Data management is the process of developing and enforcing policies, processes and procedures to manage data throughout its lifecycle. It ensures that data is useful and accessible, which facilitates compliance with regulations, and permits 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 proliferation of data that must be consolidated, and delivered to business analytics (BI) systems as well as enterprise resource management (ERP) platforms, and the Internet of Things (IoT) sensors,, machine learning, and generative artificial intelligence (AI) tools, to gain advanced insights.
Without a clear data management plan, businesses can end up with uncompatible data silos and unbalanced data sets that hamper the ability to run business intelligence and analytics applications. Poor data management can also affect the confidence of employees and customers.
To tackle these issues to meet these challenges, it’s crucial that businesses make a plan for data management (DMP) that includes the people and processes required to manage all kinds of data. A DMP can, for instance will help researchers identify the appropriate file name conventions they should utilize to organize data sets to store them over the long term and make them easy to access. It can also include an data workflow that specifies the steps needed for cleansing, validating and integrating raw and refined data sets in order to ensure they are suitable for analysis.
A DMP can be utilized by companies that collect customer data to ensure compliance with privacy laws at the global and state scale, such as the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also help guide the formulation of policies and procedures for addressing data security threats and audits.