Data management is a procedure that involves the creation and enforcement of policies, procedures and processes to manage data throughout its entire 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. This results in over at this website a growing amount of data that needs to be consolidated, and then sent to business analytics (BI) systems, enterprise resource management (ERP) platforms as well as the Internet of Things (IoT), sensors, and machine learning, as well as generative artificial intelligence (AI) tools for advanced insights.
Without a clearly defined strategy for managing data, businesses can end up with data silos that are not compatible and inconsistent data sets, which hinder the ability to run business intelligence and analytics applications. Data management issues can cause distrust between customers and employees.
To meet these challenges, companies must develop a data-management plan (DMP) which includes the processes and people needed to manage all types of data. For instance an DMP will help researchers determine the file name conventions they should use to structure data sets to ensure long-term storage and for easy access. It may also include data workflows that outline the steps to take for cleansing, validating and integrating raw data sets and refined data sets in order to ensure that they are suitable for analysis.
For companies that collect consumer information, a DMP can help ensure compliance with global privacy laws like 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 creation and implementation of procedures and policies that address data security threats.