Data management is the process of developing and enforcing policies, procedures and procedures to manage data throughout its lifecycle. It ensures that data is accessible and useful, which facilitates the compliance of regulators and makes informed decisions and ultimately provides businesses with an edge in the market.

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 which must be consolidated, and delivered to business analytics (BI) systems, enterprise resource management (ERP) platforms, and the Internet of Things (IoT), sensors, and machine learning, as well as generative artificial intelligence (AI) tools, to provide advanced insights.

Without a clearly defined data management strategy, businesses may end up with data silos that are incompatible and inconsistent data sets, which hinder the ability to run business intelligence and analytics applications. A poor data management strategy can affect the trust of customers and employees.

To address these issues companies need to develop a data-management strategy (DMP) that includes the processes and people needed to manage all types of data. A DMP, for example can assist researchers in determining the conventions for naming files that they should employ to organize data sets in order to keep them for a long time and make them simple to access. It can also contain data workflows that define the steps to follow to cleanse, validate, and integrating raw data sets as well as refined data sets to make them suitable for analyses.

A DMP can be utilized by companies that collect customer data to ensure compliance with privacy laws at the state and international level, for example, the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It also can guide the development of policies and procedures for dealing with data security risks and audits.

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