WAYS TO ENSURE DATA INTEGRITY
Ensuring Data integrity Means custodizing and maintaining the Accuracy, Validity and consistency of data in a database, data transition from the Source/creation to the recepient and assurance of data utility, Data integrity has become a major institutional challenge as big data analytics increasingly drives decision-making. To guarantee data integrity, organizations need to establish strong quality management practices that will help protect and maintain data during collection, processing and storage. ways to Preserve data integrity includes:
1. Controlling access to data via security mechanisms.
2. Designing a user interface that prevent the input of invalid data
Using error detection and correction software when transmitting data.
3. Reorganizing the database to remove old and deleted data and logs.
4. Managing and reviewing log files to trace odd program behavior or system access events.
5. Cleaning up leftover maintenance data by removing unneeded files which will allow your database to respond faster.
6. Deleting historical data related to backup and restore activities to help your database to run more efficiently.
7. Backup your database regularly in order to recover from upgrades that don’t play nice, corrupted tables and other system problems that can lead to full-on disasters.
8. Data Cleaning and Maintenance: A data cleaning approach should Satisfy several requirements. First of all, it should detect, eliminate or Correct all errors and inconsistencies. It should also be a continuous Process that supports system health in order to maintain data integrity.
9. Data Entry Training & Accountability: Data integrity starts at the source And the user. Manual data entry can result in human errors that Compromise results meant to guide business decisions. That’s why it is Vital that staff members with system access are properly trained on Data entry and upload protocols.
10. Data Validation Rules: By using data validation rules, administrators can ensure data integrity by controlling and restricting the values that users can enter into their system. By protecting information from accidental alteration, validation rules provide additional security and data quality assurance - a natural requirement for accurate analytics.
The Cost Of Poor Data Integrity; And Poor Quality Of Data Integrity Results In:
Compliance Failure
Loss Of Crucial and Sensitive Data
Manual Rework Cost to Fix Errors
Inaccurate Insights
Loss Of Trust
Failed Initiatives and
Lost Of Opportunity
Elsevier (2011) Opined That the important Focus in most big data is on data input processing and analysis of large volume of data. data integrity and quality issues starts surfacing during the data analysis and operation stages. Research estimates that an average of 25 - 30% of Funds meant for any big data project is spent on identifying and fixing data quality issues. In an extremely scenerio where data quality and integrity issues are significant, projects gets abandoned and that is very expensive loss of capability and trust.
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