Data management is a method that involves creating and enforcing procedures, policies and procedures to manage data throughout its entire lifecycle. It ensures that data is reliable and easily accessible, facilitates compliance with regulations and permits 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. The result is a growing amount of data that needs to be consolidated, and then delivered to business intelligence (BI) and analytics systems as well as enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors, machine learning and Artificial Intelligence generative (AI) tools for advanced insights.
Without a clearly defined data management strategy, companies can end up with data silos that are incompatible and inconsistent, which hinder the ability to operate business intelligence and analytics applications. Poor data management can also erode employee and customer trust.
To tackle these issues, companies must develop a plan for managing data (DMP), which includes the processes and people needed to manage all types of data. For instance, a DMP can assist researchers in determining the naming conventions for files they should employ to structure data sets to ensure long-term storage as well as easy access. It can also include an data workflow that specifies the steps for cleansing, validating and integrating raw and refined data sets to allow them to be suitable for analysis.
A DMP can be used by organizations that collect consumer data to ensure compliance with privacy laws at the global and state level, such as the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It also can guide the development of policies and procedures for addressing data security threats and audits.