Advanced analytics is a collective term for a wide range of analytics techniques that uses machine learning.
While data analytics refers to drawing insights from raw data, advanced analytics help collate previously untapped data sources, especially the unstructured data and data from the intelligent edge, to garner analytical insights.
Let us start by knowing what exactly is Advanced Analytics?
Advanced analytics is a collective term for a wide range of analytics techniques that make use of cutting-edge computing techniques such as machine learning. It employs mathematical and statistical formulas and algorithms to generate new information, recognizes patterns, and also to predict outcomes and their respective probabilities.
Since we came clear about this technology, let us understand some of the key benefits of leveraging advanced analytics. These include
• Robust support for day-to-day business decisions
• Quick hypothesis and prototyping
• Improved agility for business development
• Optimized Decision Making and Automation
• Encourages user empowerment and user adoption
• Allows Capacity for True End to end Supply Chain Visibility
• Increases Speed to Market
• Lowers Inventory Costs and boosts revenue
• Backs a Hybrid “Agile-Lean” Model
• Eliminates Duplicative data modeling
Let us look at how various industries leverage advanced analytics to gain maximum efficiency and growth.
• Banking and Finance
The financial sector often grapples with frauds like transaction irregularities and advanced analytics is the best solution to predict and analyze these threats. With advanced analytics in place, it becomes easier and cost-efficient to acquire and retain loyal banking customers.
Customer engagement and network management often pose a problem for the telecommunication industry. Now, it is easier to deal with these challenges by optimizing advanced analytics and big data. These cutting-edge technologies enable customer value segmentation, streamline operations, and automate network designing.
Advanced analytics is crucial to automate workflow in the automotive sector. It helps predict design errors in the blueprint stage of vehicles. Autonomous vehicles use advanced analytics to process and exploit humongous data sets generated by them to ensure driver safety and predict accidents.
The manufacturing industries use advanced analytics in warehouse management and predictive maintenance of machines. When you can foresee the approximate time to develop, process, and deliver a service, it becomes easier to manage supply chains and disruptions in transportation.
Driving better customer experience has always been beneficial for the growth of the retail sector. The advent of advanced analytics fast-track this process by predicting customer behavior and their preferences and now it is easier to provide the best in-store experiences.
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This UrIoTNews article is syndicated fromGoogle News