Does your data have integrity? The term refers to the accuracy, reliability and consistency of data and not just right now, in this moment, but for the whole of it’s life-cycle. Any compromises in data mean that it’s not fit for purpose, can’t be used and so therefore the practice of keeping data clean is key to an organisation or business.
The cleanliness or integrity of data also refers to regulatory practices, such as ensuring that GDPR regulations are complied with.
Maintaining data integrity is a core focus of many enterprises, and some will employ a Data Integrity Analyst to cover this role.
Different ways that data can be compromised
- Human error; this could be either malicious or unintentional
- Transfer errors; transferring data can be problematic with unintended alterations or data compromises occurring during the transfer from one device to another
- Cyber threats; data is a target to hackers/fraudsters and those who just want to cause trouble, and are manifested in bugs, viruses/malware
- Hardware issues: if any of the hardware is compromised, such as a device or disk crash, data will be compromised
How is data integrity characterised?
For data to be used to it’s full potential, each of these six points are a vital characteristic, when each of these points is ticked, data is ready to be used.
- Accurate ?
- Valid ?
- Reliable ?
- Timely ?
- Relevant ?
- Complete ?
How is it data integrity maintained?
Ongoing maintenance will be governed by a set of regulations, practices and rules that are set out in the design phase of a system build.
These regulations and practices will firstly cover data security practices; covering topics such as data loss prevention, access control and data encryption.
Secondly they will cover the strict backup and duplication practices and rules that are required and lastly, other data integrity best practices will be covered such as input validation to prevent entering any invalid data, and error detection/data validation to identify errors in data transmission.
The most common practices for data maintenance are:
- Validate input; ensuring no typos and mistakes
- Remove duplicate data
- Control access; determining who needs and doesn’t need access to it
- Validate data; is this the right data for the right task?
- Backup data; do this regularly on a pre-set schedule
- Keep an audit trail; keep a history of what changes have been made so that they can be referred to and rolled back if needs be