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What is DataGovOps? Why is it relevant today?

DataGovOps is a relatively new concept that effectively applies a DataOps-like approach to enable the safe use of enterprise data at scale. The role of DataGovOps is to provide enterprises with continuous governance and improved compliance spanning the data lifecycle.

Manual processes in governance are time-consuming, error-prone, and often crippled with inefficiencies. DataGovOps uses a “governance-as-code” approach to convert manual processes into code-based or script-based governance.

By implementing DataGovOps, companies can build and scale a data-driven culture for their business.

Please continue reading to learn more about DataGovOps and its relevance in today’s data-driven ecosystem.

Need for DataGovOps

Data is an empowering tool for businesses. However, data is only useful if its value can be continuously captured while meeting quality goals, complying with laws, and protecting privacy.

Enterprise data continues to grow exponentially as data storage becomes inexpensive, and data infrastructure is democratized. On the other hand, data privacy laws have become ever stricter, and newer data regulations are emerging every few months.

Data teams using manual governance processes are often caught in an endless loop of managing an ever-increasing stream of data volume and types. Unfortunately, in most organizations, data teams don’t have the time to focus on data governance. As a result, data governance often takes a backseat or is ignored completely, leading to issues such as poor data quality and compliance.

What is DataGovOps?

DataGovOps, as mentioned above, is based on similar principles as DataOps. However, to better understand DataGovOps, let’s start with data governance. In simple terms, data governance focuses on creating a data catalog or business glossary, understanding data lineage, providing clear and precise data quality definitions, ensuring data security, tracking privacy requirements, and clearly defining roles and responsibilities. DataGovOps codifies governance to automate it. In other words, DataGovOps enables the process of governance automation.

DataGovOps — Why is it Relevant today?

Stricter and highly complex data regulations, such as GDPR and CCPA, make managing data and data sources highly challenging for enterprises. In addition, the data stack is constantly evolving with new solutions and platforms. As a result, data engineers tasked with addressing data governance are faced with increasing complexities since they have to work with a wide range of data solutions and platforms.

Moreover, today, data processing is not simply limited to the data captured by a business. Instead, businesses enhance the data with external data sources and data shared with partners. Enterprises should ensure that these new and complex data ecosystems are subject to data governance principles.

AI models further complicate data governance, with the added focus on data ethics — should someone be appointed to keep track of the decisions that AI models drive?

These challenges and complexities make data governance framework difficult to plan and implement. Consequently, businesses are reluctant to focus on data governance. Data governance automation vis-a-vis DataGovOps can address these challenges.

Considerations in deploying DataGovOps

There are two important considerations businesses will have to take into account when it comes to implementing DataGovOps.

Today, the concept of individual responsibility is somewhat outdated because decision-making involves multiple people in a distributed team. Hence it is the team that must be empowered, as opposed to select individuals. Without careful planning, DataGovOps can overburden data teams that are already overworked. For companies with a skeletal data team, the challenge can be huge, potentially resulting in a situation that DataGovOps aims to prevent, in the first place.

Another consideration in implementing DataGovOps is to start viewing data governance as a whole, and not simply as a tool for reducing risk and ensuring compliance. Instead of making it all about compliance and results, DataGovOps should be more focused on the ease of use for the end-user.

Conclusion

The idea that the sole focus of data governance is to reduce risk and ensure compliance is no longer viable, as this can impede creativity and innovation. DataGovOps offers businesses the option of safe data usage via automation, resulting in improved data governance. It also frees up data teams, as they no longer have to waste time on repetitive manual tasks.