Data management is the process of creating and enforcing policies, processes and procedures for handling data throughout its lifecycle. It ensures that data is useful and accessible, assists in regulatory compliance and enables informed decision-making.
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 proliferation of data that must be consolidated and pushed to business intelligence (BI) and analytics systems, enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors and machine learning and Artificial Intelligence generative (AI) tools for advanced insights.
Without a clear data management plan, businesses can end up with data silos that are not compatible and inconsistent data sets that hinder the ability to run business intelligence and analytics applications. Inadequate data management can reduce trust between employees and customers.
To address these issues businesses must devise a data-management plan (DMP) which includes the people and processes needed to manage all kinds of data. A DMP, for example, can help researchers determine the conventions for naming files that they should find employ to organize data sets in order to store them over the long term and make them easy to access. It may also include an data workflow that specifies the steps for cleansing, testing and integrating raw and refined data sets in order to allow them to be suitable for analysis.
A DMP can be used by companies that collect customer data to ensure compliance with privacy laws at the global and state level, like the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also be used to guide the development and implementation of procedures and policies that address data security threats.