An overview of data integrity and its importance
Data integrity is the consistency and correctness of data across its entire life cycle. Learn more about data integrity with the help of this guide.
Clean, healthy data can be a major competitive advantage, especially for businesses that invest the appropriate time and resources into their data management strategies. In the age of Big Data, organizations that harness data effectively and promote data integrity can make better data-driven decisions, improve data quality and reduce the risk of data loss or corruption.
But what exactly is data integrity, and why is it important to the overall health of the business? More importantly, what can be done to maintain high data integrity standards? In this guide, we’ll discuss how data integrity works and why it’s important for your business.
At its most basic level, data integrity is the accuracy and consistency of data across its entire life cycle, from when it is captured and stored to when it is processed, analyzed and used.
Data integrity management means ensuring data is complete and accurate, free from errors or anomalies that could compromise data quality.
Data that has been accurately and consistently recorded and stored will retain its integrity, while data that has been distorted or corrupted cannot be trusted or relied upon for business use.
Data integrity is important for a number of reasons. However, its importance is best explained with a practical example.
Imagine you are a project manager who is running clinical trials for a new revolutionary drug that will be a game changer in the fight against cancer. You have conducted human trials over the past five years and are convinced you’re ready to move into production.
However, while going through regulatory protocols with the FDA to get your drug to market, they find data integrity issues within the data from your trials — some crucial quality control data is missing.
As a result, they halt your trials. Although you may convince them to proceed with the approval process after you’ve addressed the data integrity issues, the delay will likely cost your company millions of dollars and impact public perception of your drug.
In this example, data integrity is critical to the success of your clinical trials and the ultimate product. This is just one example from the pharmaceutical industry, but this issue cuts across many sectors and data types. Data integrity is fundamental in regulated industries, where data must be accurate, complete and verifiable at all times. Poor data integrity can cause enterprises to lose money, positive public and industrial reputations, and valuable production time.
Data integrity is a complex and multifaceted issue. Thus, data professionals must be vigilant about the various risks that can compromise data integrity and quality. These include the following:
In data management, human error is a major risk factor for data integrity. Human errors can occur when data is input incorrectly, incorrectly processed or incorrectly analyzed. In some industries, like finance or customer service, the reliance on multiple data sources can also lead to data integrity issues.
If data is not configured correctly — for example, if incorrect user permissions have been set — it may be more vulnerable to cybercriminals or data breaches. Likewise, if data is not appropriately secured with encryption and access controls, it can also be compromised by unauthorized individuals or programs.
Hardware can fail, data can be accidentally deleted or overwritten, data can be corrupted during data transfer and storage, or data may be unintentionally accessed or overwritten by other data users.
