Data management is the process of creating and enforcing policies, processes and procedures for handling data throughout its lifecycle. It ensures that data is useful and accessible, facilitates regulatory compliance, and facilitates 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 needs to be consolidated, and then delivered to business intelligence (BI) and analytics systems such as enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors, machine learning and artificial intelligence (AI) tools to provide advanced insights.
Without a well-defined data management plan, businesses can end up with uncompatible data silos and data sets that are inconsistent that hamper the ability to run analytics and business intelligence applications. A poor find data management strategy can affect the confidence of employees and customers.
To address these challenges it is crucial that companies create a data management strategy (DMP) that includes the people and processes required to manage all types of data. A DMP can, for instance can help researchers decide the appropriate file name conventions they should employ to organize data sets in order to preserve them over time and make them simple to access. It may also include data workflows that outline the steps to take to cleanse, validate, and integrating raw data sets and refined data sets to allow them to be suitable for analysis.
A DMP can be utilized by companies that collect consumer data to ensure compliance with privacy laws at the state and global level, such as the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can also be used to guide the development and implementation of procedures and policies that address data security threats.
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