to get the insights you need.

You could save up to 20 hours a week (a rough estimate we’ve collected from working closely with customers), with an automated solution as compared to using manual methods.

In the next section, we cover a step-by-step breakdown of how you can match data using an automated solution like WinPure and remove duplicates or merge data within minutes.

WinPure is a true no-code solution that lets you clean, transform, and match your data to achieve business goals. With a plug-and-play interface, and the ability to create a custom library, WinPure is a solution that saves time, improves efficiency – and most importantly – ensures accuracy of match results. 

TL:DR: Watch a video of how our solution specialist uses the WinPure software to resolve for duplicates within minutes!

Here’s a quick breakdown of how to use WinPure to match data.

• Integrate data sources from multiple data sets & file formats: Unlike a few decades ago, you no longer need to manually transform data to run a comparison. With easy integration functions, you can connect a CSV file or a MySQL file to the interface and begin a match process. 

• Advanced data cleaning functions: In the image given below, you will see how the tool profiles the data for inconsistencies and errors. So if you’re in the marketing department, you can see straight away you’ve got empty email addresses and fields with punctuations and characters that add “noise” to the data. 

• Advanced cleaning with custom regex expressions: Sometimes you’ve got complex string data such as email IDs that contain numbers and text such as [winpure123@winpure.com]. You can match these strings using advanced regex expressions built into the tool or create your library of expressions for future reference, 

• Standardizing and cleaning data by splitting the data: When you have multiple data sets, such as from market, product management, or sales, you can end up with inconsistencies in standards. For example, someone can write the data structure as dd//mm//yyyy and someone can write it as dd/mm/yy. 

This supposedly small discrepancy can affect the quality of match results and lead to a higher chance of false positives. 

You can resolve these issues on the WinPure platform by splitting the data and choosing options like Propercase, Uppercase, and many more options to resolve standardization problems. 

• Building your own word library: Have specific words and abbreviations that you want to consider during the match process? WinPure lets you build a custom word library using Word Manager that prevents the system from flagging unnecessary matches. For example, you prefer Limited over LTD, or Ltd. 

• Matching within and across data sets: From the columns you’ve cleaned before, you can now match within data sets (such as matching data of Table A, then Table B). Once done, you can then match data across the tables (A x B) to weed out duplicates.

• What to match? When choosing what to match, use:

Relevance: Choose attributes that are essential for identifying duplicates or similarities.
Data Quality: Prioritize attributes with accurate and consistent data.
Specificity: Opt for attributes that offer distinct and reliable matching criteria.

• How to match data? It varies among users. You can choose 90% fuzzy matching for similar records, exact matching for identical values, or numeric matching for phone numbers and postal codes. Exact matching works well for well-processed data.

• Assessing the match or creating master records: Once the match result is assessed, you can then decide to merge the records or save a new set of records as a master set. 

…. And there you go! You now have a clean record, fit for business use! 

According to feedback and reviews from our customers, WinPure’s no-code data matching has saved them considerable time and effort in cleaning and setting up master records.

A few decades ago, data matching was simply a logical model used by database managers to match basic data sets. But today, as no-code data match solutions are on the rise, they have also empowered business users – and – businesses to achieve goals that go beyond database management. In fact, with the onset of AI/ML based applications, data matching has become a prominent technology that fuels data-driven goals like:

✅ Entity resolution: determining and linking different data entries that refer to the same real-world entity.

✅ Identity resolution: verifying and matching multiple attributes or identifiers to establish the true identity of an individual.

✅ Record linkage: linking information about one individual spread over multiple systems (such as a government benefits database)

✅ GDPR/sanctions: matching a company’s database with government databases to ensure sanctions and privacy law compliance.

✅ Customer360 view: enabling teams to get a consolidated view of their customer data across systems.

Additionally, the benefits of data matching in businesses, government sectors, and organizations include:

1. Fraud detection and prevention

Financial institutions are under immense pressure to deal with increasingly complex fraudulent activities. From scams to fake identities, and money laundering to regulatory compliances, financial firms need data match technologies to identify fraudulent identities and to meet compliance requirements. 

2. Better Public Programs

A CBPP report in the United States uncovered a situation where more than 40% of eligible individuals were unable to access a public nutritional program due to enrollment gaps that hindered them from receiving benefits. Through data matching, four states were able to identify these gaps and pinpoint the individuals who needed targeted outreach. Data match technology has enabled public and government programs to enhance their effectiveness and use public data for improved service delivery. 

3. Prevent Mistakes and Expenses

Salesforce reports that 70% of CRM data becomes obsolete, and approximately 30% of records are duplicated. Yet, many companies continue to send emails, direct mail, and flyers to all customers in their database, leading to customer dissatisfaction and unnecessary expenses. Data match tools can help identify duplicates so companies can avoid costly expenses and mistakes.

4. Improved Customer Service

With insights come opportunities. A data match project can show you who your highest-paid customers are, what have been their common complaints and where they are most likely to need support. For example, an airline can identify where its first-class customers like going for annual vacations and can offer concierge services for those locations.

5. Improved Customer Retention

When you get better insights into your customers, you can design more personalized services or offers that can improve your retention rates. For example, if your data match project shows that 70% of your customers come from a certain area of a town, you could create local events or launch a new service to improve retention rates.

6.  Increased Organizational Efficiency

When teams have access to accurate and reliable data, they can make decisions faster and better. Companies that have invested in MDM and entity resolution processes have reported higher efficiency of up to 80%! 

7. Remove Duplicates & Improve Data Quality

One of the biggest benefits of data matching is deduplication – the process of removing duplicates within a data source. Data duplication remains one of the most challenging data quality hurdles businesses are struggling with.

8. Drive Business Growth

Efficient data matching is the backbone of entity resolution which boosts growth factors like complete customer views, targeted marketing, better products and services, and so on.

These benefits demonstrate that data match technology is beyond an IT consideration. Instead, it shapes business decisions, which are implemented by business users. Therefore, it is essential for business users to actively participate in data match projects so that they can contribute to the effective implementation of a data-driven business strategy.

Data matching may not make for an interesting conversation but its importance in business goals cannot be understated.

In the current business landscape, companies are drowning in data, yet resources are limited. Not every business can afford to hire a data analyst to address the challenges of cleaning, merging, and purging large datasets, nor can every business invest in a high-cost platform. However, neglecting these issues can disrupt the accuracy of their insights.

An automated data-matching solution offers a clear path out of this dilemma. It empowers both business and tech users to collaborate seamlessly, bridging potential gaps in data understanding and minimizing conflicts.

If you’d like to know more about data matching and how our team can help, please feel free to reach out for a no-strings-attached call!

What is data matching used for?

Identifying duplicate records, verifying the accuracy of data, and consolidating data. 

What is matching in data quality?

Identifying and correcting inconsistencies between data sets. 

What is an example of data matching?

Comparing two records to identify if they are duplicates. 

What are the types of matching?

There are three types of matching; fuzzy, exact, and numeric among many others. 

What are data matching issues?

Incorrect or incomplete data, mismatches in data formats, and differences in coding schemes are common data matching issues.