Data Quality: Why Data Matching Tool is Necessary for Your Business

Gartner estimates that business initiatives lose more than 40% of their value due to poor quality and inaccurate data. With no standardization for contact information, along with cultural differences, spelling variations, and common misinterpretations, the chances of consumer data getting duplicated, corrupted, or wrongly matched are considerably high. This ultimately leads to poor data quality. 

What’s the outcome? According to statistics, businesses in the US lost more than $3.1 trillion every year because of poor data quality. 

Having data quality management protocols in place is pivotal, and equipping your business with the right data matching tool is equally important. By finding connections and identifying patterns between all kinds of datasets, a data matching tool can minimize bad data while ensuring complete operational accuracy. 

Following are some more ways that a data matching tool proves necessary for your business: 

Data Match Tools Help Prepare Data for Business Intelligence

Before being used for any application or process, data must be prepared so it meets the requirements of the particular operation. Just so we have an idea of the criticality of data preparation, it’s important to note that almost $22,000 per data analyst is spent every year to prepare enterprise data for the purposes of analytics and reporting. 

A data matching tool automates the process of sieving raw data across multiple layers, cleansing, profiling, deduplicating, and merging it to make accurate insights possible. 

Using a Data Matching Tool Increases Data Accuracy

Limited resources need to be utilized in the most efficient manner for any business to succeed. However, capital and human resources are usually wasted because of poor decisions based on inaccurate data. 

A data matching tool optimizes accuracy levels across the business units of any organization. This resultantly improves overall efficiency along with team productivity. 

Data matching eventually facilitates standardization. A tool that ensures proper data matching provides the capacity to define a standard, as records are stored in several formats. If this standard is implemented across the company, sorting through records based on specified fields becomes easier. Thus, users are able to access accurate and complete data each time. 

Data Matching Tools Can Ensure Better Compliance

Data matching plays an essential role when it comes to ensuring compliance. This proves effective with GDPR’s demands as it requires companies to carefully manage marketing strategies in the European markets. 

GDPR has made it mandatory for businesses to ask for permission to use email IDs and other sensitive information of a customer before contacting them. Since engaging with customers requires an omnichannel approach, it becomes impossible to get permission from them when data varies across every platform. 

Inconsistency in data increases risks for incurring penalties. A data matching tool helps businesses narrow down and figure out exactly which customer they’re dealing with, and seeking explicit permission becomes convenient. 

Fraud Prevention Can Be Automated Through Data Matching

Most regulatory bodies and healthcare institutions face sizeable losses because of fraudulent payments and claims due to secret relationships between entities. Generally, the data is found in the form of records entered into the computer in different systems across branches or departments. 

Fraudsters and scammers take advantage of the multiple records that are stored at various sources inside an organization. This creates discrepancies, making it impossible to track back to the actual record. In some cases, fraudulent tactics are also carried out by employees in order to forge records, such as procurement receipts, financial reports, etc. for personal benefits. 

A data matching tool uses unparalleled fuzzy matching algorithms that help in identifying relationships between different records. This can assist in uncovering the truth behind any fraud. Companies can retrace steps following this technique, which facilitates investigations to go to the source of the issue. Several government bodies across the United Kingdom participate in the National Fraud Initiative, as it makes it mandatory for these bodies to carry out computerized data matching. 

Conclusion

A data matching software can sort out data quality problems and resolve them conveniently. Such a platform ensures that multiple standard and proprietary algorithms are leveraged to identify fuzzy, phonetic, abbreviated, miskeyed, and domain-specific variations in data. Needless to say, it can help in building scalable configurations for record linkage and deduplication, extraction, enhancement, suppression as well as standardization of customer and business data to maximize the impact of data quality across your enterprise.