The Need for Big Data
Wherever we are, we see people browsing, engaging, shopping and transacting business online. Customers feel empowered to shop online due to a large number of available choices and its time-saving features. However, as competition in the online space increase, customers can give up on loyalty with a single click if the quality of service is not satisfactory.
In such a fast-moving world wherein companies are always at the risk of losing their potential consumer base, companies need to optimize operations, improve financial management and reduce risks. Business survival is becoming as much about consumer retention as it is about growth, and for companies to survive, they need to know the psychology of their customers which is hidden in their accumulated raw data.
These large sets of unstructured raw data (Big Data) need to be collected, organized and examined to get better business insights for making informed decisions. For example, analyzing visitor clicks on a website helps a company understand site-navigation behavior, the paths people usually take to buying services, paths that led to abandonment and more. This customer data, if analyzed, can give clear insight for companies to make better decisions in real time to curb losses.
Analyzing data from Facebook, Instagram, LinkedIn and Twitter give an insight into what people like and dislike. This, in turn, leads to efficient operations, smarter business moves, identification of new sales opportunities, enhanced customer service, better operational efficiency and competitive edge over rivals and subsequently happier customers.
Let’s take Lloyds Banking Group as an example for using big data analytics to detect fraud. It used an ‘audio fingerprint’ of every customer call by analyzing over 1300 distinctive call features such as background noise, location, number history etc to highlight any suspicious or unusual activity that might lead to fraud. It used big data analytical tools that relied on voice distortion, caller ID spoofing and other similar frameworks without any need for customers to be asked additional information. This helped Lloyds Banking Group minimize and control fraudulent transactions.
With today’s advances, big data analytics has increasingly been embraced by financial services firms, insurers, retailers, healthcare organizations and other enterprises to get immediate and real-time decisions.
Any business that relies on quick decisions to stay ahead of its competitors is most likely using big data analytics to make its business tick. Below are the top industries that use big data analytics to obtain relevant results.
Big Data in Finance
Banking, financial services, insurance (BFSI), and non-banking financial services are implementing a data-driven approach to grow their businesses and enhance the services they provide to customers. This is done by gathering immense volumes of data assets, monitoring, and evaluating it for personal and security information. This helps finance organizations to track client behavior in real-time to boost performance and profitability. To help banks reduce risks and minimize losses, big data analytics detect attempts at making fraudulent transactions. It also helps banks with queue optimization, process optimization, and incentive optimization.
Big Data in Healthcare
Hospitals possess large data sets of patient records, insurance information, health plans, and other kinds of information that can be difficult to manage. By analyzing large amounts of structured and unstructured information, health care providers give a lifesaving diagnosis or treatment options very fast. Monitoring of the body vitals along with the sensor data collection (blood pressure monitors, pulse Oximeters, glucose monitors) also allows preventive healthcare firms to identify any potential health issue before it gets any worse.
Big Data in Retail Stores
Nowadays, as customers interact with companies through multiple interaction points like social media, mobile, e-commerce sites, and more, it gives a variety of data that needs to be aggregated and analyzed. This yields various insights like determining high-value customers, understanding what motivates them to buy more, what their behavioral patterns are, and what is the best way to reach them. Shoppers now expect retailers to understand exactly what they need. Big data analytics uses customer loyalty programs, buying habits, and seasonal trends to recommend new products and boost profitability.
The Internet of Things (IoT)
IoT is the interconnection through the Internet of computing devices set in everyday objects, enabling them to send and receive data. For example, a system that happens to play your favorite TV series as soon as you enter the room. However, implementing IoT is not an easy task as it is dependent on a lot of different components in the ecosystem. Once businesses have a secure and efficient system to store IoT-related data, they need to analyze it. This is where big data analysts stream in to organize, structure, examine, and model the data for it to become valuable.
In order to use big data, virtual machines work together in concert to process raw unstructured data at a very high speed. Multiple virtual machines work together to make sense of a large pool of data through programming techniques by distributing data across a cluster. This works as a framework for storing and processing data on a large scale. The tech giant Google, for example, has a quantum computer that uses Big Data Analytics. This computer is claimed to be 100 million times faster than any of the tech systems in use today.
As speed and agility become one of the critical factors in solving today’s complex problems in real-time, Big Data will continue to play an important role in many different industries around the world to increase efficiency.