Data management is a method that involves the creation and enforcement of policies, procedures and processes to handle data throughout its entire lifecycle. It ensures that data is reliable and accessible, which facilitates compliance with regulations, and allows for 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 must be consolidated, and then delivered to business intelligence (BI) and analytics systems as well as enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors, machine learning and generative artificial intelligence (AI) tools to gain advanced insights.
Without a clearly defined data management strategy, companies can end up with silos that are not compatible and inconsistent, which hinder the ability to run business intelligence and analytics applications. Poor data management can also reduce trust between employees and customers.
To tackle these issues businesses must devise a data-management strategy (DMP) which includes the people and processes required to manage all types data. A DMP can, for instance, can help researchers determine the file naming conventions that they should utilize to organize data sets to keep them for a long time and make them simple to access. It could also include data workflows which define the steps to take to cleanse, validate, and integrating raw data sets as well as refined data sets to allow them to be suitable for analysis.
For companies that gather 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 like California’s Consumer Privacy Act. It can be used to guide the development and implementation of policies and procedures which address threats to data security.