Data management is a process that involves the creation and enforcement of procedures, policies and processes to manage data throughout its entire life cycle. 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. This creates a flood of data that must be consolidated, and delivered to business analytics (BI) systems such as enterprise resource management (ERP) platforms as well as the Internet of Things (IoT) sensors,, machine learning and generative artificial intelligence (AI) tools for advanced insights.
Without a clearly defined data management strategy, businesses could end up with data silos that are incompatible and inconsistent data sets which hinder the ability to manage business intelligence and analytics applications. A poor data management strategy can cause distrust between customers and employees.
To address these issues companies need to develop a plan for managing data (DMP) which includes the people and processes required to manage all types data. For example the DMP can help researchers identify the naming conventions for files they should follow to organize data sets to ensure long-term storage as well as easy access. It could also include a data workflow that defines the steps for cleansing, verifying and integrating raw and refined https://taeglichedata.de/generated-post/ data sets in order to ensure they are suitable for analysis.
A DMP can be used by companies that collect customer data to ensure compliance with privacy laws at the global and state level, like the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also be used to guide the development and implementation of procedures and policies to address security threats to data.