Data management is the process of creating and enforcing rules, processes and procedures for handling data throughout its lifecycle. It ensures that data is reliable and accessible, assists in 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 leads to a plethora of data that must be consolidated, and 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, to gain advanced insights.

Without a clearly defined strategy for managing data, businesses can end up with data silos that are incompatible and inconsistent data sets that hinder the ability to run business intelligence and analytics applications. Unorganized data management can reduce the trust of customers and employees.

To address these challenges It is essential that businesses create a data management strategy (DMP) that includes the people and processes needed to manage all kinds of data. For example an DMP can help researchers determine the naming conventions that they should follow to organize data sets for long-term storage and for easy access. It can also include data workflows that specify the steps to take to cleanse, validate, and integrating raw data sets and refined data sets to allow them to be suitable for analysis.

A DMP can be utilized 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 also be used to guide the creation and implementation of procedures and policies that address data security threats.

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