Uses of Big Data
1. Tracking Customer Spending Habits and Shopping Behavior
In large retail stores (such as Amazon, Walmart, and Big Bazar), the management team must keep data on customer spending habits (such as which product the customer spent, which brand they wish to spend in, and how frequently they spent), shopping behaviour, and the customer's favourite product (so that they can keep those products in the store).
Which product is being searched/sold the most, and is the production/collection rate of that product fixed depending on that data?
Banking sector uses its customers' spending behavior-related data to present an offer to a specific consumer to buy his or her desired product using the bank's credit or debit card with a discount or reward. They may send the right offer to the right individual at the right time this way.
2. Recommendation
Big retail stores make recommendations to customers based on their spending habits and buying habits. Product recommendations are made by e-commerce sites such as Amazon, Walmart, and Flipkart. They track what product a customer is looking for and then recommend that product to that customer based on that data.
Assume a customer searches for a bed cover on Amazon. As a result, Amazon learned that a client could be interested in purchasing a bed cover. The next time the customer visits a Google page, he or she will see advertisements for various bed coverings. As a result, the proper product can be advertised to the right buyer.
YouTube also suggests videos based on the user's prior liked and viewed video types. During video playback, relevant advertisements are provided based on the content of the video the user is watching. As an example, if someone is watching a Big Data tutorial video, an advertisement for another Big Data course will be shown throughout that video.
3. Smart Traffic System
Data on the condition of traffic on various roads is collected using a camera mounted beside the road, at city entry and exit points, and a GPS device installed in the car (Ola, Uber cab, etc.). All such data is analysed, and jam-free or less jam-prone routes that take less time are advised. Big data analysis can be used to build a smart traffic system in the city in this manner. Another advantage is that fuel usage can be lowered.
4. Secure Air Traffic System
Sensors are present at various points of flight (such as propellers). These sensors record data such as flight speed, moisture, temperature, and other environmental conditions. Based on such data processing, an environmental parameter is set up and changed during flight.
By analysing flight machine-generated data, it is possible to anticipate how long the machine will be able to run flawlessly before it needs to be replaced/repaired.
5. Self-Driving Automobile
Big data analysis allows a car to drive itself without the need for human intervention. A sensor is put in various locations on the automobile camera to gather data such as the size of the surrounding car, obstacles, distance from those, and so on. These data are analysed, and then numerous calculations such as how many angles to rotate, what speed to use, when to stop, and so on are performed. These computations aid in the automatic execution of actions.
6. Virtual Personal Assistant Tool
Big data analysis enables virtual personal assistant tools (such as Siri in Apple devices, Cortana in Windows, and Google Assistant in Android) to deliver answers to a variety of questions posed by users. This programme tracks the user's location, local time, season, and other data connected to the query asked, among other things. It delivers a response after analysing all such data.
As an example, assume a user asks, "Do I need to bring an umbrella?" The tool collects data such as the user's location, season, and weather condition at that area, then analyses this data to determine if there is a probability of rain, and then provides the answer.
7. IoT
Machines with IOT sensors are installed by manufacturing companies to collect operational data. By analysing such data, it is possible to anticipate how long a machine will run without issue until it has to be repaired, allowing the company to take action before the machine develops a number of problems or fails completely. As a result, the cost of replacing the entire machine can be avoided.
Big data is making a huge impact in the realm of healthcare. Data on patient experiences is collected using a big data platform and used by clinicians to provide better care. An IoT gadget can detect a symptom of a potentially fatal disease in the human body and prevent it from providing early treatment.
An IoT sensor put near a patient or a newborn baby constantly monitors various health conditions such as heart rate, blood pressure, and so on. When any parameter exceeds the safe limit, an alarm is transmitted to a doctor, allowing them to take action remotely as soon as possible.
8. Education Sector
Organizations that offer online educational courses use big data to find candidates who are interested in that course. If someone searches for a YouTube tutorial video on a subject, an online or offline course provider organisation on that subject will send an ad about their course to that individual.
