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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 accessible and useful, facilitating regulatory compliance and informed decision-making and ultimately provides a competitive advantage for businesses.
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 creates a flood of data that must be consolidated and sent to business analytics (BI) systems as well as enterprise resource management (ERP) platforms and Internet of Things (IoT) sensors,, machine learning, and generative artificial intelligence (AI) tools for advanced insights.
Without a well-defined data management strategy, businesses could end up with uncompatible data silos and data sets that are inconsistent which hinder the ability to run analytics and business intelligence applications. Inadequate data management can cause distrust between customers and employees.
To overcome these challenges it is crucial that companies come up with a data management plan (DMP) that includes the people and processes needed to manage all kinds of data. For instance, a DMP can help researchers identify the file name conventions they should apply to organize data sets for long-term storage and for easy access. It can also include an data workflow that specifies the steps involved in cleansing, verifying and integrating raw and refined data sets to allow them to be suitable for analysis.
A DMP can be utilized by organizations that collect consumer data to ensure compliance with privacy laws at the state and international scale, such as the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can be used to guide the creation and implementation of policies and procedures to address security threats to data.
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