What is Big Data? Why Big Data Analytics Is Important?
Data is Indispensable.
From centuries data has been playing an important role in our lives. That said on a daily basis we create 2.5 quintillion bytes of data. This means 90% of the world’s data was created in the last two years alone. And this vast voluminous data set that is so big that it cannot be analyzed using traditional methods is called Big Data. To examine this structured and unstructured data Big Data analytics technique is used.
In this article, we will discuss what is this large volume of data, what is Big Data Analytics and why is it important.
What is Big Data?
- Is it a product?
- Is it a set of tools?
- Is it a data set that is used by big businesses only?
- How big businesses deal with big data repositories?
- What is the size of this data?
- What is big data analytics?
- What is the difference between big data and Hadoop?
These and several other questions come to mind when we look for the answer to what is big data? Ok, the last question might not be what you ask, but others are a possibility.
Hence, here we will define what is it, what is its purpose or value and why we use this large volume of data.
Businesses today look for new and better ways to stay competitive, profitable and prepared for the future, and, according to industry experts, Big Data analytics offer ways to learn new ideas, extract new insight and stay ahead of the curve.
Big Data refers to a massive volume of both structured and unstructured data that overpowers businesses on a day to day basis. But it’s not the size of data that matters, what matters is how it is used and processed. It can be analyzed using big data analytics to make better strategic decisions for businesses to move.
According to Gartner:
Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.
Importance of Big Data
The best way to understand a thing is to know its history.
Data has been around for years; but the concept gained momentum in the early 2000s and since then businesses started to collect information, run big data analytics to uncover details for future use. Thereby, giving organizations the ability to work quickly and stay agile.
This was the time when Doug Laney defined this data as the three Vs (volume, velocity, and variety):
Volume: is the amount of data moved from Gigabytes to terabytes and beyond.
Velocity: The speed of data processing is velocity.
Variety: data comes in different types from structured to unstructured. Structured data is usually numeric while unstructured – text, documents, email, video, audio, financial transactions, etc.
Where these three Vs made understanding big data easy, they even made clear that handling this large volume of data using the traditional framework won’t be easy. This was the time when Hadoop came into existence and certain questions like:
- What is Hadoop?
- Is Hadoop another name of big data?
- Is Hadoop different than big data?
All these came into existence.
So, let’s begin answering them.
Big Data and Hadoop
Let’s take restaurant analogy as an example to understand the relationship between big data and Hadoop
Tom recently opened a restaurant with a chef where he receives 2 orders per day he can easily handle these orders, just like RDBMS. But with time Tom thought of expanding the business and hence to engage more customers he started taking online orders. Because of this change the rate at which he was receiving orders increased and now instead of 2 he started receiving 10 orders per hour. This same thing happened with data. With the introduction of various sources like smartphones, social media, etc data growth became huge but due to a sudden change handling large orders/data isn’t easy. Hence a need for a different kind of strategy to cope up with this problem arise.
Aware of this situation Tom started thinking of a solution. Similarly, with the advancement of technology data started to generate at an alarming rate. To handle the huge rate of orders Tom hired 4 more chefs. Everything was going well but as the food shelf used by 4 chefs was the same it was becoming a bottleneck, so the solution wasn’t that efficient
Likewise, to tackle the data problem huge datasets, multiple processing units were installed but this wasn’t effective either as the centralized storage unit became the bottleneck. This means if the centralized unit goes down the whole system gets compromised. Hence, there was a need to look for a better solution for both data and restaurant.
Tom came with an efficient solution, he divided the chefs into two hierarchies, i.e. junior and head chef and assigned each junior chef with a food shelf. Say for example the dish is pasta sauce. Now, according to Tom’s plan, one junior chef will prepare pasta and the other junior chef will prepare the sauce. Moving ahead they will hand over both pasta and sauce to the head chef, where the head chef will prepare the pasta sauce after combining both the ingredients, the final order will be delivered. This solution worked perfectly for Tom’s restaurant and for Big Data this is done by Hadoop.
Hadoop is an open-source software framework that is used to store and process data in a distributed manner on large clusters of commodity hardware. Hadoop stores the data in a distributed fashion with replications, to provide fault tolerance and give a final result without facing bottleneck problem. Now, you must have got an idea of how Hadoop solves the problem of Big Data i.e.
- Storing huge amount of data.
- Storing data in various formats: unstructured, semi-structured and structured.
- The processing speed of data.
So does this mean both Big Data and Hadoop are same?
We cannot say that, as there are differences between both.
What is the difference between Big Data and Hadoop?
