Data management is a method that involves establishing and enforcing processes, policies and procedures to handle 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 growing amount of data which must be consolidated, and then sent to business analytics (BI) systems, enterprise resource management (ERP) platforms and Internet of Things (IoT), sensors, and machine learning, as well as generative artificial Intelligence (AI) tools, for advanced insights.

Without a clearly defined data management strategy, companies could end up with incompatible data silos and data sets that are inconsistent which hinder the ability to run business intelligence and analytics applications. Unorganized data management can affect the confidence of employees and customers.

To maintaining data processes the information lifecycle address these issues businesses must create a data-management plan (DMP) that includes the processes and people needed to manage all types of data. A DMP, for example, can help researchers determine the file naming conventions that they should utilize to arrange data sets to keep them for a long time and make them simple to access. It can also include the data workflow that outlines the steps to cleanse, testing and integrating raw and refined data sets in order to allow them to be suitable for analysis.

A DMP can be used by organizations that collect consumer data to ensure compliance with privacy laws on a global and state 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 data security threats.