Six-part topic series: Drivers of digitalization - 3: Drivers: Augmented analytics

Today we would like to draw your attention to augmented analytics. With augmented analytics, the processes of data collection, cleansing and evaluation can be automated and thus noticeably accelerated. Instead of numerous tedious and error-prone work steps - from database queries to the consolidation of results - machine learning processes and algorithms are used that independently examine data records, recognize patterns and anomalies and immediately derive causes, hypotheses and recommendations for action. Augmented analytics thus relieves the burden on data analysts and specialist departments in particular. They can make informed decisions and take action more quickly. The challenge is to ensure a high-quality database on a permanent basis. After all, augmented analytics is only as good as the data that is available for analysis.

Next driver in the series: Natural Language Processing
Author: Dario Waechter