At a glance

An IoT without data would be unthinkable: It is the combination of IOT data such as measured values, real-time information from the environment or from the Internet and Big Data that makes many complex applications possible. Good data management is required in order to effectively manage the volumes of data generated in this way. Data management or data administration creates a framework to store and utilize the emerging data in such a way that it is perfectly accessible for the respective application.

Advantages of Data Management

Well-designed data management strategies offer many benefits to businesses and researchers, including:

• fast retrieval of specific information
• simple and efficient management of large amounts of data
• effective protection of data and critical information
• Maintaining data integrity
• optimal preparation for later use
• efficient use of resources such as computing power or memory

Types of Data Management

There are different ways to manage data. Which methods are used in which combination depends on the type of data, the goals and specifications in the company, and the intended use.

Big Data Management
This methodology deals with big data, i.e. the collection, analysis, exploitation and archiving of enormous amounts of data. It is primarily a matter of capturing and storing raw data in such a way that their integrity and thus their usability for subsequent analyses is preserved. In the course of storage, it is also a priority that the data is stored and processed in such a way that it forms a solid basis for the planned business processes and analyses.

Data Warehousing
Huge data floods require corresponding structures to be stored reliably and over the long term. A data warehouse can be imagined as a highly optimized data warehouse. It offers the necessary infrastructure (e.g. servers, database technologies) to handle raw data efficiently in all respects.

Data Governance
Data governance is a set of rules that precisely describes how data is stored and used in an enterprise or organization. The goal is to keep the security and quality of stored data as high as possible. In addition, data governance is important when there are legal requirements for handling the collected data: The fixed rules make it easier to comply with the guidelines.

Quality Management
The quality of the available data is decisive for the quality of the applications and analyses based on it. It is important to examine the data for conflicts (e.g. duplicates, problematic version differences) and to eliminate these as far as possible or avoid their occurrence as far as possible.

Data Security
The security of the stored data is a central aspect that is relevant for almost every application. For example, it is important to protect the data from unauthorized access or from unplanned deletion or movement. The encryption of data and databases also plays a role in this area.

Master Data Management
Master data is data that is required by several or all areas in a company or in an application. Accordingly, it is maintained in a central location.