A Quick Essential Guide to Data Governance

In today's world, where businesses are often digitally evolving, the need for a data governance framework has become an absolute necessity for companies. The power of data to manage projects and make data-driven decisions has been demonstrated over the years since the volume of data flows increased significantly in the early 2000s.

According to the latest available data from Statista.com, about 408 Terabytes (TB) of data were created every day in 2024, for a total of 147 Zettabytes (1 ZB equals to 1 billion TB). Projections indicate that by 2025, the total amount of data created worldwide will reach 181 Zettabytes. In 2010, the data level was only 2 ZB. Internet connectivity and the proliferation of IoT devices have played a key role in this surge.

This is why every organisation, regardless of its size, must have a data governance policy for its employees and be in the best possible position to face the challenges of the future.

On this page, you will learn the key concepts of data governance which will help you to get a real understanding of it.

Introduction to Data Governance

First, let’s see what Data Governance means:

Data Governance is a process designed to develop and manage the way data is used and controlled in enterprise systems. It is based on internal standards and policies created by a wide range of employees.

An effective data governance framework ensures for the companies, data integrity, security and compliance with regulatory requirements. It also helps employees to have a common vision of data use across all IT systems and business departments.

The ultimate gain for organisations that adopt data governance is the improvement of the decision-making process based on Key Performance Indicators (KPIs) based on qualified data collection.

There are a set of principles that are essential for any organisation wishing to build its data governance.

Core principles of Data Governance

One of the most established principles of data governance is data ownership. It refers to the possession of the responsibility and the acountability of the information. Meaning the power to access, create, update or delete the information. He also has the ability to grant and deny privileges to that information.

This is why it is an absolute necessity to specify the person with the ownership of each dataset and associated information when developing the data governance strategy.

Another conventional rule is transparency. Unavoidable to build trust between employees by improving collaboration and to make stakeholders feel more confident. Also, a well-documented process help to minimise risks such as errors, fraud or risk of non-compliance.

In this context, the Data Steward plays a key part in controlling the accuracy of data and overseeing the guidelines are well followed by IT and businesses teams.

As you might expect, data integrity and security are extremely important. Not only to meet regulatory standards, but also because breaches or leaks can have a lasting public impact on organisations. Customers may lose confidence in you if you fail to protect their information.

Data governance policies include a full part to the technical tools used to manage data flows. This means using common softwares and development stacks. Imagine for a moment that you work in the marketing department of your company, and you have to collaborate with another department that uses a total different software than yours to analyse the latest customer trends. It would be a total mess isn’t it?

To avoid this scenario, each department need to be aligned concerning the tools and software used to manage the data flows. A group of people can't decide for themselves which tools to pick. They have to follow the guidelines set by the company.

Another best practice is to create KPIs to monitor the data flows. The goal is to review how data is processed within the organisation and then provide recommendations to evolve the data governance framework and adapt to new trends.

In doing so, it will be easier for employees to master the strategy, adhere to compliance requirements, and efficiently manage data quality and security aspects within their daily operations. The continuous improvement is very useful for adapting strategies to new technological needs and initiate data innovation

By following these principles, organizations can establish a strong data governance framework that enhances efficiency, security, and compliance.

Overcoming Data Governance Challenges

As with all projects, running a data governance strategy can be quite challenging in certains situations. The most impactful is the lack of executive buy-in, where you don't get proven interest from senior management and struggle to gain human and budgetary resources. To overcome this, it's important to align with business targets by building a strong business case that highlights the benefits of data governance.

In addition, providing regular reports with accurate metrics is generally appreciated by leadership, as is the success of small initiatives that demonstrate the value of data.

Another risk is the implementation of data silos that are not connected to each other, making data analysis more complex than ever and resulting in inaccurate reports. Therefore, consistent guidelines are important to have. The solution is to create a data culture to educate employees, convince them on the importance of standardising data for better outcomes.

Adopting an agile approach can also help organisations to deliver quick results while continuously improving governance processes with quick wins.

Building a strong Data Governance Strategy: Key Steps

On the development path to data governance, there are several steps to create a data governance strategy for organisations. Of course, It can't be built in one go. The duration depends on the context and the size of the organisation, it can take a few months or several years with good motivation, patience, openness and agility. Keep in mind that if you take it one step at a time, it will be easier for employees to adhere and be more productive with the concepts of data governance.

Remember, taking it one step at a time will make it easier for employees to adapt and be more productive with the new data governance.

The following is a brief overview of the key steps in developing an effective data governance strategy and making data-driven decisions.

By adopting an agility-driven data governance framework, organisations can achieve rapid results, enhance decision-making based on qualified data, and seize new opportunities—all while maintaining compliance and security in a frequently changing digital landscape.

Data Governance is a vital asset for the future

Customers constantly have new needs and ambitions, and organisations must adapt accordingly. In the coming years and decades, data will become a strategic asset that drives innovation, competitive advantage and business growth.

Companies that effectively implement a data governance strategy will be able to anticipate market trends and optimise their internal processes, while ensuring data quality, security and compliance with evolving regulations. For example, the European Union has adopted the General Data Protection Regulation (GDPR), which governs how the personal data of individuals in the EU can be processed and transferred.

The rise of AI solutions, particularly in the form of generative AI, is pushing companies to find new ways to respond to their customers. From a technical point of view, AI models are trained using data. Therefore, in order to have powerful AI tools. Data governance must be established to ensure the quality and reusability of the data sets used by AI models.

Otherwise, the performance of AI tools would not be as high as expected. Organisations that invest in scalable, agile and secure data governance practices will not only adapt to change, they will drive innovation and create new business opportunities in the digital age.

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