Augmented Analytics: What it is and Why it's Important?

Understanding Augmented Analytics: A Comprehensive Study

Augmented Analytics is the latest buzzword in the field of data analytics. It is the advanced combination of Artificial Intelligence (AI) and Machine Learning (ML) techniques to help businesses achieve insightful and accurate analytics. It enables organizations to extract hidden insights in large data sets, analyze them and help executives make data-driven decisions. The market for augmented analytics is growing rapidly, with several leading companies in the analytics space incorporating augmented analytics capabilities. This article will provide a comprehensive study on augmented analytics and its impact on the industry.

Introduction

Traditional analytics platforms such as Tableau and Power BI have limitations in handling massive data sets and cumbersome processes for extracting insightful results. Augmented Analytics comes to the rescue for the companies as it uses advanced algorithms and automation techniques to bridge the gap between traditional analytics platforms and advanced analytics platforms.

What is Augmented Analytics?

Augmented Analytics can be commonly defined as a technology that incorporates machine learning and AI algorithms to automate and improve data analytics. It helps analysts and business leaders to extract key insights from large data sets, visualize them, and make data-driven decisions.

Augmented Analytics technology assists in multiple areas, such as data preparation, data visualization, decision-making, and overall business intelligence. It offers a comprehensive and easy-to-use interface on top of traditional analytics platforms, allowing non-technical users to work with the data directly without relying on data scientists or IT teams.

Advantages of Augmented Analytics

Augmented Analytics offers a lot of advantages in terms of business intelligence and data analytics. Some of them are:

1. Faster and Easier Data Analysis

Augmented Analytics streamlines the process of data analysis by automating tasks, such as data preparation, data visualization, and data modeling. Traditional analytics platforms require expertise in data analytics, programming, and statistics, which can require significant time and effort. With Augmented Analytics, businesses can extract insights faster and easier, allowing analysts and executives to concentrate on more complex data analysis tasks.

2. Improved Accuracy of Insights

Augmented Analytics technology empowers business users with the ability to analyze data and extract insights easily, reducing the chance of human error. It also offers automated data quality checks to ensure that the analysis is accurate and reliable. With accurate insights, companies can make data-driven decisions more confidently and achieve better results.

3. Enhanced Collaboration

Traditionally, data analysts and business executives often operate in silos, which can lead to miscommunication and inefficiencies. Augmented Analytics technology promotes collaboration by providing a common platform for all users to work from. This leads to better communication, knowledge-sharing, and problem-solving within the organization.

4. Improved Decision-Making

Augmented Analytics provides valuable insights to decision-makers, enabling them to make informed decisions. Its advanced analytics capabilities enable businesses to identify hidden patterns, correlations, and trends in data that might not be apparent through traditional analysis methods.

5. Increased Efficiency

Augmented Analytics automates various tasks required for data analysis, such as data preparation, feature selection, model selection, and data visualization, resulting in significant time and cost savings for businesses.

Use Cases for Augmented Analytics

Augmented Analytics has several use cases in the market, some of which are:

1. Fraud Detection

Augmented Analytics can help businesses fighting fraud or cybercrime by using its predictive analytics capabilities to identify suspicious activities. It can analyze data patterns and apply predictive analytics algorithms to detect fraudulent transactions or cyber-attacks.

2. Sales Forecasting and Customer Behavior Analysis

Augmented Analytics can help businesses predict sales revenue by analyzing data patterns and customer behaviors. It can help identify areas where businesses need to focus by providing insights on customer preferences, behavior, demographics, location, and purchase history.

3. Social Media Analytics

Augmented Analytics can help businesses analyze social media data by identifying sentiment, patterns, and trends. It can help businesses understand the impact of their products, services, brands, campaigns, and promotions on social media platforms.

4. Healthcare Analytics

Augmented Analytics can help healthcare providers analyze patient data to improve patient outcomes and care. It can identify correlations, patterns, and trends in patient data that may be missed by traditional analytics methods.

5. Supply Chain Optimization

Augmented Analytics can assist businesses optimize their supply chain by identifying patterns and trends in, for example, demand forecasts, inventory levels, and production schedules. It can provide recommendations for changes, adjustments, and optimizations to enhance the performance and efficiency of the supply chain.

The Future of Augmented Analytics

The market for augmented analytics is growing rapidly, and several industry experts believe that it is the future of analytics. According to Gartner, by 2025, augmented analytics will be the primary driver of analytics and BI adoption.

Organizations that fail to incorporate augmented analytics may find themselves falling behind their competitors in terms of data analytics and business intelligence. Augmented Analytics already provides significant benefits to businesses, and its future potential is enormous.

Conclusion

Augmented Analytics technology is a breakthrough in business analytics, providing a comprehensive and easy-to-use interface on top of traditional analytics platforms. It enables businesses to extract insights faster, more accurately, and with less effort. With its advanced automation features and AI algorithms, augmented analytics is already providing significant benefits to businesses in different industries. Augmented Analytics is the future of analytics, and the businesses that adopt this technology will benefit from its capabilities and stay ahead of the competition.

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