Data management is a procedure that involves creating and enforcing processes, policies and procedures to manage data throughout its entire lifecycle. It ensures that data is accessible and useful, which facilitates regulatory compliance and informed decision-making and ultimately provides an advantage to 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 results in a proliferation of data that needs to be consolidated and then delivered to business analytics (BI) systems and enterprise resource management (ERP) platforms and Internet of Things (IoT) sensors,, machine learning, as well as generative artificial intelligence (AI) tools, for advanced insights.
Without a well-defined and standardized 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. Inadequate data management can cause a loss of confidence in employees and customers.
To meet these challenges, companies must develop an effective data-management plan (DMP), which includes the people and processes required to manage all types of data. For example the DMP can assist researchers in determining the file naming conventions they should apply to organize data sets for long-term storage and easy access. It can also include data workflows that outline the steps that must be followed to cleanse, validate and integrating raw data sets as well as refined data sets to allow them to be suitable for analysis.
A DMP can be used by companies that collect customer data to ensure compliance with privacy laws on a global and state level, such as the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also help guide the formulation of policies and procedures for dealing with data security risks and audits.