Data management is the process of establishing and enforcing policies, processes and procedures for handling data throughout its entire lifecycle. It ensures data is accessible and useful, which facilitates the compliance of regulators and makes informed decisions and ultimately gives companies with a competitive advantage.
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 proliferation of data that must be consolidated, and then delivered to business intelligence (BI) and analytics systems as well as enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors and machine learning and Artificial Intelligence generative (AI) tools to provide advanced insights.
Without a clearly defined data management plan, businesses can end up with incompatible data silos and data sets that are inconsistent that hamper the ability to run business intelligence and analytics applications. Unorganized data management can affect the confidence of employees and customers.
To tackle these issues to meet these challenges, it’s crucial that businesses develop a data management plan (DMP) that includes the people and processes needed to find manage all kinds of data. For instance, a DMP can help researchers determine the naming conventions for files they should apply to organize data sets to ensure long-term storage as well as easy access. It can also contain an information workflow that outlines the steps involved in cleansing, validating and integrating raw and refined data sets in order to ensure they are suitable for analysis.
A DMP can be utilized by organizations that collect consumer data to ensure compliance with privacy laws at the global and state scale, such as the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also aid in the development of procedures and policies for dealing with data security risks and audits.