Augmented Analytics: The Next Frontier in Data Analysis?

Augmented analytics is changing data analysis by combining artificial intelligence (AI) and machine learning (ML) with traditional analytics. This new and innovative approach automates many aspects of data analytics, examples being data preparation, insight generation, and insight explanation, enabling users to make data-driven decisions more efficiently and accurately. In this article, we will delve into what augmented analytics is, its benefits, the technologies that drive it, and its applications in the Buy-Side space.

Key Components of Augmented Analytics:

Automated Data Preparation:

Data cleaning, transformation, and integration is often a long-winded process. Augmented analytics tools use AI to automate these processes, ensuring that data is consistent, accurate, and ready for use without extensive human intervention.

Natural Language Processing (NLP):

NLP enables users to interact with data using natural language queries. This makes data analysis more intuitive and accessible. This will allow businesses to ask questions and receive the correct data more easily.

Machine Learning and AI Algorithms:

This is the backbone of augmented analytics, automatically detecting patterns, correlations, and anomalies in data. The potential of this is still being explored, however, they are being used to predict future trends in markets.

Insight Generation and Explanation:

Augmented analytics generate insights into data and explains it in an understandable way. AI provides context, it highlights key factors and data points, and suggests possible actions based on the insights that it generates.

Benefits of Augmented Analytics:

Efficiency:

The automation of data preparation and analysis significantly reduces the time required to obtain insights. This is allowing organisations to respond rapidly to market changes and opportunities, especially in volatile industries.

Accuracy:

AI and ML algorithms can analyse large datasets with high precision, reducing the risk of human error and potentially uncovering insights that might have been missed.

Accessibility:

This process enables non-technical users to perform complex analyses. It could be argued that augmented analytics democratises data access, allowing more employees of businesses to make decisions based on data; or at least simplify the process.

Scalability:

As augmented analytics can handle large volumes of data, it can scale with the growing data needs of an organisation, ensuring that insights remain relevant as businesses grow and data sets expand.

Next week I will be explaining the key applications of Augmented Analytics in the Buy-Side sector and the challenges and considerations.