Data management is a process that involves creating and enforcing policies, procedures and processes to handle data throughout its entire lifecycle. It ensures that data is reliable and accessible, which facilitates compliance with regulations and facilitates 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 that must be consolidated and then delivered to business analytics (BI) systems and 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 strategy for managing data, businesses could end up with data silos that are incompatible and inconsistent data sets that hinder the ability to run business intelligence and analytics applications. Inadequate data management can affect the trust of customers and employees.
To address 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. A DMP, for example can assist researchers in determining the conventions for naming files that they should follow to arrange data sets to keep them for a long time and make them easy to access. It may also include an data workflow that specifies the steps for cleansing, checking and integrating raw as well as refined data sets in order to make them 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 level, like the General Data Protection Regulation of the European Union or California's Consumer Privacy Act. It can also guide the creation of policies and procedures to address security threats to data and audits.
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