Why is Data Governance so Important in Data Management?

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Why is Data Governance So Important in Data Management?

Data is the single most important asset to any business. It allows organizations to identify new business opportunities, engage customers, and generate value. A typical business needs to deal with significant volumes of data. As such, effectively managing this data is vital for success and securing a competitive advantage.

Data management and data governance are crucial in ensuring your data assets remain secure and easy to always retrieve. Sometimes, these two terms are used interchangeably. However, they’re distinct data strategies that achieve different goals. Need some help making sense of the two? Below, Atkins explores what sets these two processes apart, along with how they impact each other.

Data Management explained

Data management is a broad term. However, it typically refers to the design and implementation of processes required to handle data. This includes everything from data generation to data retrieval. Furthermore, it also includes best practices for the disposal of data. With robust workflows in place, a business can easily manage large volumes of data efficiently.

It’s not just enterprise-level organizations that need to take data seriously. Even medium-sized operations should be viewing data as a business-critical commodity. To manage data effectively, businesses must understand the key pillars of data management.

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Key components of Data Management

One of the most important elements of data management is data preparation. In short, data preparation involves handling raw data and refashioning it for analytical purposes. It’s tempting to skip this stage and rely on raw data. However, making long-term decisions based on unprepared data is never a good idea.

Next, there are data pipelines. This involves the transfer of data from one place to another. During this transfer, data is optimized, ensuring it can be readily analyzed once it reaches its destination. There’s also data extract, transform, load (ETL) to consider. ETL is essentially an automated data integration process that’s essential for data transformation.

Other key elements involve the creation of data catalogs. This is important for providing businesses with clear oversight of all their stored data. A more formal approach to data flow structures is also required. This is often referred to as data architecture. Once organizational best practices have been determined, data usage and retrieval enjoy significant benefits.

Data security processes are also essential. These systems ensure that data is always protected from third-party attacks and unauthorized access. What’s more, it prevents accidental data loss and alterations that may skew records.

The creation of data warehouses is yet another key aspect. This involves consolidating multiple data sources and redirecting all incoming data traffic to a single respiratory. This not only allows for easy retrieval but makes analyzing data far simpler.

Finally, there’s data governance. This is a complex set of processes that ensures data is always up-to-date and readily available. Data governance policies also ensure that data management practices are compliant with the latest regulatory guidance.

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Data Governance explained

Although data governance falls under the banner of data management, it deserves its own definition. Ultimately, data governance refers to how data is managed and processed. It can be used to determine things like data ownership and access rights. Furthermore, it can assess what security measures have been put in place to uphold data privacy and protection. More importantly, it ensures that data management processes are fully compliant.

Without a dedicated team in place, most businesses will struggle with data governance. Ideally, a department should be overseen by a governance management team. It’s this team that will spotlight data awareness and champion transformation.

In addition, a data governance department should include a steering committee that takes charge of implementing new policies and putting plans into practice. Finally, there are data stewards. These are employees that handle day-to-day data operations.

Why Data Governance is vital for Data Management

Despite being an integral part of data management, as many as 38% of organizations aren’t following data governance best practices. However, any business lacking effective data governance is setting itself up for failure.

For starters, a lack of consistency between departments makes data processing a challenge. Left unchecked, unintegrated will continue to cause problems, making it impossible to conduct reliable analysis.

Mismanaged data also runs the risk of a business falling short of regulatory compliance. Data protection laws are changing all the time, making the role of data governance teams even more essential.

Final thoughts

If data governance isn’t a key component of your data management process, now’s the time to rethink your approach. In short, robust data governance ensures that your data is consistent, easy to use, and accessible.

Furthermore, you can be confident that the data you’re using is accurate and use it to make informed long-term decisions. It also means your business is adhering to strict data protection protocols and adhering to the latest regulatory guidelines. Without a data governance team in place, it’s almost certain that you’ll encounter issues with data processing, data reporting, and analysis.

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Roy M is a technical content writer for the last 8 years with vast knowledge in digital marketing, wireframe and graphics designing.


  1. Compelling insights! This piece succinctly highlights the critical importance of data governance in effective data management. A must-read for data professionals.

  2. Data governance is vital in data management as it ensures accuracy, integrity, and security of information. By establishing clear policies and procedures, it fosters trust, enhances decision-making, and maximizes the value of data assets, contributing to organizational success and innovation.

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