Understanding What Is Big Data Analytics? Importance, Type, Scope And More

Big data has become a driving force behind changes in the way many businesses are run. But what is big data analytics and why is it important? As it spreads to small and medium-sized businesses, big data has the potential to change the way business is done. The term “Big Data” has been used since the early 1990s. The majority of people believe that John R. Mashey, who was employed at Silicon Graphics at the time, popularized the term. 

This blog will tell you everything you need to know about big data. For example, we’ll answer questions like, “What is big data and big data analytics? Why is big data important? What are the possibilities of using big data now?” “How Big Data Analytics work” “Why are big corporations using big data analytics?” and also benefits of big data of big data analytics. Let’s begin.

What is big data and big data analytics?

Big data is an organization’s collection of structured, semi structured, and unstructured data. This sets of complex data can be analyzed for information that can be used in projects involving machine learning, predictive modeling, and other uses of advanced analytics. For large amounts of data to be labeled “big data,” it must possess the five Vs: 

• Volume
• Velocity 
• Variety
• Veracity 
• Value

It can be used to find new patterns and trends, which is especially helpful for businesses that want to learn more about how their customers and users think and behave. These insights can help businesses find new ways to be creative and give them a strategic advantage by letting them be the first to offer products and services that aren’t widely available yet.

Big data analytics refers to the process of looking at a large amount of data to find valuable information, such as patterns and insights, correlations, market trends, and customer preferences. With this information, businesses can make better choices.

Examples of big data analytics come in many different forms. Several companies are using big data analytics and analyzing large volume of data to make reports, creating dashboards based on their huge amounts of current and historical data, and developing predictive models. There are numerous ways to analyze big data, including predictive analysis, prescriptive analysis, descriptive analysis, and diagnostic analysis. 

What is big data analytics used for?

Big data analytics is used in many different fields, like health care, education, insurance, retail, artificial intelligence, and manufacturing, to figure out what’s working and what’s not, so that processes, systems, decision making and profits can be improved. For example, big data analytics is essential to the modern health care industry or Banking and Securities industry or retail and wholesale trade for predictive analytics and product development.

27% of business executives say its company’s Big Data initiatives are profitable 

(VIA: Capgemini)

Product Research

Big data analytics is one of the most effective methods for gathering and analyzing feedback. It enables you to comprehend how consumers see your services and items. As a result, you can make the required adjustments and further develop your products according to market trends. This makes big data analytics important for both startups and large corporations.

Customer Service

Big data makes it easier to reach customers in a data driven way and find market trends. Processing and analyzing big data can show which channels aren’t connected and where customers drop out of the journey most often. It lets businesses give customers a more personalized experience that helps build customer loyalty. It also helps find the right customers and guess what will happen in the future.

Why is big data important?

Today, Big Data analytics power all online activities—all sectors and fields of social and online media platforms. Using Spotify, you can get music. 96 million users use the site daily which creates countless millions of data. By combining this data, the platform automatically suggests music through an intelligent recommendation engine based on likes, shares, searches and more.

What makes Big Data analytics so valuable is its technology, software tools and framework. Spotify users must have noticed its top recommendations section based on your interests, past experiences, and so forth.

As we’ve already mentioned, big data analytics helps businesses in their systems to enhance operations, provide better customer service, create targeted marketing campaigns, supply chain and carry out other tasks that, in the end, can help them boost sales and be successful. Big data has changed the world in ways that no one could have imagined.

History of Big Data Analytics

Data analytics has its roots back to when computers were first employed when businesses began using computers to collect and manage huge amounts of data. But until the late 90’s and early 2000’s, big data analytics started to emerge as organizations increasingly turned to computer software to help understand rapidly growing amounts of data. Using Big Data analysis today is an essential tool for organizations in any industry.

The evolution of Big Data Analytics

Big Data transforms business intelligence functions by using data as actionable information that adds value. Increasing data analysis and data storage has created new challenges for the capture, storing, and retrieval of information across businesses. In the era the big data analysis has increased in size, with accelerated growth in the volume and variety of data, as well as speed in data acquisition and changing.

Advanced methods and tools for mining and analyzing data with complex structures and data sets, such as XML/Json data, text and image data, multidimensional data, graphs, sequences, and streaming data, are referred to as complex data analytics. This new technology will require significant new requirements to store the data and analyze data, creating a challenge for companies.

Sources of Big Data Analytics

Big data is a huge amount of information from many data sources. That means that it comes from more than one place. There are many different kinds of sources. Here, we’ve talked about the data sources where big data comes from and how they work.

