The differences between active and passive data governance


Is it better to monitor for quality or detect problems? It depends. Here’s how to choose between active and passive data governance.

Young team of marketing department are looking over the data
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The goal of data governance is to enable data operations that can help deliver great business value while simultaneously supporting data quality, security, compliance and privacy. To achieve these data governance goals, there are two types of data governance that business can use: active and passive.

What is passive data governance?

Traditionally, the more commonly used data governance framework has been passive data governance, in which the first step is data input by users. For most organizations, data is added to an enterprise resource planning (ERP) system.

All governance policies and procedures are applied after the data has been input. This includes creating exceptions, cleaning operations, creating workflows, and identifying and removing duplicates. Passive data governance is considered the more reactive approach.

SEE: Electronic data disposal policy (TechRepublic Premium)

What is active data governance?

Active data governance means data verification happens before data is input into the system, typically as soon as the data is collected. This helps ensure the veracity of data and that data quality meets the standards of the organization.

It also ensures data meets quality standards as soon as it’s added to an accessible business database. Active data governance is considered the more proactive approach.

What are the differences between active and passive governance?

The primary difference between these two types of data governance is that with active data governance, the data quality check happens at the beginning of the data’s life cycle, as soon as it’s collected. With passive data governance, data quality checks come at a later stage of the data life cycle, usually once it’s already stored in an accessible data platform.

As the quality of data is not assessed before data enters the system, passive data governance can result in errors and discrepancies. It can also lead to duplicate or incomplete data, which is not ideal for an organization that wants to be agile in its decision-making processes.

For companies in heavily regulated industries, passive data governance can be risky, as corrupt or incomplete data could lead to non-compliance. Passive data governance also uses up precious resources to clean the data after it has entered the system, which can be more time-consuming and challenging compared to cleaning the data before it is input.

Which governance model is best for your business?

An organization that is new to data governance can start by building a passive data governance strategy, which is ultimately an easier and still-beneficial data governance framework to have in place. As data needs become more sophisticated, the organization can consider moving to active data governance, which has some clear advantages in terms of efficiency and reliability.

Companies in heavily regulated industries, such as those in the pharmaceutical space, might need to use an active data governance framework to validate all data before it enters an ERP system. Implementing an active data governance system can be more challenging, but the rewards can be worth the effort.

Regardless of whether you choose an active or a passive model for your data governance strategy, you’re taking an important step in the right direction. Having some form of data governance is better than having no data governance framework at all.

No matter what data governance model you choose, the right data governance tools can support your business in its data governance strategy. Take a look at these top data governance tools and learn how they can help your business.



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