Data management is the process of developing and enforcing rules, processes and procedures for handling data throughout its lifecycle. It ensures that data is useful and accessible, 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 creates a flood of data that needs to be consolidated, and then sent to business analytics (BI) systems as well as enterprise resource management (ERP) platforms, Internet of Things (IoT) sensors,, machine learning and generative artificial intelligence (AI) tools, to provide advanced insights.

Without a clear data management strategy, companies could end up with uncompatible data silos and data sets that are inconsistent that hamper the ability to run analytics and business intelligence applications. Poor data management can also reduce the trust of customers and employees.

To meet these challenges to meet these challenges, it’s crucial that businesses develop a data management plan (DMP) that includes the necessary people and processes to manage all types of data. A DMP, for example can assist researchers in determining the file naming conventions that they should utilize to arrange data sets to store them over the long term and make them simple to access. It could also include the data workflow that outlines the steps involved in cleansing, validating and integrating raw and refined data sets in order to allow them to be suitable for analysis.

For organizations that collect consumer data, a DMP can assist in ensuring 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 be used to guide the creation and implementation of policies and procedures to address security threats to data.

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