What is Augmented Analytics?

Augmented analytics describes a technology with which Data analysis in the business environment and the so-called Business Intelligence using components of the machine learning and natural language processing (Natural Language Processing - NLP) can be supported. Business intelligence refers to processes for collecting, evaluating and visualising data in the context of a company.

The objective of these "extended analyses" is to assist in the Carrying out and preparing data and insight analyseswhich will subsequently serve as a basis for decision-making.

Process and benefit

In today's business world, generating information and raw data is not a major challenge. A critical path in the context of Big Data However, the following Data analysisThe data collected beforehand is to be processed and interpreted in such a way that it can ultimately serve as a basis for decision-making.

This is exactly where augmented analytics comes in, although data generation can also be included in this concept. The Added value results from the fact that, as a rule, both the manual and the time required for data analysis can be reduced, since machines are able to systematically and quickly search through large amounts of data. In combination with the methods of machine learning, opportunities arise to develop algorithms that are able to.., Identify trends as well as dependencies and patterns or make forecasts using predictive models.. These algorithms can sometimes optimise themselves, which can also result in an improvement in the quality of the results over time.

Although data generation is not the core element of the method, data collection can also be integrated into the technology and, in addition, benefits can be generated by separating useful information from unusable information in the raw data during data cleansing or preparation.

By implementing natural language processing in augmented analytics, this serves as a Communication interface. On the one hand, it is used to correctly interpret questions for carrying out data analyses, on the other hand, this technology is also used to prepare or communicate the results after the analysis has been carried out. This lays the foundation for supporting decisions in the business environment, which is the main benefit of augmented analytics.

Augmented Analytics Applications

Many companies offer software solutions that make use of the skills of augmented analytics and big data. In addition to, for example, SAP Analytics Cloud, Oracle Analytics Cloud, IBM Cognos Analytics or Power BI from Microsoft, the companies Tableau and SAS with SAS Visual Analytics also offer solutions in this area. The technology is used in the following business areas:

  • Healthcare: Companies use augmented analytics to optimise their operations and analyse length of stay and bed occupancy rates. In addition, forecasts of readmissions for recently discharged patients can be made and counteracted accordingly.
  • Finance and bankingIn this industry, augmented analytics is used to improve the services offered to customers and promote business growth. This enables the evaluation of data on trends and customer acquisition costs as a priority, which can subsequently be developed into specific services offered to different customer groups. In addition, it is possible to identify anomalies in transactions and, in the best case, clean them up in advance.
  • Manufacturing and retail: Similar to banking, technology is also used in retail to analyse customer trends and the efficient use of advertising materials. In production, it is used for capacity planning, process and supply chain optimisation, among other things.
  • AirlinesAirlines also use big data to increase customer satisfaction. Furthermore, it is used to forecast fluctuations in demand, which can be responded to with corresponding capacity changes.
  • TelecommunicationsIn the telecommunications industry, data on trends in telephone behaviour and internet usage can be analysed. Based on this, both services for customers and, for example, own bandwidth capacities can be optimised.