What is data matching?

Data matching identifies potential data duplicates and then assigns them as one record, usually called the “Golden Record.” Also referred to as entity resolution or record linkage, it is the first hands-on step for most projects that require the integration of one dataset with others, but as well when you want to improve your data quality at the entry point.

Data matching identifies similar entries in one or multiple different sets of data. Here is an example: the sales department might have information on Mr. J. Doe, including his phone number and physical address. The accounts department has Mr. James Richard Doe, who lives at the same address, and they have his credit card information for automatic account payments. They are the same person, and it’s important to match these disparate records.

Data matching is considered one of the most critical functions, cleansingprofiling, and standardizing data. It enables organizations to have a more holistic view of each recorded entity by discarding duplicate records and ensuring that the data is cleansed and accurate.

How does data matching work?

Data matching tries to analyze whether two entities are similar. There are many ways that this task can be performed. The most common way is based on an algorithm or a programmed loop, where each data set-piece is compared and matched against each part of the other data set.

There is a deterministic and probabilistic data matching approach. When the algorithm matches a data piece against another unique piece of data and recognizes they are the same item, this is deterministic. If a more sophisticated algorithm matches data by comparing more complex variables such as similar strings of information, this is probabilistic.

In a deterministic approach, matches are detected as exact matches; a record has the same similarities. The algorithms use patterns and rules to conclude that records are matching.

Probabilistic matching identifies the likelihood of matches based on a scoring threshold. Let’s say that three parts of a record match. Is that enough to ensure these are the same record? Is J Doe the same as John Doe? What about if it was J R Doe; is that the same record as John Richard Doe?

Another example is when data is organized into similar-sized blocks with the same attribute. They should be attributes that are unlikely to change, such as names, dates of birth, color, or shape. Then the matching can take place. For example, words can be matched phonetically or by letter. Subsequently, the relative weight for each attribute is calculated to measure its importance. Then, the matching probability can be calculated. Finally, the algorithm fine-tunes the relative weight for each point to get the total match weight. This then leads to the result: the probabilistic match for two related things.

The process can be simplified as follows:

  • Standardize data
  • Select attributes unlikely to change
  • Categorize data into blocks
  • Match via probabilities
  • Allot value to matches
  • Summarize to get the total weight

With practice, the goal is to keep fine-tuning the data matching algorithms to obtain better results.

Why is data matching so important?

Data matching is a way to avoid duplicate content. This is important because poor quality, duplicated, and inconsistent data could lead to multiple problems:

Wasted money

If an organization sends out four catalogs to one person, that is a waste of money. The company is printing more logs than required, but there are also postage costs and any possible negative ramifications; no customer appreciates being hounded. Or, if a sales team is calling targets, it is incredibly inefficient to contact the same person more than once.

No single source of customer truth

If an organization wishes to perform data analysis or make predictions about future trends, the data needs to be accurate and thorough. If the information has gaps or double-ups, there is no clear view of the customer’s interactions.

Poor customer service

Managing a customer and providing adequate service is more challenging if all their records are in disparate locations. If there are multiple sales for the same customer under different forms, it can also be frustrating for customers to have a complete overview of their purchases or interactions.

Negative reputation

Whether it is an email newsletter or physical mail, customers do not appreciate being swamped with information, especially if it is the same marketing again and again. Making the same sales calls to John Doe, J Doe, and J R Doe will not leave the customer with a positive impression.

Industry use cases of data matching

Though the ultimate aim of data matching is to find more accurate and unique records out of several similar forms, the approach can differ from industry to industry.

Banking and finance

Fintech, finance, and banking service institutions use data matching to complete tasks such as finding perpetrators involved in money laundering activities or performing customer credit scoring. Banks execute data matching processes to gain a consolidated view of customers spread across multiple business functions.

Government and public agencies

Government and public sector agencies rely on record consolidation by examining personal identification data, such as social security numbers, passports, and driver’s license numbers, to spot scams, comply with standards, and conduct political evaluations.

Education institutes

In the education sector, data matching is used to flag duplicates in student and teacher datasets across regions and measure student performance, distinguish different teaching methods, and evaluate students’ grade changes.

Healthcare industry

Healthcare establishments match patient data to identify appropriate diagnoses and accurate prescriptions. They use data matching and cleansing techniques through enterprise applications to ensure the uniqueness of their patient data. Patients could be prescribed contra-indicated medicines or receive multiple diagnoses for the same symptoms without an automated deduplication process.

Sales and marketing

Sales and marketing organizations use data-matching techniques to find matches and remove duplicate contact entries in customer relationship management (CRM) systems and related business applications. Data matching enables these companies to improve their sales activities, increase return on investment (ROI), and improve multi-channel marketing campaigns by targeting individuals using analytics and data science.

