Seven Ways Enterprises Can Benefit from Augmented Analytics

Six Ways Enterprises Can Benefit from Augmented Analytics

Data is growing at an incredible rate due to the proliferation of IoT devices, and every second, people are leaving new digital footprints. Strong analytical tools powered by AI are important for unleashing the full potential of data, since it is collected and processed in a number of complex ways.

Analytics and Business intelligence providers are gradually incorporating augmented analytics into their feature and products offerings in order to improve the functionality of their platforms. Therefore, real-world augmented analytics cases in the workplace have begun to accumulate.

Augmented analytics uses automation, Natural Language Generation (NLG), AI and ML to make data management, tracking, and assessment easier while also boosting data literacy.

Here are seven ways how enterprises can benefit from augmented analytics:

Increase online visibility

By attaching their Google Analytics profiles, for instance, to the augmented analytics platform, businesses can track the progression of their customers from beginning to end. Furthermore, augmented data analytics technologies can help with document analysis and presentations by converting enormous amounts of data into aesthetically appealing and easy-to-comprehend graphics.

Increase the range of possibilities 

Unlike traditional Business Intelligence solutions, which rely on an idea or a theory to work, Augmented Analytics employs Artificial Intelligence algorithms and contextual recommendations to uncover hidden insights that data analysts might otherwise overlook. As a result, users may have more confidence in employing AI and data analytics tools to reveal additional insights when they discover hidden similarities, and special cases in results.

Quick delivery of value

Faster automated data processing means faster results and more flexibility for employees, allowing them to focus on more important tasks like leveraging research findings to unearth new insights and making data-driven decision management.

It uses Machine Learning algorithms to automate routine activities and move enterprises from data collection to appealing visualisation in a single, quick step.

Also Read: Reimagining BI and Analytics with Augmented Analytics

Accelerate digital transformation efforts

Obtaining clean, high-quality data can be time-consuming, but augmented analytics makes it much easier. These platforms can swiftly clean, integrate, and transform data from a variety of sources, resulting in high-quality reports, speeding up digital transformation efforts.

Increase data access

Another advantage of augmented analytics is that it makes the technology more user-friendly. Non-technical users, for example, can write queries in natural language instead of SQL, boosting the number of queries conducted across the organization. Natural Language Generation, on the other hand, helps users in comprehending the results of those queries by verbally describing them or automatically generating relevant visualizations.

Bring down costs 

Rather than forcing data teams to spend hours preparing, cleaning, and formatting data for reporting, augmented analytics automates the entire process using AI and ML. As a result, businesses can move considerably more quickly and build the groundwork for long-term improvements that will continue to provide significant businesses benefits.

Taking Business Intelligence to the next level

Augmented Analytics is one of the most recent advancements in Business Intelligence (BI) and analytics methodologies, and it promises to offer a fresh perspective on current tools. By introducing AI aspects into BI and analytics operations, Augmented Intelligence in business analytics lets users organize data, produce insights, and quickly share them with others in the enterprise.

It combines Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies to give a fully redesigned user experience across the entire Business Intelligence (BI) process. The consumption of data, pattern recognition, insight discovery, and platform interaction will all be automated and efficient.

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Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.