Data Governance: Definition and Objectives

The concept of Data Governance has gained significant importance today for one main reason: in a context where massive data is becoming a true asset of value for companies, a discipline that manages and maximizes its potential is indispensable.

Conversely, not implementing good Data Governance practices can lead to uncontrolled situations that may even be harmful to organizations.

The importance of Data Governance tasks is becoming decisive, as indicated by market value figures. It's estimated to grow to $6.71 billion by 2028, at a CAGR of 19.72%, according to Mordor Intelligence.

Despite this crucial importance, it's common for tasks around Data Governance to be neglected, if not outright unknown. Therefore, today we provide a comprehensive review of this discipline: what is data governance, why apply it, and how to do it? Let us tell you.

What is Data Governance?

Data Governance includes a set of practices used to manage, protect, and ensure the quality of data within an organization.

Thus, it involves a series of processes aimed at ensuring that the data an organization holds are accurate, consistent, secure, and available when needed.

There are many ways to understand data governance. For example, the Data Governance Institute speaks of it as a "system of decision rights and responsibilities for information-related processes." This opens the door to understanding Data Governance as a systematic process that includes aspects such as policy definition, metadata management, and data quality control, among many others.

Principles Governing Data Governance

Data Quality

Data Governance practice involves ensuring that data are accurate, complete, consistent, and free of errors. They should also be relevant and timely for the organization's intended use, all established through processes aimed at guaranteeing these qualities.

As part of Data Governance quality, compliance with relevant regulations and laws, including data protection laws, must be ensured.

Data Security

Data security is crucial for protecting sensitive and confidential information and is a pillar of data governance. These practices include implementing security measures, such as encryption, access control, and password management, to prevent unauthorized access or data theft.

Transparency and Auditability

Transparency in Data Governance implies clarity regarding participants and auditors in all processes. At the same time, auditability is necessary so that processes can be tracked and accompanied by precise documentation.

Responsibility

Within Data Governance processes, it's considered that anyone involved is a data steward, regardless of their position. Thus, responsibility regarding good practices is common, establishing a culture of collaboration.

The Purpose of Data Governance and Why to Apply It

• Create a framework of rules that ensures the quality of data, its management, and security
• Resolve problems in this area
• Monitor compliance with rules and initiatives around Data Governance, including BI/Data Warehousing or MDM
• Facilitate data migration processes
• Compliance with regulations and laws
• Reduction of business risks and costs
• Boost business agility and flexibility
• Guarantee access to quality and relevant data
• Avoid data silos within the organization and harmonize systems
• Maximize security and transparency and prevent misuse of personal or confidential data

In short, data governance ensures that the organization has access to quality, accurate, and consistent data, also acting proactively to achieve this.

In this sense, not incorporating Data Governance policies has a direct impact on key areas such as development or Quality Assurance. An impact like project delays, data duplicities generation, inconsistency between systems or information sources, among other problems.

Thus, this lack is particularly evident in Test Data Management projects, as they require connecting multiple systems together. In this way, the lack of initiatives regarding Data Governance poses one of the main challenges in data segmentation processes.

When and How to Apply Data Governance

There are multiple scenarios indicating that an organization would benefit from moving away from informal data management and applying Data Governance:

• The organization's own growth and the complexity of the data it holds make traditional data management overwhelming.
• There's a detected need to launch multifunctional programs that move beyond data silos within the organization.
• A formal Data Governance is imposed for compliance with legislation or contractual requirements.

In this regard, successfully launching a data governance initiative is essential to have experts in the field. Experts capable of structuring a coherent implementation process with the complexity these types of systems entail.

Thus, some key steps in this implementation include:

1. Defining action objectives: clearly defining the objectives of Data Governance, which may include improving data quality, data security, or compliance with regulations, among others.
2. Establishing the governance structure: creating a team responsible for overseeing and directing all initiatives. Ideally, the team should include representatives from different areas of the organization, as well as Data Governance experts.
3. Identifying data owners: this step is important as the owners, responsible for the data in their respective areas, must be involved in data-related decision-making.
4. Creating data policies and standards: a crucial part of Data Governance is developing clear policies and standards for data management, addressing aspects such as data quality, privacy, security, and access. This step includes conducting a thorough assessment of the data quality existing within the organization, identifying potential issues with integrity, consistency, accuracy, and completeness. It also involves the creation of a data catalog or dictionary that documents the data sets available in the organization, as well as their origin, purpose, and the policies applied to them. This creates a centralized registry of the data.
5. Implementing access controls: defining who has access to the data and what type of access is granted, implementing security measures and assigning roles and permissions.
6. Monitoring and auditing: finally, Data Governance involves implementing monitoring and auditing processes to ensure that policies are effectively complied with.

In this entire process, it's also essential to have the deployment of suitable IT tools, capable of ensuring compliance with Data Governance policies and reducing costs in the process. If you're looking to launch a Data Governance process in your organization, icaria Technology can help. Contact us, and we will introduce you to our Data Governance tool to carry out this process.