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Data management is a procedure that involves establishing and enforcing procedures, policies and procedures to manage data throughout its entire life cycle. It ensures that data is reliable and easily accessible, facilitates compliance with regulations, and allows for 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 leads to a plethora of data which must be consolidated, and then sent to business analytics (BI) systems, enterprise resource management (ERP) platforms, Internet of Things (IoT), sensors, and machine learning and generative artificial Intelligence (AI) tools, for advanced insights.
Without a clearly defined data management plan, businesses could end up with data silos that are incompatible and inconsistent data sets that hinder the ability to manage business intelligence and analytics applications. Poor data management can also undermine employee and customer confidence.
To meet these challenges businesses must create a data-management strategy (DMP), which includes the people and processes required to manage all kinds of data. A DMP, for example can assist researchers in determining the naming conventions for files that they should follow to organize data sets to keep them for a long time and make them simple to access. It could also include data workflows that define the steps to take for cleansing, validating and integrating raw data sets as well as refined data sets to make them suitable for analyses.
For businesses that collect consumer information A DMP can help ensure compliance with global privacy laws such as the European Union’s General Data Protection Regulation or state-level regulations like California’s Consumer Privacy Act. It can also aid in the creation of policies and procedures for dealing with data security risks and audits.