Data management is an essential part of any successful organization. To ensure that data is used effectively, it is important to develop a data management plan that provides the framework for the use of that data. In my experience, users want maximum flexibility to create their own macroeconomic vision of the world for their investment processes, consume feature-rich data, and pay only for what is used in production. Companies use big data to improve and accelerate product development, predictive maintenance, customer experience, security, operational efficiency, and more.
Senior management must make it clear that data management and security are an obligation of all employees and supplier partners. A data management platform is the fundamental system for collecting and analyzing large volumes of data in an organization. Addressing data management challenges requires a comprehensive and well-thought-out set of best practices. Data mismanagement should be punishable, since it would cost an employee a labor violation or a supplier to work with you.
Data quality management is one of those areas that can have a significant positive impact in the shortest possible time. The addition of several levels to a concept allows for a quick aggregation or deepening of these concepts through the metadata described in the fifth principle. An organization must maintain its information or data assets in a way that ensures the timely, efficient and accurate recovery of its information. Because data is an integral part of a business, data management done right helps you achieve your goals and objectives efficiently and easily.
The lack of timely data can cause a forecast bias in a backtest or backcast process (the enemy of any quantity), which could lead to false positive signs in an investment strategy. Its fundamental principles of data management should allow for an agile and flexible organization, prepared for the digital economy, assigning each principle an OKR or a KPI. In this article, we will discuss the 10 key principles of data management that organizations should follow to ensure success:
1.Develop a Data Management PlanThe first step in effective data management is to develop a plan that outlines how the organization will use its data. This plan should include best practices for collecting, storing, and analyzing data as well as guidelines for security and privacy. It should also describe best practice workflows and quality assurance procedures, including verification and validation of data.
2.Utilize Big DataBig data can be used to improve and accelerate product development, predictive maintenance, customer experience, security, operational efficiency, and more.
Organizations should take advantage of big data to gain insights into their operations and make informed decisions.
3.Establish Data Management PoliciesOrganizations should establish policies that outline how employees should handle data. These policies should include guidelines for security and privacy as well as procedures for handling sensitive information.
4.Implement Quality Assurance ProceduresOrganizations should implement quality assurance procedures to ensure that their data is accurate and up-to-date. This includes verifying and validating data as well as regularly reviewing it for accuracy.
5.Utilize MetadataMetadata can be used to quickly aggregate or deepen concepts by providing additional information about the data. Organizations should utilize metadata to gain insights into their operations.
6.Maintain Data AssetsOrganizations must maintain their information or data assets in a way that ensures the timely, efficient and accurate recovery of its information.
This includes regularly backing up data as well as ensuring that it is stored securely.