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Data management is the process of developing and enforcing policies, processes and procedures for handling data throughout its entire lifecycle. It ensures that data is useful and accessible, which 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. This creates a flood 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,, machine learning and generative artificial Intelligence (AI) tools, to gain advanced insights.

Without a well-defined and standardized data management plan, businesses can end up with uncompatible data silos and inconsistency of data sets that hinder the ability to https://taeglichedata.de/ run analytics and business intelligence applications. Unorganized data management can reduce the trust of customers and employees.

To overcome these challenges to meet these challenges, it’s crucial that businesses make a plan for data management (DMP) that includes the processes and people required to manage all kinds of data. For example the DMP will help researchers determine the naming conventions that they should employ to structure data sets for long-term storage and easy access. It may also include data workflows which define the steps to be taken to cleanse, validate, and integrating raw data sets and 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 global and state level, for example, the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also aid in the development of policies and procedures for addressing data security threats and audits.