Data management is a process that involves establishing and enforcing procedures, policies and processes to manage data throughout its entire life cycle. It ensures that data is easily accessible and useful, facilitating 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 creates a flood of data that needs to be consolidated and sent to business analytics (BI) systems, enterprise resource management (ERP) platforms, Internet of Things (IoT), sensors, and machine learning, and generative artificial Intelligence (AI) tools, for advanced insights.
Without a clear data management plan, businesses can end up with uncompatible data silos and inconsistent data sets that hamper the ability to run analytics and business intelligence applications. Poor data management can also cause distrust between customers and employees.
To tackle these issues businesses must devise a data-management plan (DMP) which includes the processes and people needed to manage all types data. A DMP, for example can assist researchers in determining the naming conventions for files that they should employ to organize data sets in order to store them long-term and make them simple to access. It could also include data workflows which define the steps to follow for cleansing, validating and integrating raw data sets as well as refined https://taeglichedata.de/verwalten-von-datenprozessen-mit-data-center-management-anwendungen data sets to ensure that they are suitable for analysis.
For organizations that collect consumer data, a DMP can help ensure compliance with global privacy laws such as the European Union’s General Data Protection Regulation or state-level regulations such as California’s Consumer Privacy Act. It can be used to guide the development and implementation of procedures and policies which address threats to data security.