Data management is a method which involves the creation and implementation of procedures, policies and procedures to handle data throughout its entire lifecycle. It ensures that data is useful and accessible, assists in regulatory compliance, and permits 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 a growing amount of data which must be consolidated and sent to business analytics (BI) systems as well as enterprise resource management (ERP) platforms, Internet of Things (IoT) sensors,, machine learning, and generative artificial intelligence (AI) tools, to provide advanced insights.
Without a clearly defined data management plan, businesses can end up with uncompatible data silos and unbalanced data sets which hinder the ability to run analytics and business intelligence applications. Data management issues can reduce trust between employees and customers.
To tackle these issues companies need to develop an effective data-management plan (DMP) that includes the processes and people needed to manage all types data. For instance, a DMP will help researchers determine the file naming conventions they should apply to organize data sets for long-term storage and easy access. It can also contain data workflows which define the steps to follow for cleansing, validating, and integrating raw data sets and refined data sets in order to allow them to be suitable for analysis.
A DMP can be utilized by organizations that collect consumer data to ensure compliance with privacy laws at the global and state level, for example, the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can be used to guide the creation and implementation of policies and procedures that address data security threats.
