It’s crucial for organizations to understand why data integrity is a must. Not only does data integrity combine elements of data quality and security, it’s required for the consistent reuse of data and data-driven processes. Therefore, it’s imperative companies learn how to minimize data integrity risk.
News stories about businesses experiencing data breaches are common. They illustrate exactly how important data integrity is — and how devastating data integrity risk can be. We’ll explain exactly what data integrity means, identify common data integrity risks, and illustrate several ways to reduce your organization’s data integrity risk.
Characterizing Data Integrity Risk
To properly understand the various forms of data integrity risk, it’s necessary to define the term itself. Data integrity is the overall accuracy, reliability, completeness, and consistency of data, as well as how safe data is for issues of regulatory compliance and security. The FDA has developed the acronym ALCOA to define data integrity standards:
- A — Attributable: Attributable data means organizations should know how data is created or obtained, and by whom
- L — Legible: Legible data means organizations should be able to read and understand the data and the records are permanent
- C — Contemporaneous: This part of data integrity means organizations should know how data appeared in its initial state and what happened to it throughout the different stages of its lifecycle
- O — Original: This aspect of data integrity implies an understanding of data’s source systems and the ability to keep source data in its original state
- A — Accurate: Accurate data is errorless and conforms to the protocols of the applications for which it is used.
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Common Data Integrity Risks
Data integrity is compromised when there are problems with any part of its definition. Security lapses are a common data integrity risk many organizations experience. Because the business impact of security breaches is extremely critical, organizations frequently have to make allowances for customers — for example, Equifax bought identity protection packages for customers — resulting in increased costs. Organizations also must deal with damage to their corporate reputation and with their customer base — which can increase customer turnover.
Non-compliance with regulations is another fairly common data integrity risk. Organizations unable to satisfy the demands of regulations like GDPR are liable for large penalties. In some instances, they may be sued on top of these significant fees. Overall though, non-compliance with regulations is costly for organizations, regardless of the specific regulatory agency. Repeated compliance violations can even put companies out of business.
Perhaps the most common data integrity risk is unreliable data, which decreases efficiency and productivity. Unreliable data involves duplications of records, inaccurate data, and unidentifiable origins of data. No matter how a dataset has become unreliable, it prevents organizations from making accurate decisions and leads to added operational costs.
8 Ways to Reduce Data Integrity Risk
Because data integrity risk is so counterproductive for organizations and data-driven processes, it’s necessary to implement a number of strategic measures to reduce these threats. However, it’s almost impossible to minimize data integrity risk with just one approach, making it a better option to use a combination of several tactics. Some of the most effective ways to reduce data integrity risks include:
1. Promote a Culture of Integrity
Promoting a culture of integrity reduces data integrity risk in several ways. It helps to keep employees honest about their own work as well as the efforts of others. Workers in a culture based on data integrity are also more likely to report instances in which others take shortcuts or don’t fulfill their responsibilities regarding the many different aspects of data integrity.
2. Implement Quality Control Measures
Quality control measures include specific people and processes put in place to verify employees are working with data in accordance to security and data governance policies. For instance, data stewards can monitor the data lineage of data sources. IT personnel can monitor security systems for data integrity.
3. Create an Audit Trail
An audit trail is a particularly effective mechanism for minimizing data integrity risk. Audit trails are key for learning what happened to data throughout the different stages of its lifecycle, including where it came from and how it has been transformed or used. Understanding these specifics can ensure regulatory compliance.
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4. Develop Process Maps for All Critical Data
Developing process maps for critical data is a crucial aspect of governing how data is used, by whom, and where. By mapping these processes — ideally before data is put to use—organizations have greater control over their data assets. These maps are fundamental for implementing proper measures for security and regulatory compliance, as well.
5. Eliminate Known Security Vulnerabilities
It’s mandatory to eliminate security vulnerabilities to help minimize data integrity risks related to protecting data assets. This method of reducing risk requires subject matter expertise for determining known security vulnerabilities and implementing measures to eliminate them. It also requires technology like security patches to actually carry out this work.
6. Follow a Software Development Lifecycle
Following a software development lifecycle is a fundamental way of governing data in its journey throughout the enterprise. These development lifecycles are important for understanding the various governance protocols necessary to manage data according to regulatory and security requirements. This method is an integral step in understanding where data is and how it’s deployed, and then using this knowledge as a foundation to create sustainable practices.
7. Validate Your Computer Systems
Planning, mapping, and dictating what’s supposed to happen with data is useless without regularly testing, validating, and revalidating whether IT systems and employees are functioning according to these procedures. For instance, IT teams may be tasked with mapping source fields to target systems according to the metadata of the mapping constructs used previously. The only way to know for certain whether this process is performed is to test and validate the computer systems involved in these procedures to see if the information supports employee action.
8. Implement Error Detection Software
Error detection software and anomaly detection services can help monitor and isolate outliers, identify why errors occurred, and illustrate how to avoid them in the future. This entire process is critical for keeping data integrity risk at a manageable level.
Data Integrity Solutions
Organizations can keep abreast of these data integrity risks by relying on cloud integration platforms with built-in capabilities for data governance and data stewardship. Such comprehensive solutions accelerate the integration of big data, implement governance measures for data lakes, and automate critical aspects of metadata management. They also have measures for troubleshooting and monitoring aspects of data management vital to regulatory compliance, security, and data quality.
Talend Data Fabric is the ultimate data integrity platform, providing both speed in data integration and trustworthy, accurate data through built-in data governance and data quality capabilities. Are you looking to have ready access to data you can trust? Download the free trial to improve your organization’s data integrity.