Data management is the process of developing and enforcing policies, processes and procedures to manage data throughout its lifecycle. It ensures that data is useful and accessible, facilitates regulatory compliance 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 sent to business analytics (BI) systems as well as enterprise resource management (ERP) platforms as well as the Internet of Things (IoT), sensors, and 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 operate business intelligence and analytics applications. Poor data management can also undermine employee and customer confidence.
To meet these challenges It is essential that businesses develop a data management plan (DMP) that includes the processes and people required to manage all kinds of data. For instance an DMP can assist researchers in determining the naming conventions for files they should employ to structure data sets to ensure long-term storage and for 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 utilized by companies that collect consumer data to ensure compliance with privacy laws at the state and international level, for example, the General https://taeglichedata.de/generated-post Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can be used to guide the creation and implementation of procedures and policies which address threats to data security.