Machine data

Machine data consists of the digital information generated by computers, mobile phones, embedded systems, and other networked devices. As technologies like radio frequency identification (RFID) and telematics improved, this kind of data became more common. It indicates that all information is coming from multiple sources, including smart sensors, SIEM logs, medical devices and wearables, road cameras, IoT devices, satellites, desktops, mobile phones, industrial equipment, etc.

Web data

The public web has a lot of information which is easy to access is free to use. Individuals and businesses have widespread access to information on the Web or Internet. In addition to that, numerous wiki websites make it easy and free for everyone to get information in real time. Start-ups and small and medium-sized businesses (SMEs) benefit from the vast amount of data on the web because they don’t have to build their own big data infrastructure and data repositories to use big data.

Cloud data

Recent years have seen a dramatic rise in the popularity of public cloud computing as the go-to environment for big data projects. Cloud computing allows businesses to access resources and services on demand without investing in, or taking responsibility for, underlying infrastructure. As a result, businesses of all sizes may now benefit from big data technology since they can leverage the cloud’s scalability and reduced costs.

IOT Data

One of the technologies that has the most potential is called IOT, which stands for the Internet of Things. It will make it possible for physical items to connect to the internet, which will result in an improvement to the way in which they function by producing data. It has the capacity to store a vast quantity of data. This is how the Internet of Things has evolved into a source of big data.

Database

Businesses use both old and new databases to get information that is useful to them. This system for collecting and storing data creates a hybrid data model that requires low investment and low IT infrastructure costs. The information in these databases can be analyzed or used to gain valuable insights that help companies make more money. This is also a good source of big data.

Types of big data analytics

Big data analytics come in a variety of forms depending on the process of analyzing. Different types of analytics serve various purposes, such as determining what actions are the most appropriate to take, imagining conceivable scenarios, detecting a problem from the past, and assessing present events. Big data analytics has four main types: predictive, diagnostic, descriptive, and prescriptive.

Predictive Analytics

Predictive analytics makes use of big data to discover relevant patterns in order to forecast future outcomes and assess the attractiveness of various solutions. Businesses use it to analyze any kind of unknown data from the past, present, or future.

Diagnostic Analytics

The most basic type of data analytics is called descriptive analytics, and it involves describing the most important parts of a data set. Typical strategies for diagnostic analytics include data discovery, drill-down, data mining, and correlations.

Descriptive Analytics

The process of looking at both current and past data to find patterns and connections is Descriptive analytics. It’s the simplest way to look at data because it just talks about trends and relationships without going into more detail.

Prescriptive Analytics

Prescriptive analytics is way to process data to figure out what the best next step is. This type of analysis looks at all the important factors and comes up with suggestions for what to do next. Because of this, prescriptive analytics is a valuable tool for making decisions based on data.

What are the 5 vs of big data analytics

The “5 V’s of Big Data” help us understand the most important parts of big data by pointing out five things that big data always implies. Now, we’ll explain what the 5 Vs of “Big Data” 

Velocity

Velocity is a measure of how fast data is made and how fast it moves. This is important for businesses that need their data to move quickly so they can use it at the right times to make the best business decisions they can.

Volume:

There must be many of them. Think about how each user’s actions can be tracked across multiple channels and platforms. The amount of information in even one source could be large, but the density is likely to be low. At first glance, it’s not easy to figure out what all this activity means. Moreover, the vast majority of businesses track considerably more structured and unstructured data.

Value

The value of big data is determined by how useful the information collected is for your business. No matter how much data there is, it’s usually not very useful on its own. To be useful, data needs to be turned into insights or information, which is where data processing comes in.

Variety

Variety refers to the different kinds of collected information. This may consist of structured data such as a first name or email address. Alternatively, it could be free-form, like a review of a product. In these situations, processing of data comes before analyzing it.

Veracity

Veracity is a quality of big data that has to do with continuity, precision, reliability, and validity. Data veracity is about how skewed, noisy, or odd the data is. It can also mean that there are errors, outliers, or missing values in the data.

What is the scope of big data analytics?

Big data analytics is in high demand because it has many applications and advantages. As many industries are starting big data analytics programs it is becoming more wide spread everyday.  The access to defined decisions that it provides makes big data analytics important. Here are some uses and examples of big data that different fields are using.

Big data in healthcare

Today, health care industry uses big data to ensure better treatment and diagnostic. The prediction of costs is another important cause of using big data in health care. It is based on a deep analysis of a lot of statistical data, such as the number of patients with chronic diseases, complaints against specific doctors, the number of second visits, epidemic indices, etc.

