In a number of recent blog posts, we’ve explored the importance of data quality and the potential impact that it can have on an organisation. Improving data quality is not without its challenges, and there is no “one-size-fits-all” approach.

In this blog post, we take a closer look at data matching, discuss its importance, and explore how it can be used in your own organisation.

What is data matching?

Data matching, as the name suggests, is the process of comparing data from two or more datasets to determine if they refer to the same thing.  Data matching is particularly useful when combining data from multiple siloed sources.

For example, organisations in the accountancy space working with and processing data from multiple sources and systems might use data matching to create a “golden record” or “single view of their customer”. In this example, data matching could also help to resolve issues relating to manual entry errors or updates to the dataset, such as a name or address change.

How does it work?

There are two main methods of data matching that organisations can utilise.

• Deterministic Matching
  Deterministic matching is a direct comparison between the values of a record in two or more datasets. This method of matching often utilises unique identifiers such as a scannable barcode or Passport Number. While ideal in scenarios where unique identifies are available, deterministic matching lacks flexibility. For example, manual input errors in one dataset may cause a match to fail.
• Probabilistic Matching
  Also known as “Fuzzy” matching, probabilistic matching compares the values of several fields and assigns a weighting to determine how closely two field values match. The sum of these values determines the likelihood that individual records match. While more difficult to implement, probabilistic matching allows the user to determine the weighting of particular values, allowing them to finetune the matching process to meet their specific needs.

Why is it important?

Data matching is a critical part of improving the quality of an organisation’s data, particularly when it is working with data that is stored across multiple siloed sources. Some of the key benefits of performing data matching include:

• Deduplication
  Unclean and duplicated data is a fundamental barrier to many types of automation and digital transformation. Data matching can help to address duplicate entities present across multiple datasets. By comparing datasets, data matching allows users to identify which data points refer to the same entities.
• Increased Accuracy
  In addition, data matching can also help improve and enrich the quality of a dataset. For example, a manual error such as a spelling mistake might only be present on one dataset. By comparing these datasets, organisations can create a more accurate representation of their data.
• A deeper, more holistic view
  Data matching can help to enrich the quality of an organisation’s data. For example, one dataset may feature values that are not present in another dataset. By combining data from multiple sources, organisations are provided with a more complete view of their data.
• Improved decision-making
  The intention of any data quality exercise is to improve the business value and actionability of a given dataset. By taking steps to improve the accuracy and completeness of a dataset, the insights drawn from those datasets are likely to be more representative and capable of more accurately informing critical business decision-making.

Experts in data

Analytics Engines offer end-to-end guidance, consultation, and support in response to your data challenge. We are experts in data, and we work in close collaboration with you and your team to understand your specific objectives and challenges. We develop solutions that provide your business experts with the resources they need to make critical business decisions more effectively.

If you’d like to improve the quality of your data, arrange a no-obligation introductory call with one of our data experts using the form below.