Big Data Applications

While the field of Big Data is continuing to evolve in a humongous manner, it definitely won’t be fading away anytime soon, or anytime at all!

This might sound like a big declaration, but we know that data has always been a part of the modern economy. Since the beginning of the industrial revolution, different industries have been using data to inform their decisions and business strategies. By now, the data has increased to a huge extent.

There has been a perpetually expanding interest for big data because of its rapid growth and development and since it is able to cover different sectors of applications. 

 [source : Gartner]

Here we go,

• Banking and Securities

Fraud Detection :

By applying analytics and machine learning, we shall be able to define normal activity based on a customer's history and distinguish it from an unusual behavior indicating fraud.Immediate actions can be taken such as blocking the transaction, which shall improve profitability. 

Customer Segmentation :

Big data enables the banks to group their customers into distinct segments, which are then, defined by data sets that may include customer demographics, daily transactions, interactions with online and telephone customer service systems, and external data, such as the value of their residences. 

Promotions and marketing campaigns are then targeted to customers, according to their segments.

Personalized Marketing :

 It targets customers based on understanding of their individual buying habits.

Risk Management :

Big Data plays a vital role in integrating the banks' requirements into a centralized as well as functional platform. This helps in reducing the chances of losing data, or ignoring fraud.

Example : The Securities Exchange Commission(SEC)

• Communications, Media and Services :

Predicting Audience's Interest : 

These days traditional content delivery has been replaced with services like pay per view, on demand, live streaming and much more. In the process of content delivery across these forms, broadcasters also collect a vast amount of user data which can give an in-depth understanding of behavior and preferences.

Insights into Customer Churn :

Statistically, around 30% customers share their reviews on social media. Customers can have a personalised and enriched experience. Big data is used for understanding real-time customer sentiment, increasing marketing effectiveness and ratings and viewership.

Optimized scheduling of Media Streaming :

Through business models like on-demand and scheduled viewing , customer behaviour analytics can be mastered. Big Data Analytics help identify the exact content which customers would want to engage with on a schedule basis.This way they can increase the revenues and maximize the number of customers.

This also lead to Content Monetization which helps the media owners to know their customers' media interests. 

Effective Advertisement Targeting : 

Big Data takes the guesswork out of programmatic advertising,which has been done in random manner hoping the customers shall like what is being shown to them. It helps advertisers and businesses pinpoint the exact preferences of customers. It also gives a better understanding about what type of content viewers watch at what time and duration resulting in improved efficiency of ad-targeting.

Example : Amazon Prime, Netflix, Youtube, Spotify

• Education 

Improving Student's result :

Currently the only measurement of a student's performance is their answers in the examinations. But, as a student gives more examinations in his/her lifetime, putting together the data of all his/her performances can help analyze and gain a better understanding of an individual student's behavior. This will help create an optimal learning for the students.

Customize Programs :

With the help of ‘blended learning’ – a combination of online and offline learning, customization of programmes can be done for each student.

This gives students the opportunity to follow classes that they are interested in and also work at their own pace, while still having the possibility for offline guidance by their professors. We can see MOOCs attracts lot of students.

Reduce Dropouts :

If student's results improve, the number of drop outs shall also decrease. Predictive analytics on all data of education institutions shall give them future student outcomes. These predictions will help run scenario analysis on a course program before it is introduced into the curriculum; minimizing the need for trial-and-error. 

• Government :

1. In public service, applications like energy exploration, financial market analysis, fraud detection, health related research and environmental protection are there. 
2. The Department of Homeland and security uses big data to analyze from different government agencies to protect the country.
3. Big data is being used in the analysis of large amounts of social disability claims, made to the Social Security Administration (SSA). The analytics are used to process medical information rapidly and efficiently for faster decision making and to detect suspicious or fraudulent claims.
4. Food and Drug Administration (FDA) is using big data to detect and study patterns of food-related illnesses and diseases. 

• Healthcare Providers 

1. Electronic Health Records (EHRs) are the most widespread application of big data. Each patient have their demographics, medical history, tests, results to the laboratory tests, food habits, allergies etc. These records need no paperwork and there's no danger of data replication. Each record contains a modifiable file which can be changed by the doctors from time to time. These records are shared via a secure information system to public and private healthcare providers.
2. In order to avoid expensive in-house treatments and to keep patients away from the hospitals, personal analytics devices have come up. These are wearable which collect the patient's health data continuously and send to cloud. For example, an individual's blood pressure increased suddenly. This shall send an alert to the doctor to reach to the patient and to administer the patient lower the BP.
3. Another interesting example of the use of big data in healthcare is the Cancer Moonshot program brought up by President Obama. Medical researchers can use large amounts of data on treatment plans and recovery rates of cancer patients in order to find trends and treatments that have the highest rates of success in the real world.

• Insurance

Risk Assessments :

These assessments are used mostly by automobile, home and health insurance companies. Many insurers benefit from telematics (in-vehicle telecommunication devices) IoT devices and wearables (Fitbit, Apple Watch etc.) to track their customers in order to predict and calculate risks.

Activity trackers can monitor users’ behaviors and habits and provide ongoing assessments of their activity levels. Many insurers are also offering services and discounts based on the use of these devices.

Customer Experience :

Insurers provide the customers with personalized offers based on their preferences and behavioral data as well as offer them innovative services that streamline the purchase process. 

Automation :

With the rise of big data technologies, complicated skills like loan underwriting, reconciliation, property assessment, claims verification, receiving customer insights, customer interactions (chat-bots) and fraud detection were simplified.

• Manufacturing and Natural Resources

1. Helps in improving manufacturing process.
2. Customized product designing , i.e., understanding delivering of goods in a timely and profitable manner.
3. Better and cheaper quality assurance.
4. Big Data Analytics helps in reducing risk in Supply chain management by predicting the probabilities of delays and identifying backup suppliers.
5. Big Data also allows for predictive modeling of support decision of the natural resources that have been utilized taking into account the geological data. 

• Retail and Wholesale trade

1. Predicting Customer's Interests.
2. Demand-driven Forecasting which gives an insight of spending habits, demographics of a customer.
3. Optimized Pricing : It helps retailers find identify patterns which lead to higher profits. For instance, a product that does not sell well by itself, sometimes get overall sales increase when paired with some other complementary product.
4. Goods Promotion will invite new customers and in turn increase sales of the goods. This is identified by data analytics. It also helps retailers know why their sales is getting weak and to try different methods of promotion which shall attract the new customers and help retain their old customers.

• Transportation 

Railways :

Railways management systems have involved companies which use the data sources like maintenance logs, GPS units to record speed, distance between trains and knowing about the weather conditions to ensure commuters safety. The data provided by all these systems helps railway authorities in quick decision- making for enhanced and safe railway journey.

Road Safety :

Road accidents are unpredictable and the analysis of the factors which are responsible for road accidents is important to prevent the same instances in the future. Thus, big data analysis has been used widely to overcome the number of accidents all along maintaining the infrastructure.

Traffic Management : 

To curb the heavy traffic, many cities have traffic systems that have integrated analytics to identify traffic problems quickly.