Data management is the process of creating and enforcing rules, processes and procedures to manage data throughout its entire lifecycle. It ensures that data is useful and accessible, facilitates regulatory compliance and permits 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. The result is a proliferation of data that must be consolidated and 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 generative artificial intelligence (AI) tools for advanced insights.
Without a clearly defined data management strategy, businesses could end up with uncompatible data silos and inconsistency of data sets that hinder the ability to run analytics and business intelligence applications. Inadequate data management can affect the confidence of employees and customers.
To overcome these challenges, it’s essential that companies come up with a data management plan (DMP) that includes the processes and people required to manage all kinds of data. For instance the DMP will help researchers determine the file naming conventions they should apply to organize data sets to ensure long-term storage and for easy access. It could also include an data workflow that specifies the steps needed for cleansing, validating and integrating raw and refined data sets to make them suitable for analysis.
For companies that gather 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, such as California’s Consumer Privacy Act. It can also be used to guide the creation and implementation of policies and procedures which address threats to data security.
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