Data management is the process of establishing and enforcing policies, processes and procedures for handling data throughout its lifecycle. It ensures that data is accessible and useful, which facilitates regulatory compliance and informed decision-making and ultimately provides an advantage to 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. The result is a growing amount 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, machine learning and generative artificial intelligence (AI) tools to gain advanced insights.
Without a well-defined and standardized data management strategy, companies can end up with incompatible data silos and inconsistency of data sets which hinder the ability to run business intelligence and analytics applications. Data management issues can reduce trust between employees and customers.
To address these challenges it is crucial that companies make a plan for data management (DMP) that includes the processes and people required to manage all types of data. For instance, a DMP can help researchers identify the file naming conventions they should follow to organize data sets for long-term storage and access. It could also include data workflows that define the steps to be taken to cleanse, validate, and integrating raw data sets as well as refined data sets in order to make them suitable for analyses.
For companies that gather consumer information For companies that collect consumer information, 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 like California’s Consumer Privacy Act. It can be used to guide the development and implementation of policies and procedures which address threats to data security.
https://taeglichedata.de/maintaining-data-processes-throughout-the-information-lifecycle/
