Data management is the process of developing and enforcing rules, processes and procedures to manage data throughout its entire lifecycle. It makes sure that data is available and useful, facilitating the compliance of regulators and makes informed decisions 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 find warehouses, among other initiatives. This creates a flood of data which must be consolidated and sent to business analytics (BI) systems, enterprise resource management (ERP) platforms, Internet of Things (IoT), sensors, and machine learning, as well as generative artificial Intelligence (AI) tools, for advanced insights.
Without a clear data management strategy, companies could end up with incompatible data silos and inconsistency of data sets that hinder the ability to run analytics and business intelligence applications. Poor data management can also erode employee and customer trust.
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 required to manage all types of data. A DMP, for example, can help researchers determine the naming conventions for files that they should use to organize data sets to store them over the long term and make them simple to access. It can also contain data workflows which define the steps to be taken for cleansing, validating, and integrating raw data sets as well as refined data sets to make them suitable for analysis.
A DMP can be utilized by organizations that collect consumer data to ensure compliance with privacy laws at the state and global level, for example, the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also help guide the development of procedures and policies to deal with security threats to data and audits.