Breaking down customer details is vital in any marketing and sales strategy, but it is essential when dealing with big data. Demand generation teams frequently struggle with establishing boundaries, which might sabotage their personalization efforts by supplying incorrect or insufficient data. Data matching solutions allow organizations to discover and classify the target audience based on several demographic factors by combining data enrichment and verification skills. By designing more relevant messaging for target customers, an organization can maximize the success of sales and marketing initiatives with accurate personalization.

Benefits of data matching

Data matching is one of the initial steps in any organization's overall data management strategy, leaning toward the master data management approach.

Accuracy and efficiency

Data matching facilitates data comparison, identifying patterns, and raising red flags on complex data for further examination. It is a very reliable tool, enabling more excellent standards of accuracy while at the same time keeping unrelated information to a minimum.

Reliable clean data

To form an internal data infrastructure, organizations use a vast network of applications and data systems that are integrated into one another. However, when data is collected from a range of channels, there will be a high likelihood of discrepancies in the information. In such cases, data cleansing and deduplication are vital to data reliability.

Articulate data for business insights

Machine learning involves using data from many sources. Data matching tools simplify sifting through numerous raw data layers, cleansing, profiling, deduplicating, and merging for reliable analytics insights. To normalize data, an organization needs to be able to standardize and clean multitudes of entries across and within many data sources.

It also requires transforming numeric data into an appropriate and consistent format, such as phone numbers. Data is structured and ready for downstream business intelligence applications to process and provide quality insights.

Increase business decision accuracy

Any incorrect decisions based on erroneous data waste time and money. Data matching can help organizations increase accuracy across business departments. As a result, employee productivity and general efficiency will improve.

Enriched data for more insight

Data matching means an organization can benefit from data upgrading by combining data from reputable third-party suppliers with an existing dataset. By improving consumer data consistency and quality, organizations can improve their sales, marketing, production, and other activities. The improved data fills in any inconsistencies in the consumer information, giving the organization a complete view of its target audience.

Enhance compliance

Because of General Data Protection Regulation (GDPR) regulations, all businesses should carefully consider their marketing plans, particularly in Europe, to ensure they comply. Data matching helps to ensure compliance with this legislation. Before an organization can contact a consumer, they must seek permission to use the customer's details in marketing campaigns, such as email addresses. Because of the multichannel nature of consumer engagement, acquiring authorization from customers becomes more difficult. Additionally, when data is incorrect and fluctuates between internet sites, the danger of penalties increases. Businesses can identify exactly which customer they are working with, giving them the ability to ask for specific permission.

Strategically prevent scams

Due to concealed relationships between entities, many insurance organizations suffer significant losses due to fraudulent claims and payments. There is a range of data records submitted to various programs, but no data matching means no red flags are raised. Scammers and fraudsters use duplicate records maintained at multiple places across an organization to create discrepancies that make it tough to track back to the original document.

Employees may also utilize deceptive strategies to falsify records, such as procurement receipts or other documents for their gain. Data matching software can identify associations between distinct records using fuzzy algorithms, which determine the standard links between various forms.

Reduce storage needs

Deduplication is a process that ultimately leads to fewer records in a dataset. This leads to less storage space required, reduces the load on the server when an application needs data and makes the quality of data much higher.

Challenges of data matching

Potentially complex data matching processes

If processes are already in place for data collection and entry standardization, then data matching can be a simple process. However, if there are less robust pre-dataset standardization (and data quality) procedures, matching approaches may require more complex logic to extract all potentially matched data.

Requires data standardization

Big data, in particular, may pose issues. For example, in a necessary field, a default email address might be provided as "NA@NA.COM." This properly-formatted address may appear on hundreds of entries; thus, processes must account for how this piece of data should be handled.

User error

System users can pose problems, especially when they have the power to flag records and data. Using an employment record as an example, if a user incorrectly flags a large employer as "suspicious," all genuine applicants using the same employer may be marked as “suspicious” as part of the referral process. This type of action would almost certainly negatively influence the company and its onboarding processes and other companies that share and match data.

A deficiency of good automation or tools leads to more manual intervention. Some decisions still necessitate human intervention. Data matching can sometimes be inflexible, disjointed, and inadequate, even with the most sophisticated matching systems.

Errors in data matching

Data matching is prone to errors. Data matching accuracy is rarely tested in many data integration systems despite this issue. This is a big concern because measuring is necessary for understanding and increasing data matching accuracy. When it comes to data matching, there are two types of faults to consider:

  • False positives occur when two records refer to different entities, but the matching system believes they are the same.
  • False negatives occur when two records relate to the same entity, but the matching algorithm says they are not.

Organizations must understand the frequencies and consequences of false positives and false negatives.

Despite the challenges and limitations, data matching is vitally essential for any organization wanting to improve its data stores and implement a data-based business approach. It lets organizations create scalable setups for deduplication, record linking, suppression, augmentation, extraction, and standardization of business and customer data. Additionally, it establishes a single source of truth to maximize the value of data across the organization.

data matching diagram

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