Data compliance standards (such as GDPR, HIPAA, and CCPA, etc.) are compelling corporations to revisit and revise their data management strategies. Although each standard enforces its own specific regulations, but in a nutshell, the core objective is to give citizens more control over their personal data. These standards define a set of rules for businesses and corporations that operate within a specific geographic location (GDPR for Europe, CCPA for California) or work in a specific industry (HIPAA for healthcare), and so on.
Under these data compliance standards, companies are obliged to protect the personal data of their customers and ensure that data owners (the customers themselves) have the right to access, change, or erase their data. Apart from these rights granted to data owners, the standards also hold companies responsible for following the principles of transparency, purpose limitation, data minimization, accuracy, storage limitation, security, and accountability.
This puts immense pressure on organizations as they try to understand how to make their data management strategies comply to these rules and principles. It has become imperative to have a data quality management framework that ensures and enables all ten data quality dimensions. But to achieve that, it is crucial to clean, transform and standardize customers’ personal data records through various processes of data matching, linking, deduplication, and merging.
Data quality management processes for data compliance
GDPR’s Article 5 defines the key seven principles which are at the core of GDPR compliance.
Article 5 (1) (d) states that personal data should be:
“accurate and, where necessary, kept up to date; every reasonable step must be taken to ensure that personal data that are inaccurate, having regard to the purposes for which they are processed, are erased or rectified without delay;”
Similarly, HIPPA’s Privacy Rule gives a patient the right to:
“…ask to see and get a copy of your health records… and have corrections added to your health information…”
With large amounts of data being monitored and collected every second of the day, ensuring data accuracy is one of the biggest concerns. Personal data exists and is being used across disparate data applications, and then it is processed by different departments for various purposes. Earlier, inaccurate data records impacted an organization’s business intelligence process and insights. But now, companies are under legal obligation to keep their data clean and minimal.
Read on to see how different processes of data quality management can help you to comply with various data compliance standards mentioned above:
Data profiling
The first step of any compliance process requires you to identify potential problems with the current setup. What are the issues that are creating roadblocks in your data quality, and hence compromising data compliance? Profiling your data will show you a complete picture of your dataset in terms of missing, misspelled, invalid, and duplicated values that your records contain. This will give a deeper view of your data values and highlight potential cleansing opportunities.
Data cleansing and standardization
With the data profile generated, next step is to start cleaning your data so that you can achieve a standardized view across all datasets. Data cleansing usually involves efforts to make the data accurate, complete, and valid. A clean and standardized dataset will help you to comply with data guidelines better, and implement a transparent data strategy.
Data reduction
This is the step where the entire dataset will be examined to assess whether all data attributes being captured and stored are purposeful and necessary. The ones that are not being used to improve your business processes or customer experiences, will be subjected to a reviewal process. Once you decide which attributes of customer’s personal data are absolutely necessary to be captured, you can eliminate the noise, and make data more meaningful by parsing the data or merging multiple fields into one.
Data matching and deduplication
This is by far the most important step in preparing your data for compliance. Data matching will help you to build a single view of your customer data. Right now, you probably have multiple variations of customer records existing at different ends of your company. Sometimes errors occur due to data entry mistakes; at other times, customers input incomplete data, or data becomes irrelevant over time as last name, addresses, phone numbers, and email addresses change. No matter the reason, your company ends up with multiple data records for a single customer entity.
To overcome this challenge, you need robust, real-time data matching algorithms, that work specifically on the nature of your data. Usually, datasets include unique identifiers like social security number, account numbers, etc., that help in identifying a single entity. But in situations where such data does not exist, or cannot be used to hide personally identifiable information, it becomes necessary to use algorithms for phonetic, numeric, domain-specific, and fuzzy matching. Furthermore, these algorithms must be tuned for level and weight variables to ensure maximum accuracy and least number of false positives. Once you have the data match results, you can start deciding which records to eliminate or merge to build a single source of truth of your customer data at your company.
Benefits of data cleansing and matching for data compliance
Such proactive efforts in data cleansing and matching will help your organization to:
- Have a single source of truth that shows the complete reality relating to each client. And so, if a customer requests to access their record, legally assured, you can hand over that single record knowing there is no other variation of their data in your database.
- If a customer requests to erase their personally identifiable information, your organization will know exactly what to delete and give the confirmation back to the customer that there is no other variant of their data left at your company.
- If a client wants to update their information, you can update the change at a single place and be at peace knowing that there is no discrepancy across different datasets as all information is being derived from a central place.
- With a standardized dataset, you can hide your customers’ personally identifiable information and restrict its access to a small number of representatives at your company. This will ensure that such sensitive information is kept protected and secured. Previously, all datasets contained some form of PII that was left lying around across different data applications at your company, at the risk of being accessed by any employee.
- A central, complete view of customer data will help CISOs to feel confidence in their information management strategy. Otherwise, dispersed and dirty data would keep them anxious, not knowing if there is potential personal data processing happening at their company that they are not aware of.
Conclusion
Data compliance standards enforce a number of rules and principles that do not relate to data quality only, but also focus on data housing, management, delivery, and storage. Although, the rest of GDPR concerns are as important as data quality, but we cannot overlook the fact that the nature of data should be at the core of your compliance strategy. It is not enough to store, manage and distribute data securely and efficiently if the data involved does not follow the necessary guidelines. For this reason, it is crucial to begin your data compliance strategy by focusing on achieving data quality. And DataMatch Enterprise is an all-in-one product that can help you with that. You can download a free trial or book a demo today to understand how DME can help you with your data compliance strategy.
How best in class fuzzy matching solutions work: Combining established and proprietary algorithms
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