Any good coach will tell you that these three factors are key to success in sports.

Quick ball, accurate passes, and consistent defence are the keys to victory.

Similarly, for your data quality processes, these three are key. Businesses that prioritize investments in enhancing data quality often outperform their competitors in terms of efficiency and effectiveness.

Accuracy

Does your data quality solution ensure accurate data, irrespective of how or when it entered the environment!

It is no longer good enough to rely on a batch/etl process to cleanse data as it moves between systems.

For many organisations, this approach means that source data can never be cleaned – as legal, political or system complexities inhibit the ability to cleanse data at source.

These companies are turning to real-time data cleansing platforms that validate, correct and identify duplicate records before it is captured into the database.

If you fix the problem before it enters the source system the complexities inhibiting cleansing are largely removed.

Consistency

Does your data quality solution support the reuse of business rules and data cleansing processes across your enterprise?

Can a business data steward define business rules in an easy-to-use interface and share those rules for deployment to your ETL processes, your web services, your ERP, your MDM applications and your legacy environments.

Can this be done without redevelopment and the possibility of errors or misinterpretations?

The only way to ensure that data is handled consistently (and therefore to retain accuracy) is to use the same rules everywhere.

Speed of Execution

Data validations must not impact your users – they should not have to wait for data validations to occur.

If the solution selected requires exorbitant hardware support to run quickly in your environment, or cannot scale to handle your data volumes then it cannot solve your problem.

Has the solution you are looking at been specifically designed for high-speed execution with a minimal hardware footprint?

If not, you should consider an alternative.

How does poor data verification impact accuracy?: Delve into the implications of poor data verification on accuracy and understand its impact on decision-making processes.

4 steps to data quality: Implementing these 4 steps to data quality can significantly enhance the reliability and usefulness of your data.