What is data management in project management?

Data management is the practice of collecting, organizing, protecting, and storing an organization's data so that it can be analyzed to make business decisions. As organizations create and consume data at an unprecedented rate, data management solutions become essential for making sense of enormous amounts of data. Data management is the process of ingesting, storing, organizing, and maintaining the data created and collected by an organization. Effective data management is a crucial part of implementing IT systems that run business applications and provide analytical information to help drive operational decision-making and strategic planning by corporate executives, business managers, and other end users.

A data management plan (DMP) is a document that defines how data is managed throughout the life cycle of a project, that is, from its acquisition to its archiving. While these documents are typically used in research projects to meet funding requirements, they can also be leveraged in a corporate environment to create structure and alignment between stakeholders. Because DMPs highlight the types of data that will be used in the project and address their management throughout the data lifecycle, stakeholders, such as government teams, can provide clear feedback on the storage and dissemination of sensitive data, such as personally identifiable information (PII), at the start of a project. These documents allow teams to avoid regulatory and compliance issues, and can serve as templates for how to approach and manage data for future projects.

The multitude of databases and other data platforms that are available for use requires a careful approach when designing an architecture and evaluating and selecting technologies. Data management systems help solve the problem, ensuring that the company has accurate and reliable information by addressing the capture, maintenance, security, administration, categorization and access to data. But in larger teams, data management teams usually include data architects, data modelers, DBAs, database developers, data managers, data quality analysts and engineers, and ETL developers. Data management is more important than ever as companies undergo digital transformation and automate their business processes.

Data management refers to a set of activities to efficiently collect, organize, protect, and store company data. A good data management system facilitates collaboration between researchers and helps manage compliance with regulatory and other standards. As you can probably imagine, a lot of other changes often occur in a living system, and ensuring that the data remains accurate can require significant effort. Data management systems are specifically designed to address the challenges presented by high volumes and speeds of data.

In the case of data that is not confidential in nature, it is expected to provide a way for third parties to access raw data and research results. DAMA International, the Organization of Data Governance Professionals and other industry groups also offer guidance on best practices and educational resources on data management disciplines. Mainframe-based hierarchical databases became available in the 1960s, bringing more formality to the burgeoning data management process. Data environments are not static: new data sources are added, existing data sets change, and business data needs evolve.

Since project stakeholders expect sensitive data, such as personally identifiable information (PII), to be treated with the utmost care and security, it is important that data owners are clear about their data management practices, especially in this area. It gives you a better understanding of your data, so you can selectively apply higher levels of control and isolation to your most sensitive information. Many larger organizations face the common challenge of siloed data: the organizational intelligence that managers need to make decisions is so dispersed that it can take weeks to gather and analyze it. Data management plays an important role in creating a backup and recovery strategy that matches your company's priorities and data model.


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