Big data in sports

Modern coaches use big data sets to come up with effective ways to help both each player and the team as a whole. Data science, especially for professional teams, lets coaches make very different pairings of players and other game plans for each game.

Big data in education

Teachers can use big data analysis to collect data and find out where students struggle or do well, figure out what each student needs, and come up with custom ways to help them learn. It also lets students choose how they want to learn.

Big data in marketing

Big data in marketing is the collection, analysis, and application of enormous volumes of digital information to enhance data driven commercial operations. It also helps in serving targeted ads for every business. Targeted ads performs better as they concentrate on the particular characteristics, interests, and preferences of a consumer.

What are good tools for big data analytics?

There are many great big data analytics tools to choose from. Only the right tools can bring meaningful insights and help organizations achieve operational efficiency.

Here are top 10 tools for analyzing big data that data scientists use.

1. R-Programming: R is a free environment for computing statistics and making graphs. It compiles and runs on a wide range of UNIX platforms, Windows, and MacOS.
2. Altamira LUMIFY, Altamira LUMIFY is a popular platform for combining, analyzing, and displaying big data from different data sources that helps build intelligence that can be used.
3. Apache Hadoop : Apache Hadoop is an open source framework that is used to store and process large datasets of between a few gigabytes and a few petabytes of data quickly and efficiently.
4. MongoDB : MongoDB is a cross-platform, document-oriented database program with open source. 
5. RapidMiner : RapidMiner is a powerful data mining tool that can do everything from data mining to model deployment and model operations.
6. Apache Spark : Apache Spark is a multi-language engine for doing data engineering, data science, and machine learning on single-node machines or in clusters.
7. Microsoft Azure : Microsoft Azure, formerly known as Windows Azure, is the public cloud computing platform offered by Microsoft.
8. Zoho Analytics : Zoho Analytics is business intelligence and analytics software that you can use on your own. It lets you make dashboards and look at data.
9. Xplenty : Through reverse ETL, Xplenty is a cloud-based platform that brings together and makes use of data.
10. Splice Machine : Splice Machine is a data platform that lets you do offline and batch analysis and powers smart apps for operational workflows.

How do I learn about big data technologies?

You can teach yourself data science by taking online courses or watching videos on YouTube. If you want to work in this field, you can find a lot of resources to help you learn on the Internet. Still, self-learning lacks structure, and you might not know what important parts you’re missing.

There are also online courses you can take to learn about big data. These are a few good options for learning about big data.

• Data Science Dojo
• WeCloudData
• Data Analytics Bootcamp From Springboard
• INE
• Coding Nomads

You could also get a certificate from a well-known organization. Cornell University, the Data Science Council of America, and the Mining Massive Data Sets Graduate Certificate at Stanford University are some of the best choices.

What is the difference between Data Science and Big Data?

Data science is a catch-all term for all the methods and tools that are used during the life cycle of useful data. On the other hand, it usually refers to very large sets of data that are hard to “use” without using specialized and often new technologies and techniques.

Let’s look at the distinctions between Data Science and Analytics

Data Science	Big Data
Data science is a field.	Big Data is a way to gather, store, and process a lot of information.
It has to do with gathering, processing, analyzing, and using data in different ways. It’s more about ideas.	It’s about getting important and useful information out of a lot of data.
It is a discipline similar to computer science, applied statistics, and applied mathematics.	It is a way to track and find trends in large sets of complicated data.
The goal is to create data-driven products for a business.	The goal is to make data more useful and important by using traditional methods to pull out only the most important information from huge amounts of data.
Data Science is mostly done with tools like SAS, R, Python, etc.	Hadoop, Spark, Flink, and other tools are used most often in big data.
It is a subset of big data because it includes techniques like scraping data, cleaning it, visualizing it, using statistics, and many more.	It is a part of data science that has to do with mining, which is in the data science pipeline.
Most of the time, scientists use it.	It is mostly used for business purposes and to satisfy customers.
It is mostly about the science behind the data.	It is more involved with the processes for handling large quantities of data.

Which is the best database for big data?

