Nowadays, a large number of social applications are in the works, which translates directly into a huge data explosion on a daily basis. As millions of users connect daily, information is shared every time people use a social media platform or any other website, this is where the term Big Data becomes known.
So what is ‘’Big data’’? And why do we use it to begin with?
What does » Big Data » mean exactly?
Everything kicks off with the massive volume of data created since the beginning of the digital age. This is largely the result of widespread use of computers, the Internet, as well as technologies designed to catch the data streams of the world we live in. Although data alone is nothing new, it is not considered a revolution in itself.
Long before the appearance of computers and databases, there was paper transaction records, customer files and archival files and all of these represent ‘’data’’. Now computers, especially spreadsheets and databases, have provided us with a way to store and organize data on a large scale, making it easily attainable.
Next thing we knew, information became available literally at a click of the mouse.
In today’s world, practically every action we take leaves a digital trace. We are generating data whenever we connect to the Internet, when we carry our GPS-equipped smartphones, interact with our friends through social media or chat applications, and when we shop. Some might say that we leave a digital footprint with everything we do through digital action, which is pretty much everything. On top of that, there is a rapid increase in the amount of data generated by machines. These data are generated and shared when our « smart » home devices communicate with each other or with their home servers. All over the world, industrial machines in factories and manufacturing plants are becoming progressively more equipped with sensors that gather and transmit data.
The term « Big Data » refers to the collection of all this data as well as our ability to use it to our best advantage in a large variety of areas, including business.
Big Data’s main concept is this: The more one knows about a thing or situation, the more you can get new information and predict what will take place in the future. As we compare more data points, connections that were previously hidden begin to appear, and these connections allow us to learn and make smarter decisions. Typically, the most effective way to do this is through a process of building models, which is based on the data we can gather, and then running simulations, adjusting the value of the data points each time and monitoring the impact on our results. This process is automated – using today’s advanced analytical technology, we can run millions of these simulations, adjusting every possible variable until we find a model – or a snapshot – that helps solve the problem we’re working on.
Why do we need to use ‘’Big Data’’?
The significance of big data do not depend on the quantity of data available to a company, but rather on how the company uses the data gathered. Each company uses data uniquely; the more effectively a company uses its data, the more potential it has for growth.
We list below a number of reasons why companies need to use big data:Cost Saving: Several tools of Big Data like Hadoop and Cloud-Based Analytics will bring cost advantages to business as vast amounts of data are to be stored and such tools can also be helpful in identifying the most efficient ways of doing business.
Time reduction: Thanks to the high speed of tools such as Hadoop and cloud-based analysis, companies can easily identify new data sources, allowing them to immediately evaluate the data and immediately make quick decisions based on the lessons learned.
Better understanding of market conditions: Through big data analysis, you can gain a better understanding of current market conditions. For instance, when a company analyzes customer buying behavior, they can identify the products that sell the most and manufacture products based on that trend. As a result, the company will gain a competitive edge over its competitors.
Monitor online reputation: Big data tools can perform sentiment analysis. This allows for insight into who’s saying what about your company. Whether you want to track or enhance your company’s online presence, big data tools will help you in this area.
The use of leading data analysis techniques to optimize customer acquisition and retention: As a business, your customers are the number one asset on which your company depends. No company can be successful unless it has first established a solid customer base. Nevertheless, even with a strong customer base, we cannot afford to neglect the intense competition we face. Once a company takes a long time to learn what customers are looking for, it is very easy to start offering poor quality products. Eventually, this leads to a loss of customers, thus having an overall negative effect on the company’s success. Employing big data provides companies with the ability to monitor a variety of customer patterns and trends. Keeping an eye on customer behavior is essential for triggering loyalty.
The use of big data to fix advertising issues and provide marketing input: Big data can help transform the way all business operations are conducted. Among these is the responsiveness to customer expectations, the capacity to modify the company’s product range and, last but not least, and the ability to ensure that marketing campaigns have been effective.
Big Data is a Powerhouse for Innovation and Product Development: One other huge advantage of big data is the opportunity it provides companies in innovating and developing anew their products.
