Data management is a method that involves creating and enforcing procedures, policies and processes to manage data throughout its entire life cycle. It ensures that data is useful and accessible, assists in regulatory compliance, and permits informed decision-making.

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 results in a proliferation of data that needs to be consolidated and sent to business analytics (BI) systems and enterprise resource management (ERP) platforms and Internet of Things (IoT), sensors, and machine learning and generative artificial Intelligence (AI) tools, to gain advanced insights.

Without a clear strategy for managing data, businesses could end up with data silos that are incompatible and inconsistent data sets, which hinder the ability to run business intelligence and analytics applications. Poor data management can also affect the trust of customers and employees.

To meet these challenges to meet these challenges, it's crucial that businesses come up with a data management plan (DMP) that includes the people and processes required to manage all types of data. A DMP, for example can assist researchers in determining the file naming conventions that they should use to organize data sets to store them over the long term and make them simple to access. It can also contain an data workflow that specifies the steps needed for cleansing, testing and integrating raw and refined data sets in order 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 global level, like the General Data Protection Regulation of the European Union or California's Consumer Privacy Act. It can also be used to guide the creation and implementation of policies and procedures that address security concerns for data.