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What Is The Difference Between Augmented Analytics And Explainable Artificial Intelligence?

What Is The Difference Between Augmented Analytics And Explainable Artificial Intelligence?

Augmented analytics and explainable AI (XAI) are among the top data and analytics technology trends for 2019, according to many reports. In this article, we will help you to understand the basics between augmented analytics and explainable AI and its potentials.

Augmented Analytics


This technology focuses on areas such as augmented intelligence by using machine learning and natural language generation in order to mould how the contents of analytics are developed, consumed as well as shared. The capabilities of this technology will augment in the organisations in the form of data preparation and management, modern analytics, business process management and other such. Many reports have touted it as the next wave of disruption in the data and analytics market that the data analytics leaders should plan to adopt.

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This approach uses machine learning techniques in order to automate data preparation, insight discovery as well as sharing in an organisation. It will help the data scientists to spend less time exploring the data and more time focus on the strategic purposes for the profit of an organisation. This approach undoubtedly helps in better decision-making, accurate predictions in an organisation, better analysis of the product, price, financial, and other such aspects. The technology can also point-out the factors which are impacting your outcome as well as simplify your analysis of data in order to gain more important insights.

Explainable AI (XAI)


Most of the complex AI inbuilt models are so hard to describe how, when and where. This reason often commences an organisation in a questionable state and to overcome such issues, the concept of explainable AI emerged. Gaining a distinct explanation framework for the algorithm will help the users as well as the customers in an organisation with better information and also enhance the trust in the model over time


This approach contrast with the concept of black box models in machine learning. It is possible as well as desirable need by an organisation for the benefit over time. Explanations are crucial for the detections of anomalies in a model and explainable AI can help to find out that anomaly and let the user understand the error behind the model and fix it.

Brand Value

According to the Augmented Analytics Market by Software, Service (Training and Consulting, Deployment and Integration, and Support and Maintenance), Organization Size (SMES and Large Enterprises), Deployment Type, Vertical, and Region – Global Forecast to 2023, the  global augmented analytics market size is expected to grow from USD 4.8 billion in 2018 to $18.4 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 explainable AI is augmenting in the market than the past few years as the need for artificial intelligence and machine learning models are increasing.

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The potentials behind the two approaches are mentioned below:

Augmented Analytics: This approach analyses an organisation’s data in order to gain insights and depicts which factors are impacting the outcomes. It helps in simplifying the analysis of data and providing the data scientists to concentrate more time on the actionable insights.

Explainable AI (XAI): This approach can build explainable models and also it is able to sustain the high-level of prediction accuracy. It helps in an organisation by increasing the trusts of the customers and other such aspects by explaining the working behind a specific model.  

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