Advance Data Analytics
Advanced Data Analytics is Making Your Business Smarter
Today’s analytics are going further, thanks to hardware and software designed to help organizations make better decisions.
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Believe it or not, we have been analyzing data for ages, and analyzing large volumes of data is not new. However, getting the results faster and accurately is the result of the evolution of Big Data & Analytics.
The focus is shifting from products/services to experiences. Customer experience is the focal theme permeating all activities of any organization today. Every organization around us is successful, depending on the analyses they perform. As a result, businesses are embracing big data and analytics to gain insights into market trends and identify customer behavior.
In a fast-changing landscape, adapting the perfect blend of open source, proprietary, and cloud tools/technologies is key to achieving business goals. Our meticulous and strategic techniques are applied using emerging technologies that drive customers' business. The platforms, enabled with high availability, business continuity & disaster recovery, fault tolerance, and scalability features, hold prime importance to achieving the best end-user experience.
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Getting the most out of big data analytics requires an approach based on optimizing hardware and software for scalability, performance, and cost-effectiveness.
As the number of connected devices at the intelligent edge grows, so too does the demand for understanding collected data in real time. With real-time analytics, information can be processed and modeled like never before, with insights emerging as soon as new information enters the data stream. From improving fleet operations to ensuring patient safety, real-time analytics holds the promise of delivering insight when it can make the most difference: right away.
Using predictive analytics, businesses gain the ability to understand likely future outcomes—and optimize those outcomes to achieve their biggest goals. Large-scale predictive analytics require dedicated infrastructure capable of handling a full range of analytics workloads. Flexibility is key: achieving success in predictive analytics requires a data infrastructure adaptable enough to enable platforms like SAP HANA or Oracle Analytics Cloud. It should also support streaming analytics solutions and open source analytics tools like Spark.
Some of the most-promising developments from data scientists have emerged from the convergence of analytics and AI. Neural networks and machine learning have revolutionized the ability of computers to make inferences and understand a wide range of unstructured data. By augmenting analytics with AI, retailers can better understand how customers move through stores, and manufacturers can more easily identify defective products. Doctors can get a better understanding of medical imaging, while warehouses can enable smart inventory tracking.