The following are the top ten free big data databases:

1. Cassandra : Cassandra is a NoSQL distributed database that runs on commodity servers and manages large amounts of data.
2. CouchDB : CouchDB is an open-source, cross-platform, single-node, document-oriented, No-SQL database that works on multiple platforms.
3. Neo4j : Neo4j has an architecture that is ready for the cloud, grows with your data needs, reduces infrastructure costs, and improves performance across all connected datasets.
4. HBase : Apache HBase is a NoSQL, distributed, open-source big data store. It allows access to petabytes of data in a random, strictly consistent, real-time manner.
5. MongoDB: MongoDB is a document database that works well, is always available, and is easy to scale.
6. FlockDB :FlockDB is an open-source, distributed, fault-tolerant graph database for the management of extensive but shallow network graphs.
7. Hibari : Gemini Mobile Technologies will release Hibari as an open source solution. It is designed to store Big Data in a safe way.
8. OrientDB : OrientDB is the first Multi-Model Open Source NoSQL DBMS that combines the power of graphs and the flexibility of documents into a high-performance, scalable operational database.
9. Terrastore : Terrastore is a contemporary document repository that offers advanced scalability and elasticity capabilities without sacrificing consistency.
10. Riak : Riak offers NoSQL database solutions that make it possible for large amounts of unstructured data to be stored in a distributed system.

What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science,Machine Learning, and Big Data?

Data analytics, data analysis, data mining, data science,  machine learning, and big data are all interconnected as they are all under the information technology umbrella. Yet they have some core differences among themselves. Data science is a wide field of science that looks at how to make sense of data. 

Data mining is part of the pipeline for data science. It focuses more on understanding methods and tools to find patterns in data that are new and to make data easier to analyze. Mining data works as a mining operation where big data is the mine. businesses mostly use it to enhance customer experience.

Data science and machine learning are two ideas in the field of technology that help us make and improve products, services, infrastructure systems, and other things by using data management. Making raw data untestable and usable requires some kind of cleaning and transformation.

Data science is a technology that pulls useful information out of large amounts of data. Raw data becomes more sophisticated in the process to become processed data. It is a great tool for machine learning and decision-making. It helps predict future outcomes and take better decisions.

What challenges do businesses face when implementing big data analytics?

Big data is hard because it requires figuring out how to handle a huge amount of data, which means storing and analyzing it in many data warehouses. 

The volume of data, finding patterns correlations from data and using it phases like product development and taking informed decisions are some of the basic challenges of big data analytics.

These are the top five challenges for big data analytics:

Lack of skilled professionals 

Companies need skilled data professionals to run all of these new technologies and big data tools. Data scientists, data analysts, and data engineers will be among these professionals. They will use the tools and figure out what the big data sets mean. QuantHub did a survey that showed there will be a shortage of 250,000 data scientists in 2020. Three-fifths of respondents predicted the most difficulty in attracting data science talent.

Poor data quality costs the U.S. economy around $3.1 trillion per year.

(Via:IBM)

Security Issues

Even though better analysis could help companies make better decisions, it also has some downsides, such as security problems that could get companies into trouble when they are working with sensitive information.

Utilizing Data

Businesses struggle with their big data projects because they don’t understand them well enough. Employees can lack important knowledge about big data analytics, such as how to store data and process it, how important it is, or where it comes from.. People who work with data might know what’s going on, but others might not have a comprehensive vision.

Data Growth

One of the most pressing issues in big data is the proper storage of massive amounts of data. The amount of data that companies store in their data centers and databases is growing quickly. As time goes on, these data sets grow in a way that is hard to handle.

Integration Issues

Data in a business originates from a variety of sources, including social media pages, ERP software, customer logs, financial reports, e-mails, presentations, and reports made by staff. Putting this huge amounts of structured data together to make reports is a tough job. Firms often don’t pay enough attention to this. But data integration is important for analysis, reporting, and business intelligence, so it has to be perfect.

Join the Big Data Analytics Revolution

The use of data affect the whole human experience, so the need for professionals who are able to understand the data is high.

To start learning how businesses use Big Data analytics, you can start with Big Data today at Simplearns Big Data engineer and data analytics Bootcamp!

See a video by Simplearearn on Big Data Analysis. Get it now for FREE. Subscribe on YouTube & Be a part of the 5000+ Happy learners.

FAQ

1. How much does big data analytics cost?

According to information from the market, the minimum cost of data analytics is around $1009.99 per month. This range is enough for most simple projects involving big data.

2. Which is better to study, data science or big data?

Big data is more about technology, computer tools, and software, while data science is more about making business decisions. Due to the inability of tools to perform data engineering tasks, data engineers are in higher demand than data scientists.

3. How big data analytics is used in healthcare?

In health care, sources of big data include patient medical records, hospital records, medical exam results, and information collected by healthcare testing machines (such as those used to perform electrocardiograms, also known as EKGs).

4. What is a data lake?

Big data analytics uses a data lake which is a centralized repository for storing, processing, and securing vast amounts of organized, semi structured, and unstructured data. It can store data in its original format and handle any type of data regardless of size restrictions.