- Big data is nothing more than a concept that represents a large amount of data whereas Apache Hadoop is used to handle this large amount of data.
- It is complex with many meanings whereas Apache Hadoop is a program that achieves a set of goals and objectives.
- This large volume of data is a collection of various records, with multiple formats while Apache Hadoop handles different formats of data.
- Hadoop is a processing machine and big data is the raw material.
Now that we know what this data is, how Hadoop and big data work. It’s time to know how companies are benefiting from this data.
How Companies are Benefiting from Big Data?
A few examples to explain how this large data helps companies gain an extra edge:
Coca Cola and Big Data
Coca-Cola is a company that needs no introduction. For centuries now, this company has been a leader in consumer-packaged goods. All its products are distributed globally. One thing that makes Coca Cola win is data. But how?
Coca Cola and Big data:
Using the collected data and analyzing it via big data analytics Coca Cola is able to decide on the following factors:
- Selection of right ingredient mix to produce juice products
- Supply of products in restaurants, retail, etc
- Social media campaign to understand buyer behavior, loyalty program
- Creating digital service centers for procurement and HR process
Netflix and Big Data
To stay ahead of other video streaming services Netflix constantly analyses trends and makes sure people get what they look for on Netflix. They look for data in:
- Most viewed programs
- Trends, shows customers consume and wait for
- Promotional visuals, clicks, time spent to watch it
- Devices used by customers to watch its programs
- What viewers like binge-watching, watching in parts, back to back or a complete series.
For many video streaming and entertainment companies, big data analytics is the key to retain subscribers, secure revenues, and understand the type of content viewers like based on geographical locations. This voluminous data not only gives Netflix this ability but even helps other video streaming services to understand what viewers want and how Netflix and others can deliver it.
Alongside there are companies that store following data that helps big data analytics to give accurate results like:
- Tweets saved on Twitter’s servers
- Information stored from tracking car rides by Google
- Local and national election results
- Treatments took and the name of the hospital
- Types of the credit card used, and purchases made at different places
- What, when people watch on Netflix, Amazon Prime, IPTV, etc and for how long
Hmm, so this is how companies know about our behavior and they design services for us.
What is Big Data Analytics?
The process of studying and examining large data sets to understand patterns and get insights is called big data analytics. It involves an algorithmic and mathematical process to derive meaningful correlation. The focus of data analytics is to derive conclusions that are based on what researchers know.
Importance of big data analytics
Ideally, big data handle predictions/forecasts of the vast data collected from various sources. This helps businesses make better decisions. Some of the fields where data is used are machine learning, artificial intelligence, robotics, healthcare, virtual reality, and various other sections. Hence, we need to keep data clutter-free and organized.
This provides organizations with a chance to change and grow. And this is why big data analytics is becoming popular and is of utmost importance. Based on its nature we can divide it into 4 different parts:
In addition to this, large data also play an important role in these following fields:
- Identification of new opportunities
- Data harnessing in organizations
- Earning higher profits & efficient operations
- Effective marketing
- Better customer service
- Competitive advantages over rivals
Now, that we know in what all fields data plays an important role. It’s time to understand how big data and its 4 different parts work.
Big Data Analytics and Data Sciences
Analysis of data involves the use of advanced techniques and tools like machine learning, data mining, statistics. The data thus extracted from different sources and in different sizes is used to provide analysis.
Data Sciences, on the other hand, is an umbrella term that includes scientific methods to process data. Data Sciences combine multiple areas like mathematics, data cleansing, etc to prepare and align big data.
Due to the complexities involved data sciences is quite challenging but with the unprecedented growth of information generated globally concept of voluminous data is also evolving. Hence the field of data sciences that involve big data is inseparable. Data encompasses, structured, unstructured information whereas data sciences is a more focused approach that involves specific scientific areas.
Businesses and Big Data Analytics
Due to the rise in demand use of tools to analyze data is increasing as they help organizations find new opportunities and gain new insights to run their business efficiently.
Moreover, by focusing on customer companies can improve their operations and earn more profits. Tools like Hadoop help in reducing storage costs. Thereby increasing business efficiency, this, in turn, leads to saving money, energy and making faster decisions.
Data over the years has seen enormous growth due to which data usage has increased in industries ranging from:
All in all, Data analytics has become an essential part of companies today.
Job Opportunities and big data analytics
Data is almost everywhere hence there is an urgent need to collect and preserve whatever data is being generated. This is why big data analytics is in the frontiers of IT and had become crucial in improving businesses and making decisions. Professionals skilled in analyzing data have got an ocean of opportunities. As they are the ones who can bridge the gap between traditional and new business analytics techniques that help businesses grow.
