Large and complex data sets that are impossible for traditional data processing applications are called as Big Data. Few of the most common challenges that are faced by Big data are curation of data, storage, transfer, visualization, and managing privacy of information. Big Data is not just a new set of technology that companies are experimenting with gargantuan data sets but its redefining value of new streams based on the information that is leveraged. This decade is experiencing a confluence of enterprise IT, cloud computing and Big Data. This reshapes the technology industry by portraying emerging social trends and mobility.
Enterprise Big Data The idea of enterprise big data emerged by web search companies who faced the issue of querying very large aggregations of loosely-structured distributed data. To support distributed computing on computer cluster to work on large data sets Google developed program MapReduce. This program is an inspiration from Google File System (GFS). An apache project written in java called Hadoop is built and used by a global contributors community. It has been extensively used by Yahoo across its various businesses on 38,000 nodes.
Big Data advantages business intelligence (BI) from Big Data Analytics. It provides access to valuable data that was not available to deconstruct before. BI enables BI professionals to control the expanded analytics and create 360 ° Perspectives. For example, you can recreate powerful customer visualizations for Customer relationship management systems by creating customer sentiments, wish lists, and actual response data to measure true campaign effectiveness.
The advantages of processing Big Data
Real-time quick view into errors helps companies react at a fast pace to quickly mitigate the effects of an operational problem that are known instantly within an organization. This can save customers from having to stop using your products.
You can immediately notice the new strategies that are followed by your competitors. You can always stay one step ahead of the competition and can be immediately intimidated by changing strategy.
Service and Fraud
Organizations can monitor products used by their customers and pro-actively respond to forthcoming failures hence, this could lead to higher conversions and generate extra revenue.
Hacking and IT Security plays a very vital role and with the help of Big Data analytics fraud can be detected in real-time so appropriate measures can be taken to limit the damage in real-time.
Some are the below mentioned statistics for Big Data that are awe-inspiring
An organization’s ability to exploit value from Big Data is very critical for a company’s future success; this is believed by 70% of IT decision-makers.
If companies do not embrace Big-Data 65% companies face the fear of becoming noncompetitive.
Below is an diagrammatic representation of the above mentioned statistics to help you understand the industry scenario better.
64% of companies are experiencing a massive change in traditional business boundaries; they face the challenge of enabling non-traditional providers to enter their industries and face a strong competition for survival.
Established companies are already facing 54% increase in their competition from data enabled start-ups.
A better way to understand Big-Data is through an Use Case. Through Big-Data we can optimize funnel conversion. We can explain this best with the help of a use cases. During the entire sales conversion process big data analytics allows a company to track and locate leads right from a adword ad click to the final transaction. This helps to reveal the conversion process and how it can be improved.
Use Case Company – T.Mobile
Industry – Communication
Employees – 30,000+
Type – Optimize Funnel Conversion
Purpose – In order to identify customers that be upgraded to higher quality of products and to identify high lifetime customer value T-Mobile uses multiple indicators such as billing and sentiment analysis. This will help the T-Mobile team to focus on retaining the important customers and ensure customer loyalty as well as customer satisfaction.
Hence, we can conclude that Big Data Analytics projects are new but with quiet impressive results. Projects have been focused on increasing revenue and reducing costs by driving business in new ways and accelerating time-to-value for various business processes. The most critical and important thing for IT will be to bring a change in mind-set, time-to-value to guarantee success by improving business productivity and revenue.