The Importance of Data Management

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, 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 creates a flood of data that needs to be consolidated and then delivered to business analytics (BI) systems, enterprise resource management (ERP) platforms, and the Internet of Things (IoT) sensors,, 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 incompatible data silos and data sets that are inconsistent which make it difficult to run analytics and business intelligence applications. Unorganized data management can affect the confidence of employees and customers.

To address these challenges it is crucial that companies make a plan for data management (DMP) that includes the processes and people required to manage all kinds of data. For example the DMP can help researchers identify the file naming conventions they should apply to organize data sets for long-term storage and access. It can also contain an data workflow that specifies the steps involved in cleansing, testing and integrating raw and refined data sets in order to make them suitable for analysis.

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

Lifecycle of IMLS Software