Here comes the concept of big data. Before delving any further into this blog, let us have a look at the list of topics that it will cover:
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Big Data Tutorial for Beginners: Introduction
With the evolution of the Internet, the ways how businesses, economies, stock markets, and even governments function and operate have also evolved, big time. It has also changed the way people live. With all of this happening, there has been an observable rise in all the information floating around these days; it’s more than ever before. This outburst of data is relatively new. Before the past couple of years, most of the data was stored on paper, film, or any other analog media; only one-quarter of all the world’s stored information was digital. But with the exponential increase in data, the idea of storing it manually just does not hold appeal anymore. You will learn more about applications and examples of big data in this big data tutorial.
What is Big Data?
The conventional way in which we can define big data is, It is a set of extremely large data so complex and unorganized that it defies the common and easy data management methods that were designed and used up until this rise in data.
Big data sets can’t be processed in traditional database management systems and tools. They don’t fit into a regular database network.
But, how is big data even getting created?
Do we have any role in that?
To find the answers to these questions, let’s move on to the next topic.
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History of Big Data
The first trace of big data was evident way back in 1663. It was during the bubonic plague that John Graunt dealt with overwhelming amounts of information during his study of the disease. He was the first person ever to make use of statistical data analysis. The field of statistics expanded later to data collection and analysis in the early 1800s.
The US Census Bureau estimated that it would take eight years to handle and process the data collected during the census program in 1880, which was the first overwhelming collection of raw data. The Hollerith Tabulating Machine was invented to reduce the calculation work in the subsequent 1890 census.
After that, data evolved at an unprecedented rate throughout the 20th century. There were machines that stored information magnetically. Scanning patterns in messages and computers were also prominent during that time. In 1965, the first data center was built with the aim to store millions of fingerprint sets and tax returns.
Starting with the past, discover the current scenario of big data in this big data tutorial.
Big Data Examples
Here are a few big data examples:
Customer Acquisition and Retention
Everyone knows that customers are the most important asset of any business. However, even with a solid customer base, it is foolish to disregard competition. A business should be aware of what customers are looking for. This is where big data comes in.
Applying big data allows businesses to identify and monitor customer-related trends and patterns. This contributes to gaining loyalty. More data collection allows for more patterns and trends to be identified.
With a proper customer data analytics mechanism in order, critical behavioral insights can be derived to act on and retain the customer base. This is the most basic step to retain customers.
Big data analytics is strongly behind customer retention at Coca-Cola. In 2015, Coca-Cola strengthened its data strategy by building a digital-led loyalty program.
Advertising Solutions and Marketing Insights
Big data analytics has the ability to match customer expectations, improve a company’s product line, optimize marketing campaigns, etc.
The marketing and advertising technology sector has now fully embraced big data in a big way. Through big data, it is possible to make a more sophisticated analysis involving monitoring online activities and point-of-sale transactions, and ensuring real-time detection of changes in customer trends.
Collecting and analyzing customer data will help gain insights into customer behavior. This is done with a similar approach that is used by marketers and advertisers and results in more achievable, focused, and targeted campaigns.
A more targeted and personalized campaign will ensure more cost-cutting and efficiency as high-potential clients can be targeted with the right products.
A good example of a brand that uses big data for targeted advertisements is Netflix. It uses big data analytics for targeted advertising. The data gives insights into what interests the subscribers the most.
Risk Management
A risk management plan is a critical investment for any business regardless of the sector as these are unprecedented times with a highly risky business environment. Being able to predict a potential risk and addressing it before it occurs is crucial for businesses to remain profitable.
Big data analytics has contributed immensely toward the development of risk management solutions. Tools allow businesses to quantify and model regular risks. The rising availability and diversity of statistics have made it possible for big data analytics to enhance the quality of risk management models, thus achieving better risk mitigation strategies and decisions.
UOB in Singapore uses big data for risk management. The risk management system allows the bank to reduce the calculation time of the value at risk.
Innovations and Product Development
Big data has become a smart way of creating additional revenue streams through innovations and product improvement. Organizations are first correct as much data as possible before moving on to designing new product lines and redesigning existing ones.
The design processes have to encompass the requirements and needs of customers. Various channels are available to help study these customer needs. Big data analytics helps a business to identify the best ways to capitalize on those needs.
Amazon Fresh and Whole Foods are the perfect examples of how big data can help improve innovation and product development. Data-driven logistics provides companies with the required knowledge and information to help achieve greater value.
Supply Chain Management
Big data offers improved clarity, accuracy, and insights to supplier networks. Through big data analytics, it is possible to achieve contextual intelligence across supply chains. Suppliers are now able to avoid the constraints and challenges that they faced earlier.
Suppliers incurred huge losses and were prone to making errors when they were using traditional enterprise and supply chain management systems. However, approaches based on big data made it possible for suppliers to achieve success with higher levels of contextual intelligence.
PepsiCo depends on enormous amounts of data for efficient supply chain management. The company tries to ensure that it replenishes the retailers’ shelves with appropriate numbers and types of products. Data is used to reconcile and forecast the production and shipment needs.
Dive deep down into this big data tutorial to know more about big data.
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Types of Big Data
Data falls into three main categories:
Structured Data
Any data that can be stored, accessed, and processed in a fixed format is known as structured data. Businesses can get the most out of this type of data by performing analysis. Advanced technologies help generate data-driven insights to make better decisions from structured data.
