Data management is a procedure which involves the creation and implementation of procedures, policies and processes to manage data throughout its entire life cycle. It ensures data is accessible and useful, facilitating regulatory compliance and informed decision-making and ultimately creates 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. The result is a proliferation of data that must 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 clearly defined data management strategy, businesses could end up with uncompatible data silos and inconsistency of data sets which hinder the ability to run business intelligence and analytics applications. Data management issues can cause distrust between customers and employees.
To overcome these challenges, it’s essential that companies develop a data management plan (DMP) that includes the processes and people required to manage all types of data. A DMP can, for instance can assist researchers in determining the naming conventions for files that they should follow to organize data sets in order to keep them for a long time and make them simple to access. It can also include data workflows that outline the steps to follow for cleansing, validating and integrating raw data sets and refined data sets to ensure that they are suitable for analysis.
For companies that collect consumer data for their customers, 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 creation and implementation of policies and procedures that address security concerns for data.
https://taeglichedata.de/maintaining-data-processes-throughout-the-information-lifecycle