Data management is the process of establishing and enforcing policies, processes and procedures for handling data throughout its lifecycle. It makes sure that data is available and useful, assists in the compliance of regulators and makes informed decisions, and ultimately provides an advantage to businesses.
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 then sent to business analytics (BI) systems, enterprise resource management (ERP) platforms, Internet of Things (IoT), sensors, and machine learning and generative artificial intelligence (AI) tools, for advanced insights.
Without a clear data management plan, businesses could end up with data silos that are incompatible and inconsistent, which hinder the ability to manage business intelligence and analytics applications. Poor data management can also affect the confidence of employees and customers.
To tackle these issues businesses must devise a data-management plan (DMP) which includes the processes and people needed to manage all types of data. A DMP, for example can assist researchers in determining the appropriate file name conventions they should follow to arrange data sets to store them over the long term and make them easy to access. It could also include an information workflow that outlines the steps for cleansing, testing and integrating raw and refined data sets to ensure they are suitable for analysis.
A DMP can be used by companies that collect consumer data to ensure compliance with privacy laws on a global and state level, such as the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can be used to guide the development and implementation of policies and procedures which address threats to data security.