Here's how you can enhance data governance practices in the workplace with new technology.

Muito tempo é dispendido pelas áreas de negócio para checar informa??es ou mesmo para procurar informa??es para novos insights. A automa??o nos processos de qualidade de dados e a implanta??o de selos de qualidade e consequentemente a disponibiliza??o destas informa??es no catálogo de dados, é um passo importante para liberar as equipes para trabalharem focadas nas iniciativas de negócio. Lembrando que s?o as próprias áreas de negócio que devem ser responsáveis em definir as regras para os processos de qualidade de dados e dos selos de qualidade.

The list is long if you're looking at the width of industries X's the data required to be governed. Automated Data Classification: Anomaly Detection: Centralised Governance: Scalable Compliance: End-to-End Encryption: DLP Solutions: Monitor and control data movement within and outside the organisation. * Implement Automated Compliance Audits and Reporting * Interactive Training Modules: Phishing Simulations: Data Masking: Consent Management: Automate Routine Tasks: RPA can be used to automate repetitive data governance tasks such as updating access controls, generating audit logs, and enforcing data retention policies.

Automation is a great way to enhance any practice by removing manual routine tasks. Automation is also a great way to shore up your internal processes because it is extremely difficult to automate an undefined or ill-defined process.

To enhance data governance practices in the workplace with new technology, it's crucial to have a clear vision of the future, particularly regarding the integration of AI. AI can automate data quality checks, enforce compliance, and provide predictive insights, streamlining data governance and ensuring that data management strategies are both proactive and responsive to future challenges.

The use of data contracts can support policy management, data quality, and access management. A data contract is essentially, an agreed data structure between two parties which enables both to work effectively. These contracts are human readable - making it easy for business stakeholders to understand what is being declared. The business can then use the contracts to programatically implement access control, data quality checks, and know if schema changes will have downstream implications. ODCS is one such standard that can be used for creating and declaring data contracts.

The adoption of new technologies is key to improving data governance practices in the workplace. Automation and artificial intelligence tools can be used to monitor and manage the data lifecycle, ensuring compliance and security. Machine learning solutions enable the detection of patterns and anomalies, helping to identify risks and improve data quality. In addition, the implementation of centralized platforms facilitates collaboration and access control, ensuring that governance policies are applied consistently throughout the organization.

Enhancing the quality of the data is one pillar of data governance implementation in data management. This data quality improvement is based on different dimensional metrics such as completeness, validity, uniqueness, consistency, and more. So we can implement data governance by ensuring data quality based on data quality rules.

Parallax, the apparent shift in the position of an object when viewed from different angles, offers a rich analogy for understanding this topic. There are two perspectives from two stakeholders, your team versus the users. Imagine two triangles that share a common side or vertex. From one perspective, these triangles may appear as a simple geometric arrangement, identical, even though they are conjoined. However, as you change your viewpoint—much like the parallax effect—this arrangement can reveal new insights into their relationship. The same is true when the shared axis of automation tools and data is used to make triangles with your team or the users. Taking the perspective of one or the other offers a different view of data quality.