#4 Data Management

The term Data Management is a broad term that encompasses all technical, conceptual, and organizational methods and processes for the collection, storage, and provision of data. These define enterprise-wide measures for data quality, data privacy, data governance, data compliance, and data lifecycle management. For consistent and enterprise-wide Data Management, all areas, individuals, and departments of the enterprise must be considered. The established measures define the basis for successful Data Management. Companies view their data as valuable and indispensable resources. Because data is an important component of maintained business purposes and is exchanged with the outside world, companies also must maintain third-party data. Although Data Management initially has a difficult-to-measure benefit, it is a very important factor for the profitability of a company in the background. To take advantage of this factor, goals must first be set for Data Management to be able to measure the benefit.

Tasks of the Data Management

To measure the success and usage of Data Management, the general term is divided into four smaller areas of responsibility, so that in these sub-areas, goals, application areas, and necessary tools can be listed and evaluated.

Data Architecture

The area of responsibility for Data Architecture forms the basis of enterprise-wide Data Management. The goal here is to plan so that current and future data related to business processes can be mapped, stored, and utilized in the digital world. The fundamental basis is also of great importance in the areas of corporate compliance and data protection. If a company's data architecture is designed correctly from the ground up, new regulations and guidelines such as the GDPR in 2015 can be applied without major difficulties and resources. In addition, the data architecture should be structured in such a way that newer technologies and trends can be integrated into the mapping of business processes. These include the application of Business Intelligence, Data Science, and Machine Learning tools. All three of these areas require cleanliness, quality, and quantity of data. An enterprise-wide, uniform, and forward-thinking data architecture can take advantage of this opportunity to achieve a successful data-driven business.

Data Administration

The tasks of data administration include ensuring that the business processes and associated data abstracted into the digital world are stored and used uniformly and according to the established requirements. This primarily involves master data management, which includes critical data for the company. For this reason, data administration is the company-wide executive authority for data protection and data security concepts. These concepts protect data from unauthorized access and technical loss both internally and externally. Regular internal checks are carried out to ensure compliance with national or international regulations and standards related to data protection and data security.

Data Technology

Today, data technology can also be referred to as database administration. The tasks here are to provide the required and requested data for the execution of business processes. For example, it is the provision of master, customer, or inventory data to employees with the authorization to view this data. It should be noted that database administration should be based on the principles of data architecture and administration, as otherwise, compliance issues and data loss may arise. Additionally, it is also crucial that employees can quickly query the required data, as otherwise, the completion of business processes cannot be accomplished.

Data Usage

The stored and provided data are sometimes needed by all employees. However, a special group of specialists requires more or different data for certain tasks. This group includes data scientists, data analysts and business intelligence specialists. The task here is to provide specific data to certain groups so that new and important insights can be generated from existing data. This type of provision is not equivalent to providing data for everyday business transactions. This is because members of this special group may not have specific ideas about the requested data. Therefore, it may be necessary for this group to explore the data stores freely to find potential use cases for machine learning applications, for example. For this reason, the data architecture, data administration, and data technology must be prepared for this exploratory data access. This type of knowledge generation is a very dynamic process, which therefore requires dynamic and flexible data access.

Big Data Management

Effective data management is crucial for any organization that wants to make informed decisions and remain competitive in today's data-driven world. With the exponential growth of data, it has become increasingly important for businesses to adopt efficient and effective big data management strategies. Big data management is the practice of collecting, storing, processing, and analyzing large and complex data sets to uncover insights and patterns that can be used to inform business decisions. In this article, we'll explore the key differences between traditional data management and big data management, and provide tips for businesses looking to adopt a successful big data strategy.