Data Integrity

According to Wikipedia, data integrity is the maintenance of, and the assurance of, data accuracy and consistency over its entire life-cycle and is a critical aspect to the design, implementation, and usage of any system that stores, processes, or retrieves data.

This accurately summarises the need to invest in a certain level of integrity and reliability regarding a company’s data. In the world of anti-money laundering initiatives, data is crucial to the success of reducing and eventually eliminating money laundering activities. With the vast amount of data generated from onboarding, monitoring, investigations and much more, relying on human resources to ensure accuracy and integrity of data to create reports and analysis is quickly recognised as inefficient, time-consuming, inconsistent and much more. Often firms continue to rely on human resources due to financial constraints and the inaccessibility of modern alternatives. Barriers exist either by infrastructure or legislative restrictions. 

These limitations on resources is not an excuse to devise a reliable and robust process of housing data and, more importantly, ensure the data is readily accessible.

Resources
Resources will include labour, funding, existing technology and time. Identify how much of the resources are required to implement a new database system and how much would be needed to maintain it. It would be unwise to design a program that is not sustainable. 

Data structure
Determine how data will be entered into the database. This will include setting parameters on how consistent and restrictive the fields for entering data are set up. For example, will addresses be entered in a free form matter? Or will addresses be settled from a prepopulated source list? The more structured the data, the more reliable it will be as it ensures consistency. If the database is not robust, a four-eyes principle should be adopted.

Data accessibility
In the example above, considering address entry, if the address is not overly segmented, if there is a need to search for similar addresses or clients in a particular city, the database will not support this. Or if the information entered is not searchable, this will also result in issues when attempting to access the data.

Data Usage
The usage of the data should not only consider today’s necessity. This feature should venture to consider future uses. Brainstorming this scenario may include researching best practices of other jurisdictions or enforceable requirements of other jurisdictions. As a general rule, each data point should yield the potential to be reportable.

Testing
Your database should have scheduled testing. The testing will allow control of the databases continued usefulness. The testing will also determine if additional needs are not being met.

Data integrity can be maintained by implementing the above concepts into your data storage. It is important that the above are clearly understood to ensure that you can rely on the data housed in your system. Regulations, requirements, and business needs constantly change, proactively establishing data integrity will more than likely save you the costly project of establishing one in the future when it’s no longer an option. Phasing the changes can support a restrictive budget, but the main point is to start.