Data management is a process that involves establishing and enforcing policies, procedures and processes to handle data throughout its entire lifecycle. It ensures that data is useful and maintaining data processes the information lifecycle accessible, facilitates compliance with regulations, and enables 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 leads to a plethora of data that needs to be consolidated and then delivered to business analytics (BI) systems, 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 clearly defined data management plan, businesses can end up with uncompatible data silos and unbalanced data sets which hinder the ability to run business intelligence and analytics applications. Poor data management can also affect the confidence of employees and customers.
To tackle these issues businesses must create an effective data-management plan (DMP), which includes the processes and people needed to handle all kinds of data. For example, a DMP can help researchers determine the naming conventions that they should use to structure data sets to ensure long-term storage and easy access. It can also include data workflows that outline the steps to follow for cleansing, validating and integrating raw data sets as well as refined data sets in order to make them suitable for analysis.
For companies that collect consumer data A DMP can assist in ensuring compliance with global privacy laws such as the European Union’s General Data Protection Regulation or state-level regulations such as California’s Consumer Privacy Act. It can also guide the formulation of policies and procedures for dealing with data security risks and audits.