Data management is a method that involves establishing and enforcing procedures, policies and procedures to handle data throughout its entire lifecycle. It ensures that data is reliable and accessible, which facilitates regulatory compliance and permits 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. The result is a flood of data that needs to be consolidated and sent to business intelligence (BI) and analytics systems and enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors, machine learning and artificial intelligence (AI) tools to provide advanced insights.
Without a clearly defined data management plan, businesses can end up with data silos that are not compatible and inconsistent, which hinder the ability to manage business intelligence and analytics applications. Inadequate data management can erode employee and customer trust.
To meet these challenges to meet these challenges, it’s crucial that businesses create a data management strategy (DMP) that includes the necessary people and processes to manage all types of data. For example, a DMP can assist researchers in determining the file name conventions they should apply to organize data sets for long-term storage as well as easy access. It may also include data workflows that specify the steps to be taken for cleansing, validating and integrating raw data sets and refined data sets to make them suitable for analyses.
For companies that collect consumer information for their customers, a DMP can assist in ensuring compliance with privacy laws around the world such as the European Union’s General Data Protection Regulation or state-level regulations, such as California’s Consumer Privacy Act. It can be used to guide the development websites and implementation of policies and procedures that address security concerns for data.