Data Quality – The Hidden Enabler of Security and Compliance

Melissa AU Team | Data Quality | , , , ,

When guests check in to a 5-star hotel, they volunteer their personal information quite freely with the assurance that their data will be kept safe. After all, the customers are paying a premium for service. What happens when there’s a data breach – Marriott discovered the consequences when their systems were hacked and sensitive details of over 500 million customers were exposed. The hotel line was fined £18.4 million for failing to protect personal data.

When it comes to security and compliance, it’s all about data quality. Low quality of data increases the risk of security breaches and heavy compliance fines. How does good quality data help – here’s what you need to know.

Data Quality Impacts The Way Security Issues Are Identified

The connection between data quality and security isn’t always easy to see. When data doesn’t meet quality standards, it can lead to security breaches. For example, mislabelled data can trigger false positives in the security network. Setting off alerts for trivial issues can lead to ‘alert fatigue’. The professionals manning the security system will begin to mistrust the alerts and ignore them.

On the other hand, if data is incomplete or inconsistent, the security system may not be able to identify sensitive data and give it the protection required. For example, A file tagged as ‘P. no’ instead of ‘Phone no.’ may seem unimportant and hence not get encrypted.

Data Quality Makes It Easier To Identify Compliance Risk

Any company that holds customer data must comply with KYC and AML regulations. Failing to do so can put them at high risk for fines. When the database contains duplicate records, it’s hard to get a holistic, true picture of the customer. But, deduplicating records gives you a single, complete view of the customers that makes it easier to spot personally identifiable information and validate it to comply with Anti-Money Laundering (AML) reporting.

Standardized data formats make it easier to identify potential red flags. Machine learning and AI can further automate compliance discovery and remediation.

Reporting Becomes More Efficient

When compliance reports are based on high-quality data, the reports are more accurate. The records flagged as compliance risks are more likely to be serious issues. There are fewer false alerts and hence efforts spent on mitigating the risks have a higher ROI.

Proper data profiling and automatic discovery help identify sensitive data and its lineage thus making it easier to find data relevant to compliance regulations. Reporting becomes more trustworthy and efficient.

Centralizing Data Management Simplifies Data Quality Control Settings

Finding inaccuracies when data is siloed can be quite challenging. On the other hand, bringing data from all the different departments together and centralizing data management helps identify when personal data is stored, its lineage and how it passes through the system.

Data responsibilities can be distributed correctly and sensitive data assets can be attached to data owners. Lastly, it makes it easier for data governance to centralize the data quality efforts and set relevant controls to validate it for compliance.

High-Quality Data Makes Machine Learning Effective

Human efforts can identify only so many compliance issues and data irregularities. This is where Machine Learning and Automation come in. But, when used on poor-quality data, the results are unreliable. When data is verified, complete and records are deduplicated, companies can use data catalog tools to categorize sensitive data and establish access to the data.

Machine Learning protocols can then be installed to identify compliance risks. As compliance regulations become more and more complex and data environments expand further, the use of Machine Learning becomes critical to regulating with compliance standards.

Tips To Meet Data Quality Standards

Given the influence data quality has over security and compliance regulations, it is imperative to take the steps required to improve data quality. To qualify as high-quality data, it must be accurate, complete, consistent, valid, timely and unique. This boils down to knowing where and how it is stored and regularly checking the quality of this data. Here are three things you need to do:

  • Check all incoming data

There are multiple points from which companies collect data. You will need to set up checks at each of these points to verify and validate incoming data. For example, address autocomplete tools verify the addresses as they are being entered and minimize the risk of human error. Similarly, before emails are added to the database, they can be pinged to validate them. Simple errors like making typographic errors in the domain name can be easily corrected. This ensures that all data entering your system is correct and valid.

  • Standardize formats and deduplicate files

Apart from being verified as accurate and complete, you will need to standardize the format of these records. A simple difference like the wrong date format can have quite serious implications. For example, let’s say data collected through a mobile app uses the DD/MM format while data collected from the website uses the MM/DD format. An individual logging into the system through both these might inadvertently create two records. Standardizing records helps prevent such instances. You can then turn your efforts towards deduplicating records and ensuring that every record is unique.

  • Maintain vigilance

When it comes to meeting data quality standards, it isn’t enough to look only at incoming data. Data can be corrupted or expire with time. Thus, you also need to look at the data existing in the system from time to time. Data quality checks are not a one-time exercise but must be consistent. For example, the city may change street names. Any records with the old street name in the address must be updated with the new name to stay accurate.

A Final Word

Data is critical to compliance and security operations. Hence, improving data quality delivers benefits for both. Investing in improving data quality will show manifold returns. It streamlines processes, improves accuracy with compliance initiatives and builds trust in the data and reports. Today, working on improving data quality isn’t optional, it is a basic requirement.