Unstructured Data
Data that has an unknown structure or form is unstructured data. Processing and analyzing this type of data for data-driven insights can be a difficult and challenging task as they are under different categories and putting them together in a box will not be of any value. A combination of simple text files, images, videos, etc., is an example of unstructured data.
Semi-structured data
Semi-structured data, as you may have already guessed, has both structured and unstructured data. Semi-structured data may seem structured in form, but it is not exactly well-defined with table definition in relational DBMS. Web applications have unstructured data such as transaction history files, log files, etc.
How are we contributing to the creation of Big Data?
Every time one opens an application on his/her phone, visits a web page, signs up online on a platform, or even types into a search engine, a piece of data is gathered.
So, whenever we turn to our search engines for answers a lot of data is created and gathered.
But as users, we are usually more focused on the outcomes of what we are performing on the web. We don’t dwell on what happens behind the scenes. For example, we might have opened up our browser and looked up for ‘big data,’ then visited this link to read this blog. That alone has contributed to the vast amount of big data. Now imagine the number of people spending time on the Internet visiting different web pages, uploading pictures, and whatnot.
All of this adds up to the stockpile of data.
Characteristics of Big Data
There are some terms associated with big data that actually help make things even clearer about big data. These are essentially called the characteristics of big data and are termed as volume, velocity, and variety, giving rise to the popular name 3Vs of big data, which I am sure we must have heard before. But, if it feels new to you, do not worry. We are going to discuss them in detail here. As people are understanding more and more about the ever-evolving technological term, big data, it shouldn’t come as a shock if more characteristics are added to the list of the 3Vs. These are called veracity and value.
Let’s check out each and every one of them, individually.
| Characteristics of Big Data | Details |
| Volume | Organizations have to constantly scale their storage solutions since big data requires a large amount of space to be stored. |
| Velocity | Since big data is being generated every second, organisations need to respond in real time to deal with it. |
| Variety | Big data comes in a variety of forms. It could be structured or unstructured, or even in different formats such as text format, videos, images, and more. |
| Veracity | Big data, as large as it is, can contain wrong data too. Uncertainty of data is something organisations have to consider while dealing with big data. |
| Value | Just collecting big data and storing it is of no consequence unless the data is analyzed and a useful output is produced. |
Challenges of Big Data
It must be pretty clear by now that while talking about big data one can’t ignore the fact that there are some obvious big data challenges associated with it. So moving forward in this blog, let’s address some of those challenges.
Data growing at such a quick rate is making it a challenge to find insights from it. There is more and more data generated every second from which the data that is actually relevant and useful has to be picked up for further analysis.
Such a large amount of data is difficult to store and manage by organizations without appropriate tools and technologies.
- Syncing Across Data Sources
This implies that when organizations import data from different sources the data from one source might not be up to date as compared to the data from another source.
Large amounts of data in organizations can easily become a target for advanced persistent threats, so here lies another challenge for organizations to keep their data secure by proper authentication, data encryption, etc.
We can’t deny the fact that big data can’t be 100 percent accurate. It might contain redundant or incomplete data, along with contradictions.
These are some other challenges that come forward while dealing with big data, like the integration of data, skill and talent availability, solution expenses, and processing a large amount of data in time and with accuracy so that the data is available for data consumers whenever they need it.
Before we go further into getting to know technologies that can help manage big data, we should first get familiar with a very popular programming paradigm called MapReduce.
What it does is, allows performing computations on huge data sets on multiple systems in a parallel fashion.
MapReduce mainly consists of two parts: the Map and the Reduce. It’s kind of obvious! Anyway, let’s see what these two parts are used for:
- Map: It sorts and filters and then categorizes the data so that it’s easy to analyze it.
- Reduce: It merges all data together and provides the summary.
Big Data Frameworks
- Apache Hadoop is a framework that allows parallel data processing and distributed data storage.
- Apache Spark is a general-purpose distributed data processing framework.
- Apache Kafka is a stream processing platform.
- Apache Cassandra is a distributed NoSQL database management system.
These are some of the many technologies that are used to handle and manage big data. Hadoop is the most widely used among them.
Applications of Big Data
There are many real-life Big Data applications in various industries. Let’s find out some of them in brief.
Big data helps in risk analysis, management, fraud detection, and abnormal trading analysis.
- Advertising and Marketing
Big data helps advertising agencies understand the patterns of user behavior and then gather information about consumers’ motivations.
Big data can be used to sensor data to increase crop efficiency. This can be done by planting test crops to record and store the data about how crops react to various environmental changes and then using that data for planning crop plantation, accordingly.
Going forward in this big data tutorial, let’s see the job opportunities in this field.
Job Opportunities in Big Data
Knowledge about big data is one of the most important skills required for some of the hottest job profiles which are in high demand right now and the demand in these profiles won’t be dropping down any time sooner, because, honestly, the accumulation of data is only going to increase over time, increasing the number of talents required in this field, thus opening up multiple doors of opportunities for us.
Some of the hot job profiles are given below:
- Data analysts analyze and interpret data, visualize it, and build reports to help make better business decisions.
- Data scientists mine data by assessing data sources and using algorithms and machine learning techniques.
- Data architects design database systems and tools.
- Database managers control database system performance, perform troubleshooting, and upgrade hardware and software.
- Big data engineers design, maintain and support big data solutions.
Once we learn about big data and understand its use, we will come to know that there are many analytics problems we can solve, which were not possible earlier due to technological limitations. Organizations are now relying more and more on this cost-effective and robust method for easy data processing and storage.