Data management is a method that involves the creation and enforcement of procedures, policies and processes to handle data throughout its entire lifecycle. It ensures that data is useful and accessible, facilitates compliance with regulations and facilitates https://taeglichedata.de/generated-post 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. The result is a growing amount of data that needs to be consolidated and pushed to business intelligence (BI) and analytics systems as well as enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors and machine learning and generative artificial intelligence (AI) tools for advanced insights.
Without a well-defined data management strategy, companies could end up with uncompatible data silos and inconsistent data sets that hinder the ability to run analytics and business intelligence applications. A poor data management strategy can cause distrust between customers and employees.
To tackle these issues companies need to develop a plan for managing data (DMP), which includes the processes and people needed to manage all types data. A DMP, for example will help researchers identify the appropriate file name conventions they should use to organize data sets to store them long-term and make them simple to access. It may also include the data workflow that outlines the steps for cleansing, verifying and integrating raw and refined data sets to make them suitable for analysis.
A DMP can be used 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 be used to guide the development and implementation of policies and procedures that address data security threats.