To achieve this, it is essential to understand that Data Governance focuses on managing data throughout its lifecycle, ensuring that it meets the required quality standards to contribute to the digital transformation process of companies.
Key aspects to consider for its success include:
- Treating Data Governance as an iterative process: This means viewing its implementation as a continuous feedback loop, necessitating companies to envision it as a project spanning both short and long terms.
- Paying attention to regulatory policies: Constant vigilance towards norms and regulations set forth by governing bodies is crucial. Processes must adhere to these standards, often monitored and enforced by regulatory entities.
How to identify the most appropriate path?
Keeping strategic objectives and long-term plans in mind, it is advisable to seek guidance from expert consultants for implementing a mature data model that facilitates the desired progress.
How to define implementation cycles?
Maintaining consistency and realism, it is advisable to devise a timeline of actions to address stages such as evaluation, dashboard creation, workflow management, rule sets, and establishment of commercial terms.
Do not forget to focus on data management and administration. Utilize data management programs to centralize information and generate reports, serving as inputs for various organizational departments.
Lastly, measuring success in the initial stages of the process allows for adjustments to be made. Initial data and process traceability serve as benchmarks for subsequent reports.
IT should not be solely responsible for this information. While IT plays a significant role in Data Governance, all business areas must be involved as they provide requirements, knowledge, needs, information management, and data control.
Communication enhances the development of this implementation. Despite bringing few internal changes, communicating the implementation of Data Governance within the organization helps improve internal communication, stakeholder participation, sponsorship, executive support, among other benefits.
It is important to note that various software companies, like ours, assist organizations in consultancy and tailored implementation of Data Governance models.
At BPT, we begin by analyzing the strategic objectives of the company, actions already implemented, available resources, and identifying needs. Subsequently, we propose a Data Governance model tailored to these requirements to support the implementation process.
