Data management is a procedure that involves establishing and enforcing procedures, policies and processes to manage data throughout its entire life cycle. It ensures data is accessible and useful, facilitates regulatory compliance and informed decision-making and ultimately provides businesses with an edge in the market.
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 sent to business analytics (BI) systems, enterprise resource management (ERP) platforms as well as the Internet of Things (IoT), sensors, and machine learning, and generative artificial intelligence (AI) tools for advanced insights.
Without a well-defined and standardized data management plan, businesses can end up with incompatible data silos and data sets that are inconsistent which make it difficult to run analytics and business intelligence applications. Poor data management can also cause a loss of confidence in employees and customers.
To meet these challenges, companies must develop a data-management plan (DMP) which includes the people and processes required to manage all types of data. For instance an DMP can assist researchers in determining the file name conventions they should follow to organize data sets to ensure long-term storage and easy access. It can also include data workflows that outline the steps to follow to cleanse, validate and integrating raw data sets as well as refined data sets in order to make them suitable for analyses.
For businesses that collect consumer data for their customers, a DMP can help ensure 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 creation of policies and procedures to address security threats to data and audits.