Augmented analytics

Definition

Augmented analytics refers to the use of machine learning and artificial intelligence (AI) to automate data preparation and analysis. This enables users to gain deeper insights and make more informed decisions without the need for extensive knowledge of data science.

Background

The term augmented analytics was first coined by Gartner in 2017. The aim is to expand traditional data analysis by using advanced technologies such as machine learning and natural language processing. This enhances analysts' capabilities by providing automated insights and recommendations.

Areas of application

Augmented analytics is used in various areas, including marketing, finance, human resources, and logistics. By using this technology, companies can identify trends, analyze market conditions, and make strategic decisions based on data-driven insights.

Benefits

The main benefits of augmented analytics lie in increasing efficiency and improving decision-making. By automating time-consuming tasks, analysts can focus on more strategic tasks. It also enables less experienced users to perform complex data analyses and gain valuable insights.

Challenges

The challenges of augmented analytics include data quality and integration into existing systems. Companies must ensure that their data is clean and well-structured to enable accurate analysis. Another point is user acceptance and trust in the technology, as many are still skeptical about automated decisions. Solutions include training and transparent communication strategies to explain how the technology works.

Examples

One example of the use of augmented analytics is a B2B retailer portal that uses machine learning to analyze buying patterns and generate personalized product recommendations for business customers. This can increase customer satisfaction and increase sales figures.

Summary

Augmented analytics is revolutionizing the way data is analyzed and used by using advanced technologies to automate and improve data preparation and analysis. It offers significant benefits in terms of efficiency and decision making, but faces challenges such as data quality and user acceptance.