Data management is the process of establishing and enforcing policies, procedures and procedures to manage data throughout its entire lifecycle. It ensures that data is useful and accessible, which facilitates regulatory compliance and enables 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 results in a plethora of data that needs to be consolidated and sent to business intelligence (BI) and analytics systems and enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors and machine learning and Artificial Intelligence generative (AI) tools to gain advanced insights.
Without a well-defined data management strategy, companies can end up with uncompatible data silos and inconsistent data sets which make it difficult to run business intelligence and analytics applications. Unorganized data management can cause a loss of confidence in employees and customers.
To meet these challenges go to the website businesses must devise a plan for managing data (DMP) that includes the people and processes required to handle all kinds of data. A DMP can, for instance can help researchers decide 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 could also include an data workflow that specifies the steps for cleansing, verifying and integrating raw and refined data sets to allow them to be suitable for analysis.
A DMP can be used by companies that collect consumer data to ensure compliance with privacy laws at the state and global scale, such as 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 which address threats to data security.