9. Energy Sector
Smart electric metres read consumed power every 15 minutes and communicate this data to a server, where the data is analysed and it can be calculated when the power load is lower throughout the city. By using this technique, a manufacturing unit or a housekeeping is advised to drive their heavy machinery at night when the power load is low in order to save money on their electricity bill.
10. Media and Entertainment Sector
Companies that provide media and entertainment services such as Netflix, Amazon Prime, and Spotify analyse data received from their subscribers. Data like as what type of video or music customers are watching or listening to the most, how long visitors stay on the site, and so on are collected and analysed in order to develop the next business strategy.
Why is Big Data Important?
The importance of big data is determined not by how much data a corporation possesses, but by how that data is used. Every organisation uses data in its own unique way; the more efficiently a firm uses its data, the greater its potential for growth. The company can analyse data from any source to uncover answers that will allow it to:
- Cost Savings: Some Big Data tools, such as Hadoop and Cloud-Based Analytics, can provide cost savings to businesses when vast amounts of data are stored, and these tools can also assist in identifying more efficient methods of doing business.
- Time Savings: Due to the rapid speed of tools like Hadoop and in-memory analytics, businesses may easily uncover new sources of data, allowing them to analyse data promptly and make quick decisions based on the learning.
- Understand Market Dynamics: Big data analysis can help you gain a better grasp of current market situations. For example, by studying customers' purchasing habits, a corporation can determine which products sell the most and manufacture products in response to this trend. It will be able to get ahead of its competition as a result of this.
- Maintain Online Reputation: Sentiment analysis is possible with big data techniques. As a result, you can acquire information on who is saying what about your company. If you want to monitor and improve your company's internet visibility, big data technologies can assist you.
- Using Big Data Analytics to Improve Client Acquisition and Retention: The customer is the most crucial asset on which any organisation relies. There is no such thing as a successful firm that does not first have a stable consumer base. Even with a customer base, a company cannot afford to ignore the fierce competition it faces. If a company takes too long to figure out what its customers want, it is all too easy to start selling low-quality goods. In the end, clientele will be lost, which will have a negative impact on the overall success of the business. Businesses can use big data to observe numerous customer-related patterns and trends. It is critical to observe client behaviour in order to elicit loyalty.
- Using Big Data Analytics to Help Advertisers and Provide Marketing Insights: Big data analytics has the potential to transform all corporate operations. This involves being able to meet client expectations, altering the company's product range, and, of course, ensuring that marketing initiatives are effective.
- Big Data Analytics as a Driver of Innovations and Product Development: Another significant benefit of big data is its capacity to assist businesses in innovating and redeveloping their goods.
Data Vendors
Big data vendors offer many types of data to their customers, which are generally determined by the business and the unique instruments used in that industry. As stock exchange indices are crucial to hundreds of marketplaces, financial data is supplied to organisations across industries. Commodity information relevant to an industry may be marketed to important industry players.
In general, these information brokers take raw data from a particular source and process it through their own technologies to produce more valuable information. Data providers sell modified data to industry actors who utilise the information provided to make judgments. Each data vendor must decide how much data to collect and then disperse to its customer base.
Financial data vendors compile data on stock exchange transactions, regulatory filings, and a wide range of financial instruments. These data objects are kept on the vendor's computers. The data is communicated to the client base after it has been altered and placed in the format that the company offers to its client base. The majority of data gathering and transmission occurs during off-hours, when the client base uses the information for the next day's activities.
Financial data is sold based on the client's intended use of the data. Data providers differentiate their data offerings based on the mode of distribution, frequency of transfer, format, or standardisation of data. Each customer must decide which vendor supplies data in the format that best suits the client's needs. Customer care provided by each organisation can also affect the customer base in choosing who will acquire the data.
Data on the amount of visits to a single site or the top searches performed on specific search engines is of importance to online marketers. Data providers collect this information and manipulate it into numbers that are useful to internet sales and marketing firms. Some large market participants amass so much data on their own that they can sell this aggregate data to other corporations without violating any specific privacy policies. Using these services, consumer product companies obtain access to more detailed information about their customer base.
Big Data Vendors
The big data boom has given rise to a slew of suppliers, each advocating their own distinct approach to fulfilling today's enterprises' rising data demands. As a result, companies looking for a big data solution have a rather lengthy list of big data providers from which to choose.
