by May 12, 2014 0 comments

It’s not often that an IT innovation is as disruptive as Big-Data. The tecnology has brought a paradigm shift in our approach, storage, usage and monetization of data assets. Every path-breaking innovation has its share of hype, expectations and “Big Data “is no exception. In-fact hype has reached alarming levels of something comparable to Y2K and an e-commerce bubble of 2000. It’s not an exaggeration if we say IT associates of every domain and platform browsed through “Big-Data” to evaluate a career shift.

While we come across and hear numerous case studies of successful Big Data implementations across various domains, we see in our experience, early adopters of this technology have started reaping benefits by acting on the breakthrough insights.

A few worth mentioning are:

IT infrastructure management services (ITims): One such domain where Big Data implementation is huge success. The data feeds are from the applications and servers which are spread across geographies. These have to be monitored in near real time to detect possible failures and apply remedial measures. This necessitates a real time analytics tool that big data technologies have successfully catered to. (The data feeds are structured time stamped data)

Retail: Another domain where Big Data solutions could find increasingly successful implementation. Retailers are using Big data technologies to update/adjust prices in near real time. Using a combination of GIS and Location analytics, retailers were able to find its implementation in Supply chain management. By a combination of GIS, Location analytics, observing buying patterns, and social media feeds about a customer, retailers were able to tailor and personalize the offers. Based on the social media feed and mobile data the retailers was able to send an SMS suggestion of nearest retail store and the offers of interest running in the store.

 

Enterprise service desk implementation: A well-built knowledge base helps in quick query resolution and implementation of defect prevention and remedial measures. The data feed both structured and un-structured comes through multi-channel customer interactions i.e. through incident logging applications, voice, email and text messages.

Social media analytics: Enterprises are increasingly using Big-data technologies to complement their existing warehouse application, A $ 2bn conglomerate with interests in Finance, healthcare and customer communication domains is adapting Hadoop solution to process the raw click and social media data streams. This provides the valuable customer insights and preferences which are combined with warehouse data.

Storage alternative: Hadoop is considered as a raw active data archival strategy replacing the tape storage systems by one of the leading health care information management provider. Regulatory authorities mandate the availability of data for the period of 7-10 years. Hadoop offers a cheap alternative to traditional archival techniques.

Some Myths Surrounding Big Data
Our experience with Big Data implementation busts some of the popular misconceptions doing rounds among IT Practitioners and non-practitioners alike such as:
– Big Data is for unstructured data only
– Big Data is just hype
– Big Data replaces traditional Data warehouse. Theoretically Big Data technologies seem to replace the traditional Data warehouse. However, Data warehouse technologies offer a staggering list of features owing to the refinement and investment over the last 3 decades. Data warehouse performance with unmatched SQL optimizers has reached maturity of providing self-service BI to business users. As Gartner predicted Big-Data technologies may take 5-10 years to reach the plateau of Productivity. They hold a promise for the future but are not a replacement to the current set of BI applications.

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