Top 12 features of Augmented Analytics platform

Augmented analytics is the future of data and analytics, which is the next big wave of uproar in the market of data. In the future, it will signify as a commanding driver in the field of analytics, Power BI, data science, ML platforms, and embedded analytics. Data scientists and analytics experts should equip themselves to embrace augmented analytics.

In this era, a huge amount of data is generated daily from multiple sources. To analyze and process the raw data manually is typically a lengthy process that involves complex steps. The first step would be to understand the data as well as requirements. Once the clarity of the strategies in question has been defined, the next task is to build an algorithm or model and at last, evaluate the model.

Augmented Analytics makes this entire process easier by automating the process of understanding, analyzing data and generating useful insights. It recognizes patterns and displays clear visualizations and trends. This automation is conducted by using Artificial Intelligence, Machine Learning and Natural Language Processing. It is deduced into three sub-categories, i.e., Augmented Data Preparation, Augmented Data Discovery, and Augmented Data Science and Machine Learning.

• Augmented Data Preparation – It compiles the data preparation process like imputing missing value, data cataloging, time series feature extraction, etc. employing AI and machine learning techniques to the process.
• Augmented Data Science and ML – It automates the crucial features of analytic modelling, which helps decrease the involvement of experts that generate, operationalize, and manage AI models.
• Augmented Data Discovery – Augmented Data Discovery employs machine learning to allow findings and visualization of insights as well as results without manual implementation of models or algorithms.

What are the benefits of Augmented Analytics?

The straightforward concept behind augmented intelligence is to support human information, speed-up repetitive tasks, and enable businesses to function faster. Let us list a few advantages of augmented analytics

• Augmented analytics with AI – Currently, augmented analytics with the inclusion of human intelligence and machine learning and AI-enabled data analytics can help to make excellent decisions.
• Augmented analytics optimizes productivity – It is tedious to do repetitive and time-consuming tasks that require very little attention. With the implementation of AI, those repetitive tasks can be automated thus increasing human productivity.
• Augmented intelligence can deliver more value – Rather than working hard to do analytics, it is better to develop such an automated system that can do such tasks as data preparation, implementation of ML algorithms, insights monitoring, etc that helps businesses at all levels.

Here are a few features to look out for in an Augmented Analytics software:

Feature #1: Augmented Data Preparation

This feature leverages Machine Learning (ML) automation to augment data profiling and data quality, recognition, modelling, manipulation, enrichment, metadata development, and cataloguing. It includes abilities like automated matching, joining, profiling, tagging and annotating data before data preparation, sensitive attribute recognition, automating repetitive transformations and integrations, data quality and enrichment recommendation.

Feature #2: Autogenerated and Analyzed Segments or Clusters

This feature leverages Machine Learning (ML) to find new segments or clusters in a dataset automatically to better process them and help with performance.

Feature #3: Autogenerated Forecasts or Predictions

This feature leverages Machine Learning (ML) to create a forecast or prediction automatically providing essential insights into pre-planned strategies and conduction methods in a business.

Feature #4: Automated Algorithm Selection and Model Tuning

This feature leverages Machine Learning (ML) to automate selecting the appropriate algorithm to fit a defined use case. It also automatically adjusts the parameters of code to improve accuracy and optimize the predictive model performance.

Feature #5: Automated Anomaly Alerting

This feature leverages Machine Learning (ML) to support automated alerting, notification, or proactive collection of anomalies based on modifications of data or detections of black-listed patterns or activities.

Feature #6: Automated Descriptive Insights

This feature leverages Machine Learning (ML) to automatically detect and deliver basic insights such as variances, associations, correlations, or trends from a column or dataset. These metrics are typically displayed as concise natural language depictions or sample visualizations.

Feature #7: Automated Feature Generation or Selection

This feature leverages Machine Learning (ML) to automatically determine the best types of data or variables to be assigned as part of the predictive model building process.

Feature #8: Automated Model Monitoring

This feature leverages Machine Learning (ML) to automate inspecting the performance of models in use to ensure the relationships are still valid, and that the model is functioning well.

Feature #9: Automated Model Packaging or Deployment

This feature leverages Machine Learning (ML) to elevate the ease and speed with which the user can transfer models from a development phase to a production phase or embed them into a business model directly. It automates the process of creating APIs or containers that are used for faster deployment.

Feature #10: Contextualized or Relevant Insights

This feature leverages Machine Learning (ML) to automate insight generation that merges explicit and/or implicit usage and user feedback data to display the most relevant data required at specific moments.

Feature #11: Key Driver Analysis

This feature leverages Machine Learning (ML) to automatically recognise vital key drivers or attributes of a specific metric in a dataset.

Feature #12: Voice-based Natural Language Search

This feature offers a voice-based interface to search through the data using natural-language statements in a dataset.

Business users and executives get excellent value from augmented analytics because of data refinement and deep analysis of strategies, plans, and more. With intelligent operations from augmented analytics integrated into the system without the need for great technical skills or expertise, a quick study of data has never been easier. Augmented analytics helps business users and executives find precise metrics more easily, make relevant inquiries, and instantly uncover insights in the context of their business. While augmented analytics benefits those without deep analytical expertise, it also boosts performance for data prep tasks and more thorough analysis.

Do you agree with the points mentioned in the blog? Feel free to share your comments if we’ve missed any important points.

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Payal Paranjape

Payal is a Product Marketing Specialist at Subex, who covers Artificial Intelligence and its application around Generative AI. In her current role, she focuses on Telecom challenges with AI and its potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.