Big data refers to vast, diversified collections of information that are growing at an exponential rate. Big Data is a bigger, more complicated data collection, particularly from new data sources.
Big Data is accumulated on a massive scale, and it is processed and analyzed by many multinational corporations to reveal insights and enhance the business of numerous organizations. These data sets are so large that typical data processing technologies cannot handle them. However, these massive volumes of data may be used to solve business challenges that were impossible to tackle previously.
Examples Of Big Data
- The New York Stock Exchange creates about one terabyte of new trading data daily.
- Certain statistics show that 500+terabytes of new data get added to the databases of Facebook every day. This data is created through photo and video uploads, message exchanges, and so on.
- In 30 minutes of flying time, a single jet engine may produce 10+terabytes of data. With many flights per day, the data generation reaches up to many Petabytes.
History of Big Data
The concept of big data can be new, but the roots of enormous data sets can be tracked to the 1960s and ’70s when the data field began with the first datacentres and the development of the relational database.
Around 2005, people began to realize how much data is generated through Facebook, YouTube, and other online services by its users. The emergence of open-source frameworks like Hadoop was critical for the evolution of big data since they made huge data easier to deal with and less expensive to keep. Since that time, the volume of big data has increased a lot.
Types Of Big Data
There are three types of big data — Structured, Unstructured and Semi-structured.
Structured data consists of information already being managed in databases and spreadsheets by the organization. Structured data is data that can be stored, retrieved, and processed in a specific format.
Over time, computer science expertise has shown considerable success in creating strategies for dealing with Structured data and generating value from it. Structured data owns a dedicated data model. It also has a well-defined structure, follows a regular sequence, and is structured so that a human or a machine can access and use it. Structured data is typically kept in well-defined columns as well as databases.
Database Management Systems are example (DBMS).
Unstructured data is a completely different type that neither has a structure nor follows the formal structural rules of data models. Any data without a proper form or structure is classified as Unstructured data.
It doesn’t even have a consistent format, and it changes frequently. Because Unstructured data is so large, analyzing it and extracting value from it presents several obstacles. Since the data is huge these days, organizations have a lot of available data, but they don’t know how to utilize its value since this data is in an unstructured format. It contains information obtained from social media sources to assist organizations in gathering information on client demands.
Example: audio files, images etc
Semi-structured data can be considered another form of Structured data. Semi-structured data can contain both structured and unstructured forms of data.
Semi-structured data is a structured form that is not defined. It shares certain characteristics with Structured data. However, most of this data lacks a specific structure and does not adhere to the formal structure of data structures such as an RDBMS.
Example: Comma Separated Values(CSV) File, e.g. a table definition in relational DBMS, data represented in an XML file.
Characteristics of Big Data
In the early 2000s, industry analyst Doug Laney popularised the now-common description of big data as the three V’s:
Organizations collect data from various sources, including business transactions, industrial equipment, videos, social media, etc. Volume refers to the sheer size of the data in the computing world. Previously, storing it would have been a challenge — we are now utilizing distributed systems to store data in several places linked by a software framework such as Hadoop.
It raises the question about the quantity of data. The size of the data is highly important in defining the value of data. With big data, you must analyze large amounts of unstructured data. This can be unvalued data, such as Facebook alone generating billions of messages, 4.5 billion “like” button clicks, and over 350 million new articles submitted daily.
Velocity refers to the processing speed. It answers the question of at what speed the data is processed. With the advancement of the Internet of Things(IoT), data is flooding into organizations at an unprecedented rate and must be processed quickly.
The speed of data at which it is generated and processed to meet the demands evaluates the real potential of the data. It concerns the rate at which data flows in from various sources such as business processes, application logs, networks, social media sites, sensors, mobile devices, and so on.
There is no use in investing so much money only to wind up waiting for the data. As a result, the primary goal of big data is to supply data on demand and at a quicker rate.
Big Data is generated in multiple varieties, i.e. in all formats. The term “variety” alludes to the numerous sources and forms of data, both organized and unstructured.
In contrast to traditional data such as phone numbers and addresses, the newest trend in data is in the form of images, videos, music, stock ticker data, and financial transactions, among other things, with around 80% of the data being fully unstructured. These different kinds of unstructured data pose certain issues for storage, mining and analyzing it.
Unstructured and Semi-structured data types, such as audio, text, and video, require additional preprocessing to gain meaning and support metadata.
Some Additional V’s:
Veracity determines the accuracy of the data concerning the business value we want to extract. It simply refers to the degree of dependability that the data provides. Without veracity, it is not easily possible for an organization to apply its resources to analyze this huge quantity of data.
Because a large portion of the data is unstructured and useless, big data must find a means to filter or translate it, as the data is critical in corporate development. More accuracy and reliability in the data means a greater chance of gaining valuable information.
Value is an important issue that we need to concentrate on. It is not just the amount or the speed of data we store or process. The amount of valuable, reliable and trustworthy data needs to be stored, processed, and analyzed to find insights.
Variability refers to the inconsistency that the data can show at times, thus messing up the process of handling and managing the data effectively and efficiently.
How Big Data Works
Before organizations can put big data to use, they must analyze how information moves across a plethora of locations, sources, systems, owners, and users. There are five critical stages to take control of this vast “data fabric,” which contains both traditional, structured data and unstructured and semi-structured data.