What are Big Data’s main technologies?
According to Gartner, the definition of Big Data “Big data is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”
In fact, Big Data Technologies are the software used to implement data mining, data storage, data sharing and data visualization. The overall term encompasses the data, the data framework, including the necessary tools and techniques involved in studying and transforming the data.
Big data technologies have two categories:Operational ‘’Big Data’’ Technologies:
This shows just how much data is being generated on a daily basis, whether it’s online transactions, social media, or any other type of data originating from a specific company for analysis using software based on big data technologies. It is raw data that feeds big data technologies for analytics. Examples of the few cases describing operational big data technologies cover the peculiarities of a multinational corporation’s executives, online shopping and commerce at Amazon, Flipkart, Walmart, etc., online booking of movie tickets, flights, railroad tickets, and more.
- Analytical ‘’Big Data’’ Technologies:
We are talking about an advanced adaptation of large-scale data technologies, which is somewhat complex when compared to large-scale operational data. This is where the real investigation of big data that is critical to business decisions occurs. Examples in this area include stock marketing, weather forecasting, time series analysis, and medical and health records.
At this point, as we move forward, we will be discussing the latest technologies that are impacting the market and the IT industries in the last few years:
Artificial Intelligence:
Artificial intelligence represents a field of computer science that focuses on the design of intelligent machines that can perform a variety of tasks that typically require human intelligence. Ranging from SIRI to self-driving cars, AI expands very rapidly. Being an interdisciplinary branch of science, many approaches such as enhanced machine learning and deep learning are being used to effect remarkable change in almost all areas of technology.
NoSQL Database :
NoSQL integrates a huge set of distinct database technologies that are developing to design modern applications. It represents a non-SQL or non-relational database that provides a method for data accumulation and retrieval. Such technologies are implemented within real-time web applications and important data analysis.
These databases store unstructured data and provide faster performance and flexibility as well as the ability to handle a variety of data types on a broad scope. Typical examples in this context are MongoDB, Redis, and Cassandra.
R programming:
R stands for a programming language and an open-source project. It is a free software that is widely used for statistical computation, visualization, unified development environments like Eclipse and Visual Studio support communication.
According to experts, it has become the most widely known language worldwide. Employed by data miners and statisticians, the language is also used extensively in the design of statistical software, mostly in the field of data analysis.
In-memory Database:
The in-memory database (IMDB) is stored in the main memory of the computer (RAM) and controlled by the in-memory database management system. In prior, conventional databases are stored on disk drives.
Data Lakes:
Data Lakes refers to a global repository for storing all data formats in terms of structured and unstructured data at any scale. Through the process of data accumulation, data can be stored as it is, with no need to transform it into structured data and without having to perform many types of data analysis, from dash-boarding and data visualization to bulk data transformation, real-time analysis, and machine learning for better business interferences.
Blockchain:
Blockchain is the allocated database technology which transports Bitcoin digital currency in a safe and secure way, once written, they are never deleted or modified afterwards. This is an ecosystem with high-level of security and a great option for a variety of important data applications in banking, finance, insurance, healthcare, retail, and more.
Even if this technology is still under development, many merchants of various organizations like AWS, IBM, Microsoft and even startups have tried multiple experiments to introduce the possible solutions in building blockchain technology.
Hadoop Ecosystem:
The Hadoop ecosystem features a platform designed specifically to assist in solving problems related to large data sets. It incorporates a wide variety of components and services, including ingest, storage, analysis, and maintenance within it. Most of the services in the Hadoop ecosystem complement its various components, which include HDFS, YARN, MapReduce and Common.
Data continues at an unprecedented rate to change our world and the daily lives we lead. Imagine if Big Data is able do all these things today, picture what it will be able to do tomorrow. As the volume of data that we have will only increase, analytical technology is only going to get more advanced.
This means that for businesses, being able to exploit Big Data is going to be more and more critical in the years to come. Firms that consider data to be a key asset will outlast and flourish. Anyone not taking advantage of this revolution is in danger of being left behind.