In most businesses today, data is the fuel that runs the organizational machine. It informs decisions, drives innovation, and ultimately influences a significant portion of daily operations. So why is it, then, that so much data isn’t automated or properly integrated at many businesses? A lack of proper data integrity almost completely negates the ability to make data driven decisions, which are becoming more and more crucial each day for everyone from the individual to the most successful enterprises. As someone operating a business in any field, it behooves your company to embrace a strong data integrity plan. Here is a short guide to reinforcing data integrity at your business today.
Data Integrity Defined
At its most basic definition, data integrity is a measurement of how complete an organization’s data is, its accuracy, and whether it’s reliable. Typically, data integrity gets defined as the quality and consistency of data. But what does that even mean, exactly? In a broad sense, it’s more about how your data is stored, retrieved, used, and protected. At any company that uses data frequently, there are two types of integrity that should be followed: physical integrity and logical integrity. The concept of physical Integrity is self-explanatory. It merely means maintaining the physical storage or cloud storage of your data. Under logical data integrity procedures, companies handle the finer details of maintaining quality data. This is usually a four pronged approach.
The first is securing domain integrity, which just means that any data will have relevant fields and be conducive to proper collection. Next, referential Integrity ensures the data can be stored properly and retrieved as needed, without spoiling the data in any way. Entity Integrity refers to reducing duplicate records or introducing irrelevant data. User defined Integrity governs everything that users do with the data outside of the typical domain, entity, or referential spheres. This also covers how metadata gets handled at an organization, along with ensuring compliance throughout the entire process.
No business wants to lose customers, but that’s precisely what can happen if the company doesn’t address data risks. Poor data integrity can directly or indirectly lead to that exact scenario, or worse. Whenever technology is involved, security risks can come up at any time. Part of a data integrity manager’s job is to mitigate risk and prevent data management problems that come up as you work with your data. Adding redundancy, minimizing data loss, and training employees will lead to fewer risks over time and better data integrity for the entire company.
Common Integrity Issues
Problems with data integrity can stem from a number of distinct sources. These include everything from human error (we all make mistakes sometimes) to hardware problems.
The sole purpose of a data integrity initiative is to ensure your data is high-quality and consistent. Therein lies some of the problems: a common data integrity issue is inconsistency, regardless of the industry in which your company operates. Whether it’s merely inconsistent data or missing pieces of information in the database, inconsistency can be a big problem. Decision-making suffers, reporting becomes inaccurate, and the chance of human error greatly increases. So how does inconsistent data get introduced into your databases in the first place? It’s usually the result of human error and mistakes during the data entry process. Manually inputting data is usually the culprit in these situations.
If fields within your records aren’t defined well, that could also lead to problems when the same types of records are located in different locations in your database. Slight variations can negatively affect data quality. Hardware issues, storage, cybersecurity, and especially human errors can have an adverse effect in your data integrity, so paying special attention to as many factors as possible is key to solving/mitigating many of the most common data integrity issues at your organization.
Building Data Integrity
Education and comprehension are an integral part of building data integrity into your operation, but there are a few other focus areas that are just as vital. It all starts with knowing where your data comes from and being familiar with its sources. How does data enter your business? How is it stored and retrieved? What sort of protocols are you using to understand/audit your data?
Building Integrity requires following at least four key tenants: data alignment, quality, accessibility, and enrichment. On the quality and accessibility and, those are fairly self-explanatory. But when it comes to enrichment and alignment, that means you need to make sure that all systems are combined together (and that you’re not mixing legacy systems with current ones) and that you’re able to add context through enrichment as well.
The Importance of Compliance
Every organization faces the challenges of remaining in compliance with various established standards, whether they be organizational or government. To maintain a well-developed data integrity plan, remaining in compliance is critical. GDPR, or the General Data Protection Regulation, covers a great deal of compliance standards required of most organizations to keep data private and secure. Privacy is a key aspect of maintaining data integrity, so your policies and procedures should reflect a strong dedication to keeping data secure and private. Metadata—the data that defines and describes other data—also needs to be strictly controlled, to avoid compromise or surveillance issues with your data.
Technology can help ensure the quality, security, and accuracy of your business data. Remaining compliant is a critical process within any organization and will go a long way toward improving data integrity across the board.