Data management is the process of creating and enforcing policies, procedures and procedures to manage data throughout its entire lifecycle. It ensures that data is reliable 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 needs to be consolidated, and then delivered to business intelligence (BI) and analytics systems such as enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors, machine learning and Artificial Intelligence generative (AI) tools to provide advanced insights.
Without a clear data management plan, businesses could end up with data silos that are not compatible and inconsistent data sets, which hinder the ability to run analytics and business intelligence applications. Poor data management can also reduce trust between employees and customers.
To address these challenges, it’s essential that companies create a data management strategy (DMP) that includes the necessary people and processes to manage Surveys on IT Lifecycle all kinds of data. For instance the DMP will help researchers determine the naming conventions that they should follow to organize data sets for long-term storage as well as easy access. It can also contain data workflows that specify the steps to follow to cleanse, validate, and integrating raw data sets as well as refined data sets to make them suitable for analysis.
A DMP can be utilized by companies that collect consumer data to ensure compliance with privacy laws at the state and global level, like the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It also can guide the development of policies and procedures for dealing with data security risks and audits.