https://taeglichedata.de/generated-post-2/ Data management is a method that involves establishing and enforcing processes, policies and procedures to handle data throughout its entire life cycle. 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 creates a flood of data which must be consolidated and sent to business analytics (BI) systems, 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 well-defined data management plan, businesses can end up with incompatible data silos and inconsistent data sets which hinder the ability to run analytics and business intelligence applications. Data management issues can reduce trust between employees and customers.

To meet these challenges businesses must create a plan for managing data (DMP) that includes the processes and people needed to handle all kinds of data. A DMP, for example will help researchers identify the conventions for naming files that they should use to organize data sets to store them long-term and make them easy to access. It could also include a data workflow that defines the steps to cleanse, testing and integrating raw and refined data sets in order to allow them to be suitable for analysis.

A DMP can be used by companies that collect customer data to ensure compliance with privacy laws at the state and global level, such as 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 that address data security threats.