How to counter shadow data governance

Shadow IT is a term used when IT is not involved in technology and invokes incorrect or different reactions based on who one asks. In fact, it is also a shortcut implemented by developers to get agile workflows. Lately, big organizations are seeking to close such a gap and increase awareness against it. Developers have learned the ways to deliver tools to match the needs.

data governance

Causes of Shadow Data Governance

Shadow data governance occurs when data analysts or data scientists make a copy of the data from the IT-approved primary database to run the tools in their different environments. Many fail to get the IT approvals.

The question here arises of how to address the shadow data governance challenge. Understanding the root causes is important. Big corporate houses spend heavily to stand up such a vast database and data scientists dump these into a CSV file.

Speed is the primary pain to shadow data governance along with a lack of flexibility while processing and tooling. Often the data is considered artifact management and this has worked perfectly for analytics workflows for much traditional business.

Today’s data for businesses are more varied like one-size-fits-all data solutions. The tools are now more influential. The shadow data turns tension between the unset requirement of data scientists as well as the process of traditional IT approaches the data governance.

New Risk

All the organizations are suggested to have data risk and perfect governance policies in place to help the correct data governance. However, the leaders need to know that force-fit models may not work in the AI era. The IT teams serve the needs of the business as well as data scientists.

New Tooling

Creating a data governance policy is not solved just by purchasing a shiny new tool. Some updates could be important. The actual implementation however differs from one to another. Meanwhile, many are underlying principles of similarities.

Shadow Data Governance and Technical Debt

Data scientists and data analysts spend enough time on the data preparation tasks such as data loading and data cleansing. They are tempted to save time by using shadow data governance. However, they need to know that such short-term solutions lead to technical debt in the long run. If it continues, someone would be paying the debt down.

Organizations are suggested to rethink on the data management strategies to see the full benefits of data governance. They need to set up a proper team to look after it.


Data governance is the call of the time for organizations and proper data governance helps in filtering out unwanted and errors. It also protects the data from getting misused. The work is challenging and the greatest challenge is to make the data accurate, consistent and accurate.

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