Data management is a process that involves creating and enforcing policies, procedures and processes to handle data throughout its entire life cycle. It ensures that data is useful and accessible, which facilitates compliance with regulations, and permits 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 results in a plethora of data that needs to be consolidated and sent to business intelligence (BI) and analytics systems, enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors and machine learning and artificial intelligence (AI) tools for advanced insights.
Without a clearly defined data management strategy, businesses could end up with incompatible data silos and inconsistent data sets that hinder the ability to run business intelligence and analytics applications. Poor data management can also undermine employee and customer confidence.
To meet these challenges businesses must devise an effective data-management plan (DMP), which includes the people and processes required to manage all kinds of data. A DMP can, for instance will help researchers identify the appropriate file name conventions they should utilize to organize data sets to preserve them over time and make them simple to access. It may also include the data workflow that outlines the steps needed for cleansing, verifying and integrating raw and refined data sets in order to ensure they are suitable for analysis.
A DMP can be utilized 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 be used to guide the development and implementation of procedures and policies that address data security threats.