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Data management is a method that involves establishing and enforcing procedures, policies and procedures to handle data throughout its entire life cycle. It ensures that data is reliable and accessible, which facilitates compliance with regulations, and enables 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 delivered to business analytics (BI) systems and enterprise resource management (ERP) platforms, and the 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 plan, businesses can end up with silos that are incompatible and inconsistent, which hinder the ability to manage business intelligence and analytics applications. Inadequate data management can cause a loss of confidence in employees and customers.

To tackle these issues businesses must devise a plan for managing data (DMP) that includes the people and processes required to manage all types of data. For example an DMP will help researchers determine the file naming conventions they should apply to organize data sets to ensure long-term storage as well as easy access. It can also contain an information workflow that outlines the steps to cleanse, testing and integrating raw and refined data sets to make them suitable for analysis.

For companies that gather consumer data For companies that collect consumer information, a DMP can assist in ensuring compliance with privacy laws around the world like the European Union’s General Data Protection Regulation or state-level laws like California’s Consumer Privacy Act. It can be used to guide the creation and implementation of procedures and policies to address security threats to data.