Data management is the process of creating and enforcing rules, processes and procedures for handling data throughout its lifecycle. It ensures that data is accessible and useful, assists in regulatory compliance and informed decision-making, and ultimately provides companies with a competitive advantage.
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 results in a growing amount of data that must be consolidated, and delivered 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, to provide advanced insights.
Without a well-defined 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. A poor data management strategy can reduce trust between employees and customers.
To tackle these issues companies need to develop a data-management strategy (DMP) which includes the people and processes needed to handle all kinds of data. A DMP can, for instance will help researchers identify the naming conventions for files that they should follow to organize data sets to keep them for a long time https://taeglichedata.de/pflege-von-datenprozessen-nach-sitzungssaal/ and make them easy to access. It could also include the data workflow that outlines the steps involved in cleansing, testing and integrating raw and refined data sets to allow them to be suitable for analysis.
For companies that gather consumer data for their customers, a DMP can assist in ensuring compliance with privacy laws around the world like the European Union’s General Data Protection Regulation or state-level regulations such as California’s Consumer Privacy Act. It also can guide the formulation of policies and procedures for dealing with data security risks and audits.