Benefits of Big Data Analytics
- Cost Reduction
- Better Decision Making
- New product and services
- Fraud detection
- Better sales insights
- Understanding market conditions
- Data Accuracy
- Improved Pricing
How big data analytics work and its key technologies
No single technology can encompass large data, but advanced big data analytics can be applied to data, to get the most value from the information.
Here are the biggest players:
Machine Learning: Machine learning, trains a machine to learn and analyze bigger, more complex data to deliver faster and accurate results. Using a machine learning subset of AI organizations can identify profitable opportunities – avoiding unknown risks.
Data management: With data constantly flowing in and out of the organization we need to know if it is of high quality and can be reliably analyzed. Once the data is reliable a master data management program is used to get the organization on the same page and analyze data.
Data mining: Data mining technology helps analyze hidden patterns of data so that it can be used in further analysis to get an answer for complex business questions. Using data mining algorithm businesses can make better decisions and can even pinpoint problem areas to increase revenue by cutting costs. Data mining is also known as data discovery and knowledge discovery.
Hadoop: Hadoop is open-source software that helps manage data processing and storage of data applications in an organized manner on computer servers. Hadoop has become a key technology that supports advanced big data analytics initiatives, including machine learning, data mining, etc. Hadoop system can handle different forms of structured and unstructured data giving an extra edge to collect, process and analyze data easily.
In-memory analytics: This business intelligence (BI) methodology is used to solve complex business problems. By analyzing data from RAM computer’s system memory query response time can be shortened and faster business decisions can be made. This technology even eliminates the overhead of storing data aggregate tables or indexing data, resulting in faster response time. Not only this in-memory analytics even helps the organization to run iterative and interactive big data analytics.
Predictive analytics: Predictive analytics is the method of extracting information from existing data to determine and predict future outcomes and trends. techniques like data mining, modeling, machine learning, AI are used to analyze current data to make future predictions. Predictive analytics allows organizations to become proactive, foresee future, anticipate the outcome, etc. Moreover, it goes further and suggests actions to benefit from the prediction and also provide a decision to benefit its predictions and implications.
Text mining: Text mining also referred to as text data mining is the process of deriving high-quality information from unstructured text data. With text mining technology, you uncover insights you hadn’t noticed before. Text mining uses machine learning and is more practical for data scientists and other users to develop big data platforms and help analyze data to discover new topics.
Big data analytics challenges and ways they can be solved
A huge amount of data is produced every minute hence it is becoming a challenging job to store, manage, utilize and analyze it. Even large businesses struggle with data management and storage to make a huge amount of data usage. This problem cannot be solved by simply storing data that is the reason organizations need to identify challenges and work towards resolving them:
- Improper understanding and acceptance of big data
- Meaningful insights via big data analytics
- Data storage and quality
- Security and privacy of data
- Collection of meaningful data in real-time: Skill shortage
- Data synching
- Visual representation of data
- Confusion in data management
- Structuring large data
- Information extraction from data
Organizational Benefits of Big Data
Big Data is not useful to organize data, but it even brings a multitude of benefits for the enterprises. The top five are:
- Understand market trends: Using large data and big data analytics, enterprises can easily, forecast market trends, predict customer preferences, evaluate product effectiveness, customer preferences, and gain foresight into customer behavior. These insights in return help understand purchasing patterns, buying patterns, preference and more. Such beforehand information helps in ding planning and managing things.
- Understand customer needs: Big Data analytics helps companies understand and plan better customer satisfaction. Thereby impacting the growth of a business. 24*7 support, complaint resolution, consistent feedback collection, etc.
- Improving the company’s reputation: Big data helps deal with false rumors, provides better service customer needs and maintains company image. Using big data analytics tools, you can analyze both negative and positive emotions that help understand customer needs and expectations.
- Promotes cost-saving measures: The initial costs of deploying Big Data is high, yet the returns and gainful insights more than you pay. Big Data can be used to store data more effectively.
- Makes data available: Modern tools in Big Data can in actual-time presence required portions of data anytime in a structured and easily readable format.
Sectors where Big Data is used:
- Retail & E-Commerce
- Finance Services
With this, we can conclude that there is no specific definition of what is big data but still we all will agree that a large voluminous amount of data is big data. Also, with time the importance of big data analytics is increasing as it helps enhance knowledge and come to a profitable conclusion.
If you are keen to benefit from big data, then using Hadoop will surely help. As it is a method that knows how to manage big data and make it comprehensible.