1. Introduction
Organizations store large quantities of customer, operational, and transactional data, with new information being collected every second. Traditional BI systems selectively analyzed data because of the inability to process large qualities of data. Big Data processes all available data, no matter what the size, to uncover hidden patterns, unknown correlations, and useful insights. The use of social networks and collaborative platforms is providing us with newer data that enables understanding customer preferences.

Big Data presents incredible opportunities and enormous challenges unlike conventional business intelligence solutions. While conventional BI applications answer the "What", Big Data Analytics answers the "Why". Data is now interwoven into industries, and is as essential as other essential factors.



Big Data has been around for over a decade now. However, it was not feasible to use Big Data for data processing because of the lack of proper tools. The available tools and the huge quantum of data have propelled Big Data into the forefront. The recent advancement in tools for managing Big Data has made the prospect of gaining insight both feasible and cost effective.

Some examples of tools available for Big Data are:

• Hadoop: An open-source platform for consolidating, combining, and transforming large data volumes
• MapReduce: A programming framework to support processing large data sets on distributed nodes to generate aggregated results
• Hive: data warehouse software that facilitates querying and managing large datasets residing in distributed storage
• MATLAB: A numerical computing environment and a high-performance fourth-generation programming language for technical computing

2. RPL OFFERINGS
Big Data is a crucial differentiator that allows organizations outperform their peers. However, without the right technological partners your Big Data environment can turn to be counter-productive. RPL understands the fundamentals of Big Data and helps you transform your business and your perspective. This simple shift can transform your perspective, changing Big Data from a technological problem to a business solution.

RPL uses Big Data to change your data into facts. RPL combines online and offline customer data bringing new business insights into your strategy. With RPL, masses data converge into data silos that provide cross-channel insight and improve customer-orientation with deeper personalization. Big Data would enhance productivity and create significant value, by reducing waste and improving organizational data comprehension. Forward thinking leaders across sectors are using Big Data's precise predictions to optimize marketing and organizational strategies.

Understanding the data needs of your business is critical for the success of your Big Data environment. Big Data implemented fittingly provides you with what you want, even before you know it. RPL's solutions detect previously unrecognized patterns and trends in your data. This provides precise predictions that provide you with an insight to alter your strategy. Our solutions transform masses of data in easily comprehendible relationships, analyses, and predictions. Our solutions enable innovative approaches that dynamically change as problems do.

Big Data revolution is in its early stages, and most of its potential is still undiscovered. It has set the industry on a path of change and innovation. Organizations committed to innovation can reap benefits of Big Data, and make it their competitive advantage. You can determine what you can accomplish through Big Data.

3. INDUSTRY FOCUS
The following sections provide the possible benefits of implementing Big Data for each industry. The potential benefits, however, are innumerable.

Retail
Customers care about customer experience. Big Data provides innumerable insights into customer behavior that allows organizations better service customers. It allows organizations understand customer trends, and create experiences that are tailored to individual users.

The improved insights and predictions allow organizations better manage their merchandise, supply management, and demand planning.

Retailers can review customer loyalty card data to identify sales trends, optimize their product mix, and develop special offers. This improves customer satisfaction while pushing up profitability.

Automotive
Automotive manufacturers generate huge volumes of data every year for regulatory, warranty, and recall control. Big Data help them by predicting early recalls and improving recall efforts. Big Data helps organizations detect defects earlier in the cycle, thus greatly decreasing defect management effort and increasing customer safety.

Insurance Insurance industry leaders are looking at newer ways to maintain competitive advantage and sustainable profitability. The challenges are rising because of the increasing customer expectations, market diversity, and changing distribution mix of the insurance industry. Big Data can help insurance companies differentiate themselves from their peers by personalizing customer experience, quickly evaluating customer risk, and efficiently detecting frauds.

Big Data allows insurance companies to analyze social media, market surveys, sales demographics and other data pools to better identify customer behavior and expectation.

Predictive analysis of Big Data can help insurance companies identify the right product for the right customer at the right time.

Telecommunications Telecommunications companies across the globe are seeing an unprecedented increase in the volume of data generated per customer.

By successfully harnessing Big Data to turn these huge volumes of data into actionable intelligence, organizations can drive consistent high-quality customer experience.

RPL can help you design smarter campaigns that are based on customer profile and customer usage. This results in higher acceptance rates and improved revenue, apart from increasing customer loyalty and satisfaction.

RPL helps your derive mission-critical network analytics. This can provide deep insight into network bottlenecks, capacity requirements, and network viability. Big Data can also allow you to provide contextual location-based services that improve customer reach and customer loyalty.

Healthcare
We understand that healthcare industry's Big Data is not about volumes, but is about complexity, diversity, and timeliness. Big Data can immensely help healthcare research as well as healthcare provider organizations. Standard and consolidated data can help healthcare organizations understand effective treatments for specific conditions, identify patterns in disease management, and gain other important insights while reducing cost.

Banking and Finance
Banking and Finance organizations can immensely benefit from Big Data, because they are data-driven. Most of the data is not analyzed because of the lack of appropriate tools.

Banks can use predictive analysis to design customer-focused offers for customers, and provide them to customers at the next communication point. Big Data can be used to derive a 360 degree view of the customer's financial preferences. The combination of cross selling, one-to-one targeting and personalized offers drives revenues while improving customer loyalty. Big Data can reduce business risk by analyzing credit risk, fraud risk, and non-compliance risk.

4. BENEFITS
The potential of Big Data is in its ability to solve business problems and provide new business opportunities. We help you get the most from your Big Data investments, by aligning all business capabilities, strategies, people, process, and technologies. We help you focus only on that data needed to deliver specific business outcomes.

The recent studies have proven a minimum increase of 15% in productivity for companies implementing Big Data. In a survey conducted recently, more than 50% said that Big Data helped them meet consumer demand, while 70% experience improved operational efficiencies.