Data management is a process that involves the creation and enforcement of processes, policies and procedures to manage data throughout its entire lifecycle. It ensures that data is useful and easily accessible, facilitates regulatory compliance, and allows for 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 leads to a plethora of data which must be consolidated, and delivered to business analytics (BI) systems as well as enterprise resource management (ERP) platforms as well as the Internet of Things (IoT), sensors, and machine learning and generative artificial Intelligence (AI) tools for advanced insights.
Without a well-defined data management strategy, businesses could end up with incompatible data silos and inconsistent data sets which hinder the ability to run analytics and business intelligence applications. A poor data management strategy can reduce trust between employees and customers.
To address these issues companies need to develop a data-management strategy (DMP), which includes the people and processes needed to manage all types of data. For instance an DMP can help researchers determine the naming conventions for files they should apply to organize data sets for long-term storage as well as easy access. It may also include data workflows which define the steps to be taken to cleanse, validate and integrating raw data sets as well as refined data sets in order to ensure that they are suitable for analysis.
A DMP can be utilized by companies that collect consumer data to ensure compliance with privacy laws at the global and state level, for example, the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also aid in the creation of policies and procedures for dealing with data security risks and audits.