Big data provides fresh insights, which lead to new possibilities and business concepts. There are three key actions for getting started:
Big data brings together data from many different sources and various applications. Traditional data integration mechanisms aren’t much efficient. New methodologies and technologies are required to evaluate massive amounts of data at the scale of terabytes or petabytes.
A big data strategy is a plan to help you monitor and improve how you gather, store, manage, share, and use data within and outside your business. A proper and efficient big data strategy is the secret behind business success.
During integration, one needs to collect and bring in the data, then process it, and make sure it’s formatted in proper order and available in a form that the business analysts can analyze.
Big data requires storage. Computing systems of this generation provide the speed, power and flexibility needed to access huge amounts and types of big data quickly.
Cloud can be used to solve the problem of huge storage requirements. Some important data may be stored in traditional data warehouse premises for easy access and security. Other versatile and low-cost choices for storing and processing large data include cloud systems, data lakes, and Hadoop.
One can store their data in any form they want and bring needed processing requirements and process engines to those data on an on-demand basis. The cloud is quickly gaining popularity since it fulfils your present computation requirements and allows you to utilize resources as needed.
Your investment in big data shows its value when you analyze and take decisions based on that data. Get more clarity with proper visual analysis of your different data sets.
Analyze the data more and more to make discoveries — share your findings with others. Big data feeds today’s advanced analytics endeavours, such as artificial intelligence. Put your data to work.
Businesses must capture the full potential of big data and operate in a data-driven manner to remain competitive, making choices based on the evidence offered by big data rather than gut feeling. Data-driven firms outperform their peers in terms of performance, operational predictability, and profitability.
Benefits of Big Data
- Patients can be constantly monitored in the medical and healthcare industries.
- Businesses can utilize outside intelligence outside the organization while taking big decisions.
- Big data has altered the face of customer-focused businesses and the global economy.
- Big data has enabled predictive analysis, saving organizations from operational risks.
- Access to social data from search engines and websites such as Facebook and Twitter allows businesses to perfect their business plans by increasing customer service.
- Predictive analysis has helped organizations grow business by analyzing customer needs.
- Big data has enabled various multimedia sites, such as YouTube and Instagram, to exchange data.
Applications of Big Data
Generally, almost all organizations have various goals for choosing big data projects. While the main goal for these organizations is to improve customer experience, other goals like cost reduction, better marketing, and making existing processes more efficient
Machine learning is a trending topic right now. And big data is one of the reasons why. We can now teach machines by giving them massive amounts of data which is much easier than programming. Also, it helps in self-learning by machines.
Travel and Tourism
They are one of the biggest users of big data technology. It has helped them to estimate the need for travel facilities in numerous locations, improve the company through dynamic pricing, and many other things.
Netflix and Gamble & Procter use big data to guess customer demand. They create potential models for new products and services by distinguishing important aspects of previous and current products or services and modelling the link between those properties and the commercial success of the offerings.
Spotify collects data from millions of customers globally using Hadoop big data analytics and then analyses the data to provide music suggestions to individual users.
Amazon Prime, which aims to create a wonderful customer experience by combining video, music, and Kindle books in one place, also extensively uses big data.
Financial and Banking
These sectors extensively use big data technology. Big data may help banks analyze client behaviour based on investment patterns, shopping trends, investment motives, and personal or financial histories.
Fraud and Compliance
The security landscape and regulatory standards are always changing. Big data allows you to spot trends in data that may indicate fraud and analyze massive amounts of data to speed up regulatory reporting.
Big data has already created a huge difference in the healthcare sector. Medical experts and health care personnel may now give individualized healthcare services to specific patients thanks to predictive analytics.
Some hospitals are using data obtained from a mobile phone app from millions of patients to help clinicians to practise evidence-based care rather than performing a battery of medical/lab tests on every patient that comes to the hospital.
The Military also uses big data technology a lot. You can consider the amount of data the government generates on its records. In the military, a normal fighter jet plane processes petabytes of data during its flight.
The military uses big data for several different use cases. Big data is analyzed by various government agencies and is used to protect the country.
Big data is used quite significantly in education. Some schools have implemented a learning and management system that records, among other things, how much time a student spends on different sites when they login into the system.
It is also used to measure teachers’ effectiveness to ensure a pleasant experience for students and teachers. Teachers’ performance can be fine-tuned and measured against student numbers, behavioural classification, and other variables.
Big data has been used in the insurance industry to provide customer insights for open and simpler products by understanding and predicting customer behaviour through data gathered from social media, GPS-enabled devices, and CCTV footage. Big data also helps for better customer retention from insurance companies.
Energy and Utilities
Smart metre readers capture data roughly every 15 minutes, as opposed to once a day with traditional metre readers. This data is being used to analyze utility consumption efficiently, allowing for improved customer feedback and better control of utility use.
A significant volume of data is defined as big data, i.e. data that is huge. Big data is a large and fast-expanding subject. While big data is not suitable for all forms of computing, many businesses are turning to it for certain workloads and utilizing it to enhance their existing analysis and management tools.
By correctly implementing systems that deal with big data, organizations can gain real good value from already available data.