Data matching is the process of linking a data field from one source to a data field from another source. This reduces the likelihood of making mistakes, helps you standardize your data and makes it easier to understand them by associating your data with, for example, identities.
From a data privacy perspective, data matching allows you to accurately link sensitive data to the identity of the person associated with this data. In this case, data matching can identify data owner records within all data sources, then match and link records across resources and systems to create a 360-degree view of each individual data owner.
Data matching is a critical element of any data privacy framework because manually discovering and classifying personal and sensitive data as a whole—and understanding how your company uses and shares that data—is not precise or comprehensive enough to address the data access and compliance requirements of today's privacy regulations. To support compliance initiatives, you need an automated, reliable data matching solution. You also need to understand customer data to KVKK, what the European Union General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) refer to as data holders. In other words, you need to be transparent about how the data you have is processed.
Data matching helps you establish a single source of accuracy for business-critical personal and sensitive data about your customers. It also lets you see how you know this data, what actual data records you keep about your customers, which systems keep records, and how those records are relevant and linked. Understanding this data in detail allows you to gain deeper insights into customer preferences and behavior.
Recently, an industry analyst based on research into the impact of GDPR puts the average time it takes to answer a data access query at more than a week, and the cost per request is also about $1,400. By automating your data matching procedures with customer IDs, it can help you modernize this process, respond faster to new customer rights/obligations to improve transparency, and deliver significant cost savings in a short time.
Coop Alleanza is Europe's largest consumer co-operative with 2.7 million members and 430 stores across Italy. Created by merging five small Italian co-operatives, the company needed to combine customer, product and sales data to create a 360-degree customer view without compromising its compliance with GDPR requirements by protecting its customers' personally identifiable information (PII). Using Informatica MDM, the company was able to identify and manage customer data across multiple internal and external systems, protecting PII by using it securely with a low risk rate to personalise customer experiences.
Given the volume, variety and speed of data collected by companies of all sizes, it has rarely been feasible to manually identify and manage any of your data, whether sensitive or otherwise. A data-driven business, especially one that leverages data lake analytics for customer insights and migrates to cloud workloads, needs to be able to move faster than the speed at which it can prepare data that drives people's movements.
AI training to recognize personal data as defined by privacy regulations allows it to quickly and comprehensively scan, match and link millions of records on an enterprise-scale. This is the only way to match data with sufficient speed and reliability to accelerate visibility into mapped data for faster, more authoritative analytics and business intelligence and for use in new applications.
The KVKK, GDPR, CCPA and other privacy regulations that control the storage and use of consumer information control companies to securely store and responsibly manage all the data they have about individual consumers. Being able to link seemingly unrelated pieces of information to a specific individual provides a company with the insights it needs to strike a balance between the need to properly enforce its privacy policies and the need to make data available for legitimate commercial uses.
In addition, the new privacy guidelines allow individuals to request a full report of all information a company has about them, giving individuals the ability to control their own data, specifying which practices they will allow in approved use. They may also assert rights over personal data, such as refusal to sell personal data to third parties, erasure (right to be forgotten) and receipt of all data records elsewhere (data portability).
A company can process such requests effectively only if it reliably knows what data it has and how it relates to the individual. Data matching with automation supports compliance with data privacy regulations by making it efficient and effective to correlate, empower, and manage requests and permissions of individual data holders. In addition, Data Matching enables centralized management of personal data from a single place, making it easier to implement the processing of data owner rights in a consistent manner. Data Matching helps data comply with privacy guidelines by protecting customer data, reducing the risk of incidental non-compliance from data misuse, and safely excluding users from risky practices of sensitive information based on identity-driven policies.
As privacy regulations become more widespread, it will become impossible to comply with each new regulation individually; companies need to address them at scale by functionalizing privacy compliance as a repeatable function. By associating and linking data subject records through metadata, data mapping helps support the operationalization of privacy and ensures safe and reliable use, making privacy an integral part of automated data management as a whole.
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