Data management is the process of developing and enforcing rules, processes and procedures to manage data throughout its entire lifecycle. It ensures that data is useful and easily accessible, facilitates compliance with regulations, and permits informed decisions.

The importance of effective data management has grown significantly as organizations automate their business processes, leverage software-as-a-service (SaaS) applications and deploy data warehouses, among other initiatives. This creates a flood of data that must be consolidated, and delivered to business analytics (BI) systems, enterprise resource management (ERP) platforms, and the Internet of Things (IoT) sensors,, machine learning, and generative artificial intelligence (AI) tools, for advanced insights.

Without a clear strategy for managing data, businesses can end up with silos that are not compatible and inconsistent data sets, which hinder the ability to run business intelligence and analytics applications. Inadequate data management can reduce the trust of customers and employees.

To meet these challenges it is crucial that companies make a plan for data management (DMP) that includes the processes and people required to manage all kinds of data. For example the DMP can help researchers determine the naming conventions that they should use to structure data sets for long-term storage and easy access. It may also include an data workflow that specifies the steps involved in cleansing, testing and integrating raw and refined data sets to make them suitable for analysis.

A DMP can be used by organizations that collect consumer data to ensure compliance with privacy laws at the state and international scale, such as the General Data Protection Regulation of the European Union or California's Consumer Privacy Act. It also can guide the development of policies and procedures for addressing data security threats and audits.

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