In today’s data-driven world, information is obtained from many different sources within organizations. Customer records, transaction data, marketing platforms, support systems, and databases all have data in their own formulations. While this data is valuable, it is too often found in silos and has duplicates or inconsistencies. This is where the need for data-matching comes in as regards data-analytics.
Data matching is used to find the records that correspond to the same entities from two or more sets of data. Without it analytics results can be misleading, incomplete, or inaccurate.
What Is Data Matching?
Data matching is a process of comparing the data from different sources so as to identify records representing the same person, the same object or event. These records may not look exactly like this, but they do have attributes that are used to point to the fact that they are meant to go together.
For example,a customer may be represented in one system as “Amit Sharma” and in another one as “A. Sharma.” Data-matching techniques help to understand that both the records are of the same individual.
More simply, the process of data matching is that of finding a way in which related data can be connected together in order to analyze the data as one view.
How Data Matching Works
Data matching starts with the selection of some key information for the sample name, email address, phone, ID or transaction details. These attributes then get compared to each other across a defined number of data sets using defined rules or algorithms.
Modern data analytics software often has automated match algorithms that take into consideration a range of differences in spelling, formatting, and incomplete records. Some of the systems try to perform exact matching, and some try to use probabilistic matching or fuzzy matching to handle the real-world inconsistencies in data.
The goal should be to be able to associate records that are somehow related, without making false matches.
Data Matching vs Deduplication of data
Data matching is very similar to (and often confused with) data deduplication, but they are not the same thing.
The data Deduplication is related to the process of eliminating duplicates from a single data. Data matching goes even further by attempting to find associations in between associated records in multiple data sets. More so in analytics, data-matching is more often the first step of a two-step function prior to the second action – Deduplication and data consolidation.
Why is it Important to Data Analytics?
The process of data matching is an important one in the production of reliable analytics.
- First, it enables the data to be more accurate. When the records are matched correctly, the results of analytics are a reflection of the real state of customers, operations or transactions.
- Second, it is there to espouse a common vision regarding the data. Businesses are able to conduct analyses of the entire customer experience, financial activity or operational performance rather than have piecemeal data.
- Third, it is careful not to duplicate insights. Without matching, therefore, the same entity is counted more than once and this leads to inflated metrics that make for poor decision-making.
- Fourth, it is related to making better business decisions. Accurate and connected data is something decision-makers can believe in.
Use Cases for Data Matching
Data matching is quite popular in all lines of industry. In customer analytics, it is about putting the data from the sales, marketing and support systems together to get a single customer profile. In the financial facet, it is used in transaction reconciliation and fraud detection. Also, in the arena of healthcare, it is used to match the records of the patient of the various healthcare systems. For Government and public services which means proper verification of identity as well as proper reporting.
In all of these use cases, the motive is the same – connect the data to get the complete picture.
Challenges in Data Matching
While it is quite powerful, it is not without challenges. The biggest problem is being able to get quality data. Missing values, inconsistent formats and outdated records can bring down theĀ accuracy of matching. Another challenge is to strike a balance between precision and recall. Over-stringent rules could have been bad at the problem of finding valid matches, andĀ loose rules could cause false matches.
As well as privacy and compliance. Sensitive data needs to agree with here and, particularly the personal information being the matching of financial information.
Future of Data Matching – Analytic
With the increase in data volume, the importance of data matching will be more important. Future systems are going to focus more on real-time matching and smarter algorithms and also more closely couple with data governance frameworks.
With analytics now, powered with AI, datamatching will be able to not only connect records, however it will be able to help identify relationships and insights automatically.
Conclusion
Data matching process is a central process of data analytics and helps connect and properly analyse data from various sources. Identifying which records belong where can improve accuracy, eliminate duplication, and yield a single view of data.
In an age where decisions are increasingly being made on the back of analytics, effective data matching is not a choice; it is a necessity. Organizations that invest in excellent data matching practices have better ways of understanding their data and have greater faith to trust their data and improve outcomes from their analytics initiatives.
Also Read: What Are The Main Trends For The Future Of Big Data?
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