Data Governance vs Data Quality

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Data governance and data quality are two different concepts. Data governance is the initiative a company takes to create and enforce a set of rules and policies regarding its data. Basically, data governance rules cover anything you can think of that concerns your company’s data. On the other hand, data quality is the ability of a given data set to serve an intended purpose. Both serve their respective roles in data management of a company, individual or government.

While these definitions explain both the terms, a little more explanation is needed let’s assume that data is water, data quality will be to ensure that water is pure and does not get contaminated, and data governance will be to make sure right people with right tools are at the right place of a water system.

Data Quality:

The idea behind data quality is pretty straight forward. It goes hand in glove with the concept of data cleansing. Basically, we are talking about data when it’s generated in the enterprise or when it comes to the enterprise and making it fit for its purpose. So, that might include things like standardizing it into various kinds of code sets or business standards. It could include fixing data that are incorrect such as a zip code that doesn’t match the city that’s supposed to be associated with. And once we have got the data in the correct form suitable for its purpose, it can be used in follow on processes like business analytics, operational processes or data mastering.

Data Governance:

Companies are struggling to get their data under control, and data continues to grow exponentially. At the same time, IT budgets are being reduced truly understanding data governance and its core components can help organizations or enterprises drive better value form its data. Data governance is the practice of defining the standards, processes or technology upon which any organization will rely on to manage its data. A comprehensive data governance strategy satisfies regulatory requirements, ensure business continuity and empower search and retrieval of all business data. Data governance occurs at the cross-section of technology, people, policies, and processes.

There are three technology components of data governance,.

  • Backup

It’s the first core component of data governance. If data that impacts the business if deleted by accident than it must be backed up. It must be easy to recover or restore data from the backup whenever it’s needed.

  • Archiving

Archiving is also another essential component of data governance. Nowadays, data comes in many forms video, audio, word document, images and many might include structured information such as a database or unstructured information such as emails and files on the disk. Archiving is essential for capturing, indexing and searching all different types of data. So, data can be readily available when needed.

  • e-discovery

Finally, e-discovery is the process of gathering electronically stored information needed for litigation or for legal investigation, proving that the complete data has been produced and then has not been tampered with is essential. Just as important organizations must prove they have data governance standards they abide by consistently. Even the best archiving and backup solutions provide little value if process adherence can’t be proven in court.

Increasing regulatory requirements have made archiving a vital component of data governance. Remember, the best data strategies ensure that the right technologies implemented hand in hand with the right people, policies and processes. In order to ensure that data is accessible, recoverable and defensible.