Data matching is the process of finding connections between data elements in a single database or across multiple sources. The objective is to consolidate your records to make datasets perform efficiently and drive business decisions. Without data matching to harmonize your data, information can be spread between multiple records, dividing intelligence and insight, leading to incorrect conclusions.

When is data matching used?

Typically, data matching is associated with combining duplicate customer records into a single view. For example, sometimes, customers create two accounts on an e-commerce website after forgetting their password. If both versions remain active, the customer could get duplicate communications such as e-mails, post, or telephone calls.

Data matching can also help to remove duplicate inventory items, identify incorrect addresses or anomalies between different business systems. According to Gartner, business initiatives lose over 40% of their value due to inaccurate and inadequate data. To put that into context, the cost of poor data amounts to over $3 trillion in the US each year. Data matching is a proven way to minimize the impact of dirty data and is a necessity within quality management frameworks.

How does data matching work?

Data matching tools will use fuzzy logic to determine duplicate records across your various data repositories. Records will be assigned a score based on the degree of match, allowing you to profile your data within minutes. The tools will fix any issues with standardization and cleansing, matching them, and removing duplicates.

In the example below, you can see a summary of how fuzzy data matching works.


On average, an analytics project will use data from six or more sources. It is highly likely that there will disparities in data formats between each of those sources, requiring a significant amount of resources to clean and standardize it. Data matching tools can prepare the data, ensuring that all sources are in the same format automatically and remove the burden on teams.

Complex algorithms will sift through layers of raw data to profile and cleanse it, ready for deduplication and merging. Data becomes more accurate and enriched, allowing an organization to start benefitting from it.

Compliance and regulation

New regulations like GDPR and CCPA require companies to be vigilant with marketing strategies and the data they hold. Data matching plays an essential role in ensuring a business complies with regulations.  For example, under GDPR, a company can only send marketing communications to a customer that opts into receiving them. Data matching will remove any duplicate records, mitigating any risk of a customer opting out, but still receiving marketing messages via a second account.

In the UK, government bodies are part of the National Fraud Initiative. The group makes it mandatory for authorities to engage in computerized data matching as a way of reducing fraudulent behaviors. Fuzzy matching will identify relationships between different records that uncover fraud, which may never have otherwise been spotted.

Data enrichment

Data matching helps to enrich customer records and create a better experience. Imagine you have two records, one contains transactional data and the other social media data. A data matching tool can identify that the records are the same customer, merge the data, and remove one of them. The business gets a better oversight of the customer, helping with segmentation, and creating a personalized journey. Campaigns that use segmentation can boost ROI by 760%.


Data matching leverages algorithms to eradicate problematic records and help maximize the impact of data across the enterprise. In an ever-growing digital landscape, it is becoming a crucial feature for businesses looking to provide exceptional customer experience and meet regulation rules. Without connecting your data, it is challenging to create meaningful insight to drive the right decisions.

If you need data matching for your consumer or marketing database, StrategicDB can help!