Dr. Sebastian Derwisch, Senior Analyst – Data & Analytics
Interest among business departments in defining their own use cases and extracting valuable information from data themselves is growing. Classical business intelligence software lacks the agility needed to gain insights from data through explorative analysis. As a result, the use of self-service analytics has become popular. Business users can now prepare and analyze all available data on their own – visually and with the help of mathematical models.
In order to analyze data, you need to master data preparation, visualization and the necessary statistics. Modern technology can support all these steps. Examples include graphical user interfaces for data preparation, alerts highlighting problems in data, automated visualizations, correlation analyses and automated forecasts. Such functions should enable business users to analyze data independently and, on this basis, to formulate requirements more effectively and without having to rely on IT or data scientists, whose resources are often limited within the company.
The comprehensive technological support provided to users during various phases of analysis is called augmented analytics. Augmented analytics uses data analysis and machine learning heuristics to automate analyses or to generate proposals for directed analysis.
Users are supported in the process of identifying and processing data up to the visualization or application of a mathematical model. Pattern recognition is thus simplified and partially automated. Natural language processing (NLP) simplifies queries and analyses by automatically filtering, aggregating and visualizing data with simple language inputs. Even the complete automation of the analysis process and the creation of prognosis models through automated machine learning is already a reality today. In recent years, various manufacturers of analytics and BI software have addressed the topic of augmented analytics and added corresponding functionality. Augmented analytics is an important development towards better data analysis by more users. Reason enough to take a detailed look at the functions available today and evaluate whether they could create advantages for users, for example, by reducing the time spent on implementation or by allowing users to carry out activities that actually exceed their methodological abilities.