The Bottom Line of Big Analytics – By Serge Haziyev

With Big Data Analytics challenges becoming a nucleus of modern business, it is no longer sufficient to rely exclusively on traditional models while managing projects. Big Data attracts great interest across organisations and industries because of its potential used to derive greater understanding of past and present events to predict the future and prescribe actions to create desired outcomes. But Big Data alone can’t achieve these things and simply having the data isn’t enough. This is where analytics enters the discussion.

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Big Data and Advanced Analytics combine to form Big Analytics. When capturing and transforming vast amounts of data into meaningful and highly useful information, you can create more impactful decisions in the following areas:

Retailers are using Big Analytics to create personalised shopping experiences and enhance loyalty programs by initiating tailored customer conversions to present unique offers to upsell and cross-sell products based on a shopper’s online and in-store browsing, as well as similar consumer behaviors.

Healthcare administrators can use Big Analytics to leverage operational, economic, and demographic and health trends. Doctors and clinicians can leverage Big Analytics to identify at risk patients, tailor preventative healthcare activities, and prescribe treatment based on similar diagnosis and patient history.

Big Analytics are being used to find the best route for deliveries and backhaul; updating real-time current conditions and calculating load availability against vehicle specifications.

Sales and Service
Big Analytics can be used by inside sales and customer support centers to make specific offers and modifications to subscriptions or buying plans based upon the nature and progression of a customer interaction.

Fraud Detection
Big Analytics can quickly detect changing human behaviors and network anomalies signaling a threat, as well as take immediate action to thwart an attack.

Given the success and potential of Big Analytics, the question is why aren’t more companies and organisations embracing information-driven business models? Below see three major use cases and associated technical challenges that are common across the Big Data industry including startups, mature ISVs and enterprises.

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With Big Analytics leaping forward, it is important to enable the data discovery and improve data science and analytics teams performance to harness the potential of exercising advanced analytics across all possible data sources.


About the Author
Serge Haziyev is a VP, Technology Services Group at SoftServe. He has more than 18 years’ experience in designing, evaluating and modernising large scale software architectures in various technology domains including BI, Big Data, Clouds, SOA and carrier-grade telecommunication services for both Fortune 100 and startups.
He is a co-author of the architectural poker game currently used by leading institutions to teach students to architect Big Data solutions. Also, Serge is a co-author of Big Data chapter in the SEI Series book Designing Software Architectures: A Practical Approach.

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