Data for any enterprise has a critical value as a business asset. Key business decisions rely upon timely data access. However, unlike tangible business assets, data is prone to degradation. Moreover, neglect of data hygiene leads to security issues including failure to meet regulatory compliance.
Data hygiene in essence equates to data cleanliness by ensuring all company data adheres to consistency and accuracy measures. Regardless of where the data storage takes place or how the data usage occurs, maintaining data hygiene depends on the following particulars.
- Timeliness – Ensuring date is relevant and up-to-date.
- Accuracy – Data is free from mistakes.
- Complete - Ensuring completion of data and validating no missed records.
- Consistent – Ensuring that across the company networks all systems employ a consistent data format.
- Uniformity – Establishing uniformity with naming conventions and data inputs across all datasets.
- Validity – All data standards and pre-set rules must be followed.
For the management of the above particulars, a series of techniques and tools can be utilised by businesses to build data hygiene. Coupled with automation capabilities, the automated systems allow data cleansing and profiling to detect anomalies and resolve issues.
Without proficient data hygiene, maintaining operational resilience and efficiency becomes difficult. For the reason that multiple digital sources exist, a challenge that exists for businesses focuses on how to keep sight of their data collection, access and storage. Thus, organisations quickly lose visibility and control over sensitive information.
A second problem of shadow data arises. In this case, enterprises struggle with determining undiscovered or unknown data which could pose security risks. At the same time, upholding data integrity requires execution of checks and balances to confirm no tampering has occurred. Only under the circumstances of encompassing data integrity can a strong cyber security posture prevail.
A critical part of security rests upon the classification of data in particular sensitive data. Accordingly, classification allows the protection of sensitive data from breaches and attacks by prioritising the security of those databases.
As a continuous process data cleansing depends on quality checks being integrated throughout the lifecycle from collection, accessing and storage of data. For proactive data maintenance companies should aim to implement the following practices:
- Invest in data quality solutions and tools: Automated tools which detect data inconsistency such as duplication, incomplete records and validate processes assist with real time data cleansing.In addition, the tools free up time and resources for teams by promptly identifying issues.
- Adopt security-first culture: Establish a culture where data security, integrity and business continuity are second to none. Commence training sessions for employees on data management standards, encryption and monitoring quality.
- Develop data governance policies: Enforce policies as well as delegating roles and responsibilities to the relevant stakeholders. Also, establish mechanisms for reporting and accountability to ensure strict compliance with regulations.
