Head of Data & Analytics at Bimbo do Brasil (part of Bimbo Group).

The modern age is characterized by an abundance of data. Companies generate large amounts of information, both structured and unstructured, on a daily basis. With the exponential growth of data, organizations are realizing the need for better data governance. Simply put, data governance refers to the processes, policies and practices that organizations put in place to ensure the effective management and use of their data.

While the concept of data governance is relatively straightforward, its implementation can be more complex. In many organizations, introducing data governance processes leads to increased bureaucracy. This paradoxical situation arises because data governance processes are designed to make data management more efficient, but the actual result can be more complicated. One of the main reasons for this paradox is that data governance processes often generate changes and complexities in business processes. They can involve multiple departments, stakeholders, and approval processes. In many cases, these processes are designed to be comprehensive. However, this can lead to a situation where it is difficult to know when a process has been completed and where the responsibility lies. This, in turn, can lead to delays, confusion and increased bureaucracy.

 ​In many organizations, introducing data governance processes leads to increased bureaucracy. This paradoxical situation arises because data governance processes are designed to make data management more efficient, but the actual result can be more complicated 

Another contributing factor to the data governance paradox is the lack of priorities and competition with day-to-day business activities. When there is no clear owner for the data governance program, it is easy for processes to stall and bureaucracy to build up. In many cases, data governance programs are seen as an additional burden on existing work processes rather than a valuable tool for improving data management. This can lead to resistance to change and lack of stakeholder buy-in, which can further delay implementation and increase bureaucracy.

In addition to the factors mentioned above, the lack of investment in the right tools and technologies can also contribute to the creation of even more bureaucracy. But this is not the main issue, the point of complexity lies in the difference between classic data governance and modern data governance.

Classical data governance often focuses on control and compliance. It is designed to ensure that data is managed in a standardized and structured way, following established policies and procedures. Classic data governance is often associated with strict rules and regulations and tends to prioritize the needs of the organization over the needs of individual users. On the other hand, modern data governance is more focused on innovation and decision making. It is designed to enable organizations to make better use of their data by providing the right people with the right data at the right time, with a focus on simplicity and ease of access. Modern data governance is often associated with more flexible and agile processes and tends to prioritize the needs of individual users and the business outcomes they are trying to achieve. Modern data governance also tends to be more collaborative and inclusive than classic data governance. It engages a broader range of stakeholders and encourages participation and involvement from multiple business functions, including IT, legal, compliance, and business users. Classic data governance is more focused on control and compliance, while modern data governance is more focused on innovation and decision making and tends to prioritize the needs of individual users and business outcomes.

In summary - without adequate support tools, clear owners and when trying to implement classic governance, the probability of increasing bureaucracy is high. With good tools and a good governance implementation plan, it is still necessary to use innovation and agile methods to guarantee that the focus of data governance is on goals, users and decision-making. This includes access prioritization and tools that make data easier to live with, manage and use.

Classic data governance with a focus on control generates more bureaucracy, while modern data governance with a focus on innovation and decision making may not.