Choosing the correct provider is both a commercial and a technical issue. And there are basic distinctions in how different vendors strive to deliver on the big data promises of "actionable insights" and "competitive advantage" that business and IT leaders must be aware of. Here are a few examples of the various types of big data suppliers.
On-Premise Vendors
On-premise vendors provide a physical Hadoop platform made up of a large number of servers situated in a large onsite company location. When you consider the original hardware and facility costs, software license and support charges, the significant amount of electricity required, and the expense of putting an on-site IT crew on the payroll to guarantee that everything on the hardware and software side runs well, this can be rather costly.
The key advantages of on-premise Hadoop are that enterprises have complete control over all systems and data. Internally, all business data is kept, processed, and safeguarded behind the corporate firewall. Having a dedicated on-site IT personnel to provide maintenance and support—in an environment where IT and business leaders can collaborate closely to ensure that the tech side is always aligned with the organization's business objectives—is also a significant benefit. While the initial expenditure may be significant, the long-term benefits of an on-premise solution can more than offset the price.
Cloud Vendors
Cloud vendors, also known as Big Data as a Service (BDaaS) providers, provide a more streamlined model to enterprises than their on-premise counterparts. Rather than investing extensively in costly onsite hardware and support, enterprises can contract for quick access to the cloud vendor's own fully scalable storage and analytics platform, paying only for what they need. Installation and licence fees are reduced because cloud vendor storage and analytics services are available online in an essentially "plug-and-play" style.
Many cloud vendors allow organisations to pay a monthly charge for services or pay as they go. As an organization's data demands grow, the cloud platform's comprehensive scalability enables for on-demand access to unlimited storage capacity. Thousands of virtual servers may literally be set up in the cloud in a matter of minutes. And the organisation only pays for the space and processing resources that it actually utilises. Cloud suppliers are an appealing choice for both large and small organisations seeking a cost-effective approach to harness their data for competitive advantage without having to perform all of the hard lifting themselves.
Open-Source Vendors
Open-source providers, such as Hortonworks, package open-source Hadoop components such as HDFS, Hadoop Common, Hadoop MapReduce, and Hadoop Yarn into a single fully supported big data solution. Because this solution is supported by an open-source community of software developers, organisations that contract with open-source vendors will benefit from ongoing software enhancements, refinements, and innovations. An open-source solution, on the other hand, is far from "out-of-the-box," as it requires an on-premise data warehouse, the merits and cons of which have already been covered.
Proprietary Vendors
While open-source Hadoop solutions are widely regarded as dependable and reliable for storing, managing, and analysing massive volumes of structured and unstructured data—particularly where time to insight is not a major factor—it takes the addition of differentiated, proprietary software tools to generate the kinds of actionable real-time insights that lead to competitive advantage.
Proprietary suppliers provide solutions that are mostly based on the Apache Hadoop open-source distribution but have varying degrees of proprietary customisation. Cloudera, for example, the first vendor to build and sell Apache-based software, also offers a proprietary Cloudera Management Suite to streamline the installation process and shorten deployment time. Another vendor that has distinguished itself is Databricks, which provides unique stand-alone support for the Spark processing engine. To elevate Hadoop to new levels of performance, proprietary suppliers, many of which are cloud-based, pick up where open-source Hadoop leaves off.
Public Cloud Vendors
Public Cloud Vendors, which include Amazon Elastic Compute Cloud (EC2), Google Cloud Engine, and Windows Azure Services Platform, are online services that make their applications and storage resources available to the general public via the Internet. These scalable services are either provided for free or on a pay-per-usage basis. Public clouds provide services to many businesses and are best suited for organisations with predictable computing demands that do not require direct control over the environment. Many vendors, including Qubole, Databricks, and EMR, provide Hadoop services over the public cloud.
Private Cloud Vendors
Private cloud suppliers provide many of the same benefits as public cloud vendors, but they do so via a proprietary architecture. Private clouds are more expensive since they are dedicated to a particular enterprise. They are, however, better suited to enterprises with mission-critical workloads where data security in a cloud environment is crucial.
