7 best practices for effective data governance

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Often confused with information management, data governance encompasses the different procedures and policies put in place to ensure that a company's data is accurate and properly managed throughout its lifecycle, from its ingestion, storage and access to its disposal.

Although having a data governance policy has numerous benefits, many companies still do not know how to do it or delay its implementation.

In this article, we explain best practices for developing an effective data governance methodology.

Why data governance is important
In the information age, proper data management is the key to business success. Only those companies that manage their data correctly will achieve good business performance and remain competitive.

However, in order to achieve effective management, records must be properly governed. What does this mean? It is necessary to establish a set of principles and practices that ensure the quality , integrity, security and availability of information.

Determining a data governance czech republic phone number lead framework therefore involves developing an organized structure in which the responsibilities and processes necessary to ensure proper use of data and promote its democratization are defined.

By having a data governance policy , companies can:

Make accurate and confident decisions, backed by solid information
Ensuring regulatory compliance
Mitigate the risk of information misuse, sensitive data exposure, and security breaches
Create a reliable and quality database
Improve productivity
Driving innovation
Reduce costs and optimize resources
7 best practices for achieving an effective data governance strategy
Implementing an appropriate data governance policy is key to ensuring the quality of information , unlocking its potential and obtaining competitive advantages in the market.

But how do you develop it? Below, we share some best practices to achieve this goal.

Think big and put people first
If you have never implemented this type of initiative, it is important to know that data governance combines people, processes and technologies.

While it is always necessary to keep the big picture in mind, you have to start with the people. Once the teams, responsibilities, objectives, skills and training have been defined, you start building the processes and, finally, incorporating the technology.

Respecting this path is essential, since without the right people, it is difficult to create the processes necessary for the technical implementation of data governance. Once the right human resources are involved, they will be able to build processes and provide guidance on the technological tools necessary for the project.

Knowing all the data and organizing it
When starting a data governance project in an organization, you must begin by knowing the scope of all the data you have and where exactly it is located.

All data needs to be considered, including metadata and unstructured data obtained through different tools, because achieving effective governance practices is difficult when you don't have the full picture.   

Do you already know the origin, location, categories and criticality of all your data? It's time to organize it, systematizing it based on its level of sensitivity, current policies and business context, establishing a single source of truth.  

Having a clear data organizational structure lays the foundation for good information organization.

Analyze the evolution of data throughout its life cycle
An effective data governance strategy tracks information at each stage of its development, through different policies that impact the acquisition, storage, transfer and deletion phases.  

Policymakers should break down the data governance process into different steps to see how they interact with each other and create a cohesive strategy that spans the entire organization.

On the other hand, to ensure that processes are executed correctly and to detect and rectify any violations at the source, it is essential to establish data ownership or administrators at each stage of the data life cycle.