Augmented Analytics vs Interpretable Artificial Intelligence. What is the Difference?

Augmented Analytics vs Interpretable Artificial Intelligence. What is the Difference?

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According to many reports, Augmented Analytics and Interpretable AI are among top data and analytics technologies trends for 2019. In this article, we will help you understand the basics between Augmented Analytics vs Interpretable AI and its possibilities.

Augmented Analytics

This technique focuses on areas such as enhanced intelligence using machine learning and natural language generation so that the content of analytics is developed and shared. The capabilities of this technique will augment in the organization in the form of data preparation, increase in management, modern analytics, business process management and so on. Many reports have described it as the next wave of data and interaction disruption in the market, which data analytics leaders should plan to adopt.


This approach uses machine learning techniques to automate data preparation, insights as well as to share the insights in an organization. This data will allow scientists to spend less time searching for data and focus more time on strategic objectives for organization benefits. This approach undoubtedly helps in making better decisions in an organization, accurate predictions, better analysis of the product, price, financial and other such aspects. Technology can also point to factors that are affecting your results, as well as simplify your analysis of data in order to gain more critical insights.

Interpretable Artificial Intelligence

It is very difficult to describe most complex AI inbuilt models, how, when, and where. This reason often puts an organization in a suspicious position and to overcome such issues, the concept of Interpretable AI emerged. By obtaining a different explanation framework for the algorithm will help the organization with better information of the users as well as the customer and will increase the confidence in the model over time.


This approach is contrary to the concept of black box model in machine learning. It is possible to benefit over time with an organization as well as the desired requirement. Explanations are important for detecting discrepancies in a model, and Interpretable AI can help to determine the discrepancy and allow the user to understand the error behind the model and fix it.

Global Brand Values

According to the global market forecast for the global assessment forecast period and According to the software, service (training and consultation, deployment and integration, and support and maintenance), organization size (SMEs and large enterprise),  the  global augmented analytics market size is expected to increase from $ 4.8 billion in 2018 to $ 20.4 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 30.6% during the forecast period. On the other hand, the brand value of Interpretive AI is increasing in the market compared to the last few years because the need of artificial intelligence and machine learning models is increasing tremendously.

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Augmented Analytics: This approach analyzes the data of an organization in order to gain insight into what factors are affecting the results. It helps to simplify the analysis of data and provides it to data scientists so that they can provide more time focusing on actionable insights.

Interpretable AI: This approach can create an interpretive model and it is capable of maintaining high-level prediction accuracy. It helps in an organization by enhancing customer trust and such other aspects by explaining the work behind a specific model.

So that's it for today, See you very soon with a new update. Stay tuned with us for more updates on Artificial Intelligence, Machine Learning, and Data Science.

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