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    What do you mean by Augmented Analytics?

    The word augment means: make (something) greater by adding to it; increase. Along similar lines, Augmented Analytics is the use of technology (primarily Artificial Intelligence (AI) & Machine Learning (ML)) to augment users about how data is being prepared, analyzed, and shared with other users.

    Broadly speaking, it has three components: Augmented Data Preparation, Augmented Analytics for Analytics & BI and Augmented Analytics for Data Science & ML, which we will discuss further.

    As Gartner defines it, Augmented Analytics refers to: “Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management, and deployment.”

    Where is Augmented Analytics used?

    Gartner estimates that, on average, only 35% of people in organizations have access to Analytics & BI tools. To increase the adoption rate of analytics, and help organizations explore, analyze, understand, and utilize more data more effectively, Augmented Analytics is used.

    In the lifecycle of Data Analytics, Augmented Analytics is used right from data preparation, insight generation, and explanation. In this journey of data the major components where Augmented Analytics is used are:

    Augmented Analytics for Data Preparation

    Let’s take a basic example, with AI & ML in Data Preparation in ETL or the Extract, Transform, and Load phase, automatic detection of dates, geographies, personal information, etc., can take place. With Augmented Analytics, we can accelerate data profiling, improve data quality, accelerate data cataloging, automate metadata development, and prepare data easily. Such systems are also useful in removing special formatting in PDFs or text data and eliminating manual interventions.

    Augmented Analytics for Analytics and Statistical Techniques

    After augmenting data preparation, AI & Machine Learning techniques can be applied to bring context-aware insights and to automatically select relevant statistical techniques that can be applied to the data like forecasting, clustering, pattern recognition, etc.

    Use in Conversational Analytics

    Also in modern BI platforms augmented analytics is being used with techniques like Natural Language Processing and Natural Language Generation in creating rich descriptions of insights by taking minimal information from the user. Based on the questions asked by the user highly relevant insights can be generated in the form of charts, graphs, etc.

    These three are just a few examples of how Augmented Analytics works. In addition to these, it can also be used in Data Science and Machine Learning aspects like feature engineering, model explanation, model management, etc. This is especially useful when there are not enough skills or resources available to implement and manage advanced analytics models like data scientists.

    In essence, Augmented Analytics can be used effectively to automate tasks like Data Preparation, Smart Data Management, code generation, visual creation, and NLP.

    Benefits of using Augmented Analytics

    Some of the benefits of using Augmented Analytics to increase the value of your existing processes are:

    • Faster Delivery of results and insights.
    • Uncover hidden data and patterns.
    • More trustworthy algorithms and processes.
    • Increase in analytics adoption of the company.
    • Increase in Data Literacy across the organization.
    • Improved efficiency in automating operational tasks.

    READ MORE: How The Potential Of Augmented Analytics Will Push The Analytics Adoption By 30 Percent