Agile data processing and governance for data-forward enterprises
Posted: Tue Jan 21, 2025 4:31 am
Discover 3 keys to agile data processing and governance to lead in an increasingly competitive data economy.
The strategy implemented for information processing or Data Governance allows companies to navigate the digital era with solid and reliable data. In this way, they manage to empower teams and the business to be truly data-forward and compete in the data economy by monetizing the available information.
An agile approach to data governance with automated and decentralized processes allows for more in-depth and effective treatment of data and the information that results from it. Having the right information is essential for making data-driven decisions and finding new lines of business. So, what are the keys to a good Data Governance model and what are the practical improvements to implement?
51 % of data directors consider establishing clear and effective data nurse database as their primary mission and 48% consider improving data quality.
Source: MIT Management
3 objectives for a good Data Governance model
Addressing information processing and Data Management in an agile and effective way requires the establishment of three goals from the governance function:
Recognize and minimize information bias and the presence of silos : obtaining pure information in a company is a challenge since it often appears biased in some way . To avoid this, it is advisable to consider information processing based on actions such as support in predictive tools and the promotion of self-service by users. It is also necessary to avoid information silos by promoting data integration, connection between sectors and, above all, acting from the perspective of data management and master data.
Effective Data Governance: The Guide to Minimizing Errors and Achieving Data Governance Goals
Using information for decision-making and promoting process transparency : Information is critical to properly assess how organizations are operating and to provide the metrics that reveal their true progress. This will help them understand data needs, identify areas where adjustments need to be made, and export best practices to other processes or departments.
The strategy implemented for information processing or Data Governance allows companies to navigate the digital era with solid and reliable data. In this way, they manage to empower teams and the business to be truly data-forward and compete in the data economy by monetizing the available information.
An agile approach to data governance with automated and decentralized processes allows for more in-depth and effective treatment of data and the information that results from it. Having the right information is essential for making data-driven decisions and finding new lines of business. So, what are the keys to a good Data Governance model and what are the practical improvements to implement?
51 % of data directors consider establishing clear and effective data nurse database as their primary mission and 48% consider improving data quality.
Source: MIT Management
3 objectives for a good Data Governance model
Addressing information processing and Data Management in an agile and effective way requires the establishment of three goals from the governance function:
Recognize and minimize information bias and the presence of silos : obtaining pure information in a company is a challenge since it often appears biased in some way . To avoid this, it is advisable to consider information processing based on actions such as support in predictive tools and the promotion of self-service by users. It is also necessary to avoid information silos by promoting data integration, connection between sectors and, above all, acting from the perspective of data management and master data.
Effective Data Governance: The Guide to Minimizing Errors and Achieving Data Governance Goals
Using information for decision-making and promoting process transparency : Information is critical to properly assess how organizations are operating and to provide the metrics that reveal their true progress. This will help them understand data needs, identify areas where adjustments need to be made, and export best practices to other processes or departments.