Data matching is also known as entity resolution and record linkage. It is referred to as a task to identify and assign two different records as one and the same across various data sets. Data matching finds accurate matches across the system by identifying duplicate records to select them and merge accordingly. They can also select a master record and delete identical entries in organizations. This is the reason data matching is regarded as an important function for proofreading, standardizing and cleansing data entries. By discarding duplicate content, organizations ensure that data residing in their systems are accurate and clean.
How does data matching work?
Now that you are aware of what data matching is, do you know how it works in real-time? We know data matching’s basic purpose is to find data that have a similar kind of entry within the system. Many times, data found by the software comes in more than two copies which may or may not have common identifiers. However, data matching tools can detect such data duplicity within the database accurately.
Data matching software analyzes records and detects similar matches within directories using various algorithms. But the approach to detect and match data may further depend on either of the two matching styles, deterministic or probabilistic. Although there are many ways to perform data matching, these two are most commonly used by contemporary industries. Often, they are based on data matching algorithms or a pre-defined programmed loop in which every set of data is matched and compared against one another.
The deterministic record linking system relies on several matching identifiers, whereas the probabilistic record linking system is based on the likelihood that various identifiers will be matched. Businesses mostly use probabilistic data matching since deterministic data matching can sometimes prove inflexible.
To start with data matching, the data is either blocked or sorted into smaller blocks having similar attributes. They must be attributes that do not change such as date of birth, name, shape or colour. The matching starts thereafter which can be performed variously. For instance, name data can be matched phonetically and with the identification of letters. The relative weight of every attribute is calculated to ensure its value after which, they are calculated to make out matching probabilities. Lastly, the algorithms adjust the relative weight of every attribute to find the total match weight to ensure the probabilistic match of the compared data.
Pros and cons of probabilistic and deterministic data matching
While the two most commonly used data matching tools are probabilistic and deterministic data matching, they come with several pros and cons. Probabilistic data matching is quick to implement according to the setting match percentage as compared to a detailed match combination. It can however bring an average out unique situation or separate the data entries more swiftly. Alternatively, deterministic matching can allow users to detect data minutely when the granular process is tuned but it may take more testing time to create every match criterion. Therefore, the combination of the two works the best for every industry.
Uses of data matching
Data matching is essentially used for eliminating duplicate content or merging a similar set of data in a system. This can be done variously by using data mining processes. The companies match data in order to establish a critical link between two or more sets of data or for other purposes. Some common industries which use data matching include:
Financial institutions
Many financial service organizations, banks and fintech companies use data matching to maintain initiatives like analyzing money laundering offenders’ activities or clients’ credit scores analysis. The financial institutions use the data matching technique to get a comprehensive view of their users serving various economic operations.
Public Sector or government bodies
By assessing personal identity information like SSNs and registration numbers, government and public sector companies may identify corruption, abide by rules, and perform socio-political analysis. Data matching may be used to identify probable scams, associated activity, and participants. Additionally, the government collects a variety of demographic data for national surveys, which is routinely collected by many organizations using various criteria and kept in different systems. Through the combination of this information, the government may develop empirical studies and develop a better understanding of various regions of the nation.
Education Industry
Data matching is extensively used by schools and universities to identify redundancy in teaching and learning datasets across all geographies. They also use data matching to assess students’ performance, assess their grade fluctuations, analyze teaching strategies or verify if the teaching techniques are adequate or not.
Healthcare Sector
Just like the education sector, healthcare providers benefit a lot from data matching. They can easily compare patients’ data appropriately and determine diagnosis with prescriptions. At health centers, patients’ data is compared to determine appropriate diagnoses and precise prescriptions. To assure the accuracy of their patient data, firms use data matching and cleaning procedures using apps. If an automated deduplication method is not employed, patients may receive many therapy or unsuitable drugs for the same ailment. Medical records are linked with several other datasets in order to investigate the effects of various factors such as treatments, diseases, and drugs.
Marketing and Sales
Data matching is the most commonly used technique in sales and marketing because it simplifies the process of analyzing and categorizing the targeted audience. The data matching is done according to sociodemographic variables which are refined and verified according to their activities. Personalization allows these companies to capitalize on marketing and advertising activities by creating appropriate and relevant ads or promotions for potential buyers.
Why is Data Matching so important for contemporary firms?
Automate Fraud Prevention
Multiple records kept at numerous locations within an organization are used by hackers and criminals. Employees occasionally utilize unethical methods to fabricate documents for their own gain, like financial reports, sales receipts, etc. Data matching software use top-notch flexible matching algorithms to find connections between various records. It could be useful in revealing the fraud’s hidden causes. This method enables businesses to go back and retrace their processes, simplifying investigations to find the root of the issue.
Save money
When companies send more than one catalogue to a customer, it is a kind of a waste of money. Similar wastage may take place if a particular data set is printed multiple times or sent to customers without proper assessment such as date or time. When sales teams call one customer multiple times, they waste their significant time besides losing the customer’s interest. All these can be prevented if data matching is applied across organizations effectively.
Enhance customer service
Managing and organizing customers’ complaints is equally important as managing customers’ positive reviews. When all the messages get disorganized or recorded under the same customer’s name multiple times, it might get difficult for the company to segregate good and bad reviews. This can further create issues while solving product or sales-related matters. Hence, data matching can be used to avoid such issues and help firms maintain their customer database appropriately.
Improve decision-making with enriched data
Correct decisions are very important for the smooth running of the business. Data matching can help here by improving data management and accuracy across business departments. This further increases employee productivity and efficiency. When data will get upgraded constantly, it will help organizations in improving sales and marketing alongside other activities such as accounts, taxes and compliances. Even though systems across organizations require manual intervention at times, using data matching can save human effort and time significantly.
