Key data governance practices

With these practices, you can improve the way you manage data across the organization.

A recent report on the state of data governance shows that 98% of the organizations surveyed consider data governance to be important, and about 50% have already begun their implementation. They did so either through their own employees or through an external company providing data governance consulting services.

Effective data governance generally extends across the organization, including human resources, processes and IT systems. When it comes to corporate data governance, small businesses and start-ups often back down, intimidated by the size of the project: “At my level, the profits would hardly exceed the necessary investments.

In reality, it is a false stereotype. Data governance is more a way to process and think about your data than a long and tedious effort to complete at the same time. It does not require as much effort on a smaller scale, while offering tangible benefits. Let’s take a closer look at some key practices and their impact on your IT ecosystem.

What are the main data governance practices?

Data governance involves a number of practices to apply to how you manage data across the organization, both from a process and IT landscape perspective.

From an IT perspective, data governance means that you need to design, document and execute the following strategies and models across your IT ecosystem:

  • The spine
  • Data model

It’s an integrated model that defines the types of data your organization produces and consumes. This model is generally organizationally independent and independent of the system. It is also mapped to your IT landscape to define which systems exploit each type of data. Setting up such a model can help you minimize data redundancy and maintain data consistency and quality.

Data management practices

These are the main rules governing data processing. They include data storage, organization, backup and preservation strategies. It is important that the practices you apply also include reprocessing, modifying/transfer, lineage and retention policies, as they are essential to maintaining data quality and helping you achieve all the benefits mentioned in the previous section.

Essential Practices and Strategies

Data access strategy

This strategy defines the types of information your organization works with and how it is collected, stored and stored. It also focuses on how data consumers (users and software systems) access each type of data (via an API or direct access to the database). This is essential for regulatory compliance because it centralizes each user’s data and ensures the interoperability and maintainability of the system.

Data Governance

Import/data export model

Data governance practices require that each integration point be fully documented, including its data model and nature, whether it is “push” or “pull,” the protocol and timing it uses, and the interface it has.

This model reduces maintenance efforts and helps protect data integrity, system interoperability and substantially.

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