Data management is the process of establishing and enforcing processes, policies and procedures to manage data throughout its lifecycle. It ensures that data is useful and easily accessible, facilitates regulatory compliance and facilitates 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 results in a proliferation of data that needs to be consolidated, and delivered to business analytics (BI) systems as well as enterprise resource management (ERP) platforms as well as the Internet of Things (IoT) sensors,, machine learning, as well as generative artificial Intelligence (AI) tools, for advanced insights.
Without a clearly defined data management plan, businesses can end up with uncompatible data silos and data sets that are inconsistent that hinder the ability to run analytics and business intelligence applications. Data management issues can reduce trust between employees and customers.
To meet these challenges it is crucial that companies create a data management strategy (DMP) that includes the people and processes needed to manage all kinds of data. A DMP, for example can help researchers decide the conventions for naming files that they should employ to organize data sets to keep them for a long time and make them simple to access. It can also include data workflows which define the steps to follow to cleanse, validate, and integrating raw data sets and refined data sets to allow them to be suitable for analysis.
For companies that gather consumer information, a DMP can help ensure compliance with privacy laws around the world like the European Union’s General Data Protection Regulation or state-level regulations such as California’s Consumer Privacy Act. It can also guide the development of procedures and policies for https://www.taeglichedata.de/master-data-management-the-first-steps-in-data-consolidation dealing with data security risks and audits.
