Data management is the process of establishing and enforcing rules, processes and procedures for handling data throughout its entire lifecycle. It ensures that data is reliable and accessible, facilitates compliance with regulations, and enables 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 results in a growing amount of data that must be consolidated and then delivered to business analytics (BI) systems as well as enterprise resource management (ERP) platforms and Internet of Things (IoT), sensors, and machine learning and generative artificial Intelligence (AI) tools, for advanced insights.
Without a clear data management plan, businesses can end up with incompatible data silos and data sets that are inconsistent which hinder the ability to run analytics and business intelligence applications. Inadequate data management can reduce trust between employees and customers.
To overcome these challenges It is essential that businesses come up with a data management plan (DMP) that includes the processes and people required to manage all kinds of data. For instance, a DMP will help researchers determine the file name conventions they should use to structure data sets for long-term storage and easy access. It can also include data workflows which define the steps to follow for https://taeglichedata.de/maintaining-data-processes-throughout-the-information-lifecycle/ cleansing, validating and integrating raw data sets as well as refined data sets to ensure that they are suitable for analysis.
A DMP can be used by companies that collect customer data to ensure compliance with privacy laws on a global and state scale, 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 to address security threats to data.
