Data management is the process of developing and enforcing policies, procedures and procedures for handling data throughout its lifecycle. It ensures that data is reliable and accessible, which facilitates regulatory compliance and enables 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. This results in a growing amount of data that must be consolidated and sent to business analytics (BI) systems such as enterprise resource management (ERP) platforms, and the Internet of Things (IoT) sensors,, machine learning, as well as generative artificial intelligence (AI) tools, for advanced insights.
Without a clear data management strategy, companies could end up with uncompatible data silos and inconsistency of data sets that hinder the ability to run business intelligence and analytics applications. Inadequate data management can reduce the trust of customers and employees.
To overcome these challenges, it’s essential that companies create a data management strategy (DMP) that includes the people and processes needed to manage all types of data. For instance an DMP will help researchers determine the naming conventions for files they should apply to organize data sets for long-term storage as well as easy access. It can also contain an data workflow that specifies the steps for cleansing, checking and integrating raw as well as refined data sets to ensure they are suitable for analysis.
For businesses that collect consumer data for their customers, a DMP can help ensure compliance with global privacy laws like the European Union’s General Data Protection Regulation or state-level regulations like California’s Consumer Privacy Act. It can also help guide the formulation of policies and procedures for dealing with data security risks and audits.