Application of Big Data
The primary goal of big data analytics is to help companies make more informed business decisions by enabling data scientists, predictive modelers, and other analytics professionals to analyze large volumes of transactional data, as well as other forms of data that may be untapped by more conventional Business Intelligence(BI) programs.
That could include web server logs and Internet clickstream data, social media content and social network activity reports, text from customer emails and survey responses, mobile phone call detail records, and machine data captured by sensors and connected to the Internet of Things.
Big data has found many applications in various today. The major fields where big data is being are as follows :
Government
Big data analytics has proven to be very useful in the government sector. Big data analysis played a large role in Barack Obama’s successful 2012 re-election campaign. Also most recently, Big data analysis was majorly responsible for the BJP and its allies to win a highly successful Indian General Election 2014. The Indian Government utilizes numerous techniques to ascertain how the Indian electorate is responding to government action, as well as ideas for policy augmentation.
Social Media Analytics
The advent of social media has led to an outburst of big data. Various solutions have been built in order to analyze social media activity like IBM’s Cognos Consumer Insights, a point solution running on IBM’s BigInsights Big Data platform, which can make sense of the chatter. Social media can provide valuable real-time insights into how the market is responding to products and campaigns. With the help of these insights, the companies can adjust their pricing, promotion, and campaign placements accordingly. Before utilizing the big data there needs to be some preprocessing to be done on the big data in order to derive some intelligent and valuable results. Thus to know the consumer mindset the application of intelligent decisions derived from big data is necessary.
Technology
The technological applications of big data comprise of the following companies which deal with huge amounts of data every day and put them to use for business decisions as well.
For example, eBay.com uses two data warehouses at 7.5 peta bytes and 40PB as well as a 40PB Hadoop cluster for search, consumer recommendations, and merchandising Inside eBay’s 90PB data warehouse.
Amazon.com handles millions of backend operations every day as well as queries from more than half a million third-party sellers. The core technology that keeps Amazon running is Linux-based and as of 2005, they had the world’s three largest Linux databases, with capacities of 7.8 TB, 18.5 TB, and 24.7 TB.
Facebook handles 50 billion photos from its user base. Windermere Real Estate uses anonymousGPSsignals from nearly 100 million drivers to help new home buyers determine their typical drive times to and from work throughout various times of the day.
Fraud Detection
For businesses whose operations involve any type of claims or transaction processing, fraud detection is one of the most compelling Big Data application examples. Historically, fraud detection on the fly has proven an elusive goal. In most cases, fraud is discovered long after the fact, at which point the damage has been done and all that’s left is to minimize the harm and adjust policies to prevent it from happening again.
Big Data platforms that can analyze claims and transactions in real-time, identifying large-scale patterns across many transactions or detecting anomalous behavior from an individual user, can change the fraud detection game.
Call Center Analytics
Now we turn to the customer-facing Big Data application examples, of which call center analytics are particularly powerful. What’s going on in a customer’s call center is often a great barometer and influencer of market sentiment, but without a Big Data solution, much of the insight that a call center can provide will be overlooked or discovered too late.
Big Data solutions can help identify recurring problems or customer and staff behavior patterns on the fly not only by making sense of time/quality resolution metrics but also by capturing and processing call content itself.
Banking
The use of customer data invariably raises privacy issues. By uncovering hidden connections between seemingly unrelated pieces of data, big data analytics could potentially reveal sensitive personal information. Research indicates that 62% of bankers are cautious in their use of big data due to privacy issues. Further, outsourcing of data analysis activities or distribution of customer data across departments for the generation of richer insights also amplifies security risks.
Such as customer’s earnings, savings, mortgages, and insurance policies ended up in the wrong hands. Such incidents reinforce concerns about data privacy and discourage customers from sharing personal information in exchange for customized offers.
Agriculture
A biotechnology firm uses sensor data to optimize crop efficiency. It plants test crops and runs simulations to measure how plants react to various changes in condition. Its data environment constantly adjusts to changes in the attributes of various data it collects, including temperature, water levels, soil composition, growth, output, and gene sequencing of each plant in the test bed. These simulations allow it to discover the optimal environmental conditions for specific gene types.
Marketing
Marketers have begun to use facial recognition software to learn how well their advertising succeeds or fails at stimulating interest in their products. A recent study published in the Harvard Business Review looked at what kinds of advertisements compelled viewers to continue watching and what turned viewers off. Among their tools was “a system that analyses facial expressions to reveal what viewers are feeling.”
The research was designed to discover what kinds of promotions induced watchers to share the ads with their social network, helping marketers create ads most likely to “go viral” and improve sales.
Smart Phones
Perhaps more impressive, people now carry facial recognition technology in their pockets. Users of I-Phone and Android smartphones have applications at their fingertips that use facial recognition technology for various tasks. For example, Android users with the Remember app can snap a photo of someone, and then bring up stored information about that person based on their image when their own memory lets them down a potential boon for salespeople.
Telecom
Nowadays big data is used in different fields. In telecom also it plays a very good role. Operators face an uphill challenge when they need to deliver new, compelling, revenue-generating services without overloading their networks and keeping their running costs under control.
The market demands a new set of data management and analysis capabilities that can help service providers make accurate decisions by taking into account customers, network context, and other critical aspects of their businesses. Most of these decisions must be made in real time, placing additional pressure on the operators.
Real-time predictive analytics can help leverage the data that resides in their multitude of systems, make it immediately accessible, and help correlate that data to generate insight that can help them drive their business forward.
Healthcare
Traditionally, the healthcare industry has lagged behind other industries in the use of big data, part of the problem stems from resistance to change providers are accustomed to making treatment decisions independently, using their own clinical judgment, rather than relying on protocols based on big data. Other obstacles are more structural in nature. This is one of the best places to set an example for Big Data Applications. Even within a single hospital, payor, or pharmaceutical company, important information often remains siloed within one group or department because organizations lack procedures for integrating data and communicating findings.
Healthcare stakeholders now have access to promising new threads of knowledge. This information is a form of “big data,” so called not only for its sheer volume but for its complexity, diversity, and timelines.
Pharmaceutical industry experts, payers, and providers are now beginning to analyze big data to obtain insights. Recent technological advances in the industry have improved their ability to work with such data, even though the files are enormous and often have different database structures and technical characteristics.