Advanced analytics and its real-world applications

Advanced analytics is a collective term for a wide range of analytical techniques that use machine learning.

While data analytics is about deriving insights from raw data, advanced analytics helps bring together previously untapped data sources, especially unstructured data and intelligent edge data, to gather insights analytical.

Let’s start by knowing exactly what is Advanced Analytics?

Advanced analytics is a collective term for a wide range of analytical techniques that use advanced computing techniques such as machine learning. It uses mathematical and statistical formulas and algorithms to generate new information, recognize patterns and also to predict outcomes and their respective probabilities.

Since we have been clear about this technology, let us understand some of the major benefits of leveraging advanced analytics. These include

• Robust support for day-to-day business decisions

• Rapid hypothesis and prototyping

• Improved agility for business development

• Improved decision making and automation

• Encourages user empowerment and adoption

• Enables capability for true end-to-end supply chain visibility

• Increases speed to market

• Reduces inventory costs and increases revenue

• Supports a hybrid “Agile-Lean” model

• Eliminate duplicate data modeling

Let’s see how various industries are leveraging advanced analytics to gain efficiency and growth.

• Banking and finance

The financial industry is often plagued with fraud such as transaction irregularities and advanced analytics is the best solution to predict and analyze these threats. With advanced analytics in place, it becomes easier and more profitable to acquire and retain loyal banking customers.

• Telecommunication

Customer engagement and network management are often challenges for the telecommunications industry. Now it’s easier to meet these challenges by leveraging advanced analytics and big data. These cutting-edge technologies help segment customer value, streamline operations, and automate network design.

• Automotive

Advanced analytics is crucial for automating workflow in the automotive industry. It helps to predict design errors in the design phase of vehicles. Autonomous vehicles use advanced analytics to process and exploit the huge datasets they generate to keep drivers safe and predict accidents.

• Manufacturing

Manufacturing industries use advanced analytics in warehouse management and predictive machine maintenance. When you can predict the approximate time needed to develop, process, and deliver a service, it becomes easier to manage supply chains and transportation disruptions.

• Retail

Improving the customer experience has always been beneficial for the growth of the retail industry. The advent of advanced analytics is accelerating this process by predicting customer behavior and preferences. Now it’s easier to deliver the best in-store experiences.

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