Glossary

Data Awareness & Data Management

The competitiveness of companies increasingly depends on professional and efficient data management. In many companies, however, there is still a lack of a basic understanding of how their own data can be sensibly managed, processed and exploited internally. This requires a basic understanding of data, i.e. data competence among employees.

Big Data Analytics & Data Engineering

Big data analytics examines and analyzes large amounts of data from different sources. In a corporate context, this can include Website data, data on customer behavior, controlling and employee data as well as open source databases. Well-prepared data and the patterns it reveals can be used to improve corporate processes and optimize business models. Data engineering describes an essential basic requirement for efficient data analysis: the construction of suitable data architectures and systems. Only then can data be systematically collected, processed and made usable. The challenge for companies is to combine different data sources in a meaningful way and adapt them to their own business model in order to generate added value.

Data Science Life Cycle for Companies

The data science life cycle is a strategic approach for the successful implementation of data projects in companies. It includes all the important steps for successful data management: business understanding, data mining, data cleaning, data exploration, data engineering, predictive modeling & analytics and finally data visualization. This enables companies to implement data projects or business cases in a structured and results-oriented manner from start to finish. The combination with design thinking can bring additional added value. The combination of data science and design thinking offers the unique opportunity to correctly define, understand, prototype and test the actual challenge right from the start. As a method, design thinking mainly helps align projects in a user-oriented manner and check the objectives repeatedly without single-mindedly devoting oneself to the implementation of the project. The results achieved in this way can often be more valuable for companies than originally intended.

Business Intelligence

Business intelligence (BI) features a special technology and tool-driven focus and supports decision-makers in (data) management and the visualization of usable information. BI encompasses a variety of tools, applications, and methods that enable companies to collect data from internal systems and external sources and prepare them for analysis. This can then be used to execute queries and create reports, dashboards and data visualizations.

AI-Basics, Control & Management of AI-Projects

Artificial intelligence (AI) is not new, but it is delivering completely new opportunities through big data as well as greater computing power, better software and programming solutions. This not only enables companies to acquire data faster and more accurately, but also to use specific, intelligent solutions for the systematic optimization of company processes or entire business models. But the use of AI primarily requires good management and target-oriented control.

Data Science with Python, machine learning, Deep Learning

In order to work directly with data, you need software solutions, analysis tools or simple programming languages. There are now countless solutions on the market. The Python programming language is a universal and easy-to-learn application for data analysis (and data science in general). Machine learning and machine learning algorithms are an elementary sub-area of artificial intelligence. A distinction is made between supervised learning and unsupervised learning, in which the necessary parameters are set by a programmer. Here, too, the Python programming language is well suited for learning and for use in specific cases.

Deep learning (DL) is a special sub-discipline of machine learning and the supreme discipline in the field of artificial intelligence. Deep learning algorithms are currently the most exciting class of algorithm, as they are self-learning systems made up of multi-level structures of algorithms, which are also known as “neural networks.” Due to the complexity of the algorithms and the vast amounts of data that can be trained, very precise conclusions are possible, which the systems can include again in further information processing. This is where the greatest potential currently lies in the field of artificial intelligence.

Security Awareness & Analytics

IT security in companies starts with employees. Cyber attacks are now considered to be the greatest existential risk to companies. The human factor is crucial, because almost 9 out of 10 cyber attacks exploit human errors. Security awareness training can therefore make a decisive contribution to corporate security. In addition, network security, i.e. the functionality and integrity of company networks, is an important building block for the security of a company.

Information Security and Privacy

Information security aims to protect information and data and is based on three areas: confidentiality, availability and integrity. Sensitive data must be protected from unauthorized access by third parties, but must remain fully and correctly accessible for authorized users at all times. Corresponding measures and standards should be implemented by the company. A special focus here is on the interface between information security and data protection, as the objectives between information security officers and data protection are sometimes contradicting each other.

IT Security Training / BSI IT Basic Protection

Employees can be empowered to implement successful IT security concepts (to protect the specific electronically stored information and IT systems) on many levels. In principle, different norms and standards are relevant. On request, the HPI Academy can offers training courses on the following topics: IT security officer (IT-Sibe/CISO) ISO/IEC 27001/27002 and various BSI training courses (e.g. BSI IT-Grundschutz-Praktiker, ISO 27001, IT- Security audits at KRITIS operators).

Cybersecurity Management in Companies

The aim of cybersecurity is to identify cyber risks and reduce them to a tolerable level. From a strategic perspective, a company’s cybersecurity program and risk management must perform certain core functions. Processes for the identification of cyber risks, the use of protection and detection functions as well as organizational and technical measures to react to and restore after a cyber incident must be in place. This also includes relevant standards, frameworks and best practices to set up a cyber risk management program from a technical, regulatory and legal point of view.