Data management is the process of establishing and enforcing rules, processes and procedures for handling data throughout its lifecycle. It ensures that data is useful and accessible, which facilitates regulatory compliance 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. The result is a flood of data that needs to be consolidated, and then delivered to business intelligence (BI) and analytics systems such as enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors, machine learning and Artificial Intelligence generative (AI) tools for advanced insights.
Without a well-defined data management plan, businesses can end up with uncompatible data silos and inconsistency of data sets which hinder the ability to run business intelligence and analytics applications. Poor data management can also undermine employee and customer confidence.
To address these https://taeglichedata.de/verwalten-von-datenprozessen-mit-data-center-management-anwendungen issues companies need to develop an effective data-management plan (DMP) which includes the people and processes needed to manage all types data. For example the DMP will help researchers determine the naming conventions that they should follow to organize data sets for long-term storage and easy access. It can also include an data workflow that specifies the steps involved in cleansing, testing and integrating raw and refined data sets to make them suitable for analysis.
For organizations that collect consumer data for their customers, a DMP can assist in ensuring 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 help guide the development of procedures and policies for dealing with data security risks and audits.