Data management is the process of developing and enforcing policies, processes and procedures for handling data throughout its lifecycle. It ensures that data is useful and accessible, which facilitates compliance with regulations, and enables 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 results in a plethora of data that needs to be consolidated, and then delivered to business intelligence (BI) and analytics systems, enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors, machine learning and Artificial Intelligence generative (AI) tools to gain advanced insights.

Without a clearly defined data management strategy, businesses could end up with incompatible data silos and data sets that are inconsistent which hinder the ability to run analytics and business intelligence applications. Poor data management can also cause a loss of confidence in employees and customers.

To tackle these issues It is essential that businesses come up with a data management plan (DMP) that includes the processes and people required to manage all kinds of data. For instance, a DMP can help researchers identify the file name conventions they should apply to organize data sets for long-term storage and easy access. It can also include data workflows that define the steps to follow to cleanse, validate, and integrating raw data sets and refined data sets to make them suitable for analyses.

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

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