Data management is the process of developing and enforcing rules, processes and procedures to manage data throughout its lifecycle. It ensures that data is accessible and useful, assists in the compliance of regulators and makes informed decisions and ultimately gives a competitive advantage for businesses.
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 creates a flood of data that must be consolidated, and delivered to business analytics (BI) systems, enterprise resource management (ERP) platforms as well as the Internet of Things (IoT), sensors, and machine learning, and generative artificial Intelligence (AI) tools, for advanced insights.
Without a clearly defined strategy for managing data, businesses can end up with data silos that go to the website are not compatible and inconsistent data sets, which hinder the ability to operate business intelligence and analytics applications. Inadequate data management can undermine employee and customer confidence.
To meet these challenges, companies must develop a data-management plan (DMP), which includes the processes and people needed to handle all kinds of data. A DMP can, for instance will help researchers identify the file naming conventions that they should use to organize data sets in order to preserve them over time and make them simple to access. It can also include data workflows that outline the steps to follow for cleansing, validating and integrating raw data sets and refined data sets to make them suitable for analyses.
A DMP can be used by companies that collect customer data to ensure compliance with privacy laws at the state and international scale, such as 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 policies and procedures that address security concerns for data.
