Data management is the process of developing and enforcing rules, processes and procedures to manage data throughout its lifecycle. It ensures that data is reliable and easily 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 which must be consolidated and then delivered to business analytics (BI) systems, enterprise resource management (ERP) platforms as well as the https://taeglichedata.de/how-to-set-up-a-relevant-and-useful-deal-room Internet of Things (IoT), sensors, and machine learning, as well as generative artificial Intelligence (AI) tools, to gain advanced insights.
Without a well-defined data management strategy, companies could end up with uncompatible data silos and inconsistency of data sets that hinder the ability to run business intelligence and analytics applications. Inadequate data management can erode employee and customer trust.
To meet these challenges, companies must develop an effective data-management plan (DMP) which includes the people and processes needed to manage all types data. For example the DMP can assist researchers in determining the naming conventions that they should apply to organize data sets to ensure long-term storage and easy access. It may also include a data workflow that defines the steps for cleansing, testing and integrating raw and refined data sets in order to make them suitable for analysis.
For companies that gather consumer information A DMP can help ensure compliance with global privacy laws like 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 that address data security threats.
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