Data management is a procedure that involves establishing and enforcing policies, procedures and processes to manage data throughout its entire lifecycle. It ensures that data is useful and accessible, facilitates compliance with regulations and facilitates 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. The result is a growing amount of data that must be consolidated and delivered to business intelligence (BI) and analytics systems and enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors, machine learning and generative artificial intelligence (AI) tools to gain advanced insights.
Without a clear data management strategy, businesses may end up with data silos that are incompatible and inconsistent, which hinder the ability to run business intelligence and analytics applications. Data management issues can cause distrust between customers and employees.
To meet these challenges It is essential that businesses develop a data management plan (DMP) that includes the people and processes required to manage all kinds of data. For instance the DMP will help researchers determine the file name conventions they should follow to organize data sets to ensure long-term storage and access. It may also include data workflows that define the steps that must be followed for cleansing, validating, and integrating raw data sets as well as refined data sets in order to make them suitable for analysis.
A DMP can be utilized by companies that collect customer data to ensure compliance with privacy laws at the state and international level, such as the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also help guide the formulation of policies and procedures to address security threats to data and audits.
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