Data management is the process of creating and enforcing rules, processes and procedures for handling data throughout its entire lifecycle. It ensures that data is useful and accessible, assists in compliance with regulations, 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. This creates a flood of data that needs to be consolidated, and then sent to business analytics (BI) systems such as enterprise resource management (ERP) platforms as well as the Internet of Things (IoT) sensors,, machine learning, and generative artificial intelligence (AI) tools, to provide advanced insights.
Without a well-defined and standardized data management strategy, companies could end up with uncompatible data silos look at more info and unbalanced data sets which make it difficult to run analytics and business intelligence applications. Poor data management can also reduce trust between employees and customers.
To tackle these issues, it’s essential that companies develop a data management plan (DMP) that includes the processes and people required to manage all kinds of data. For instance an DMP will help researchers determine the naming conventions for files they should follow to organize data sets to ensure long-term storage and for easy access. It can also contain the data workflow that outlines the steps involved in cleansing, verifying and integrating raw and refined data sets to make them suitable for analysis.
A DMP can be used by companies that collect consumer data to ensure compliance with privacy laws at the state and global level, such as the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It also can guide the development of procedures and policies for dealing with data security risks and audits.