Today, Big Data is one of the most important discussions among business leaders and industry captains. We are today living in a digitally-driven world, due to which every enterprise is going after Big Data in order to derive valuable insights out of the huge amount of raw data. So, in this blog post, we will learn what Big Data Analytics is, why it is so important, and what its various features and advantages are.
Big Data Types
Big Data is primarily measured by the volume of the data. But along with that, Big Data also includes data that is coming in fast and at huge varieties. Primarily, there are three types of Big Data, namely:
- Structured Data
- Unstructured Data
- Semi-structured Data
Big Data can be measured in terms of terabytes and more. Sometimes, Big Data can cross over petabytes. The structured data includes all the data that can be stored in a tabular column. The unstructured data is the one that cannot be stored in a spreadsheet; and semi-structured data is something that does not conform with the model of the structured data. You can still search semi-structured data just like structured data, but it does not offer the ease with which you can do it on the structured data.
The structured data can be stored in a tabular column. Relational databases are examples of structured data. It is easy to make sense of the relational databases. Most of the modern computers are able to make sense of structured data.
Unstructured data, on the other hand, is the one which cannot be fit into tabular databases. Examples of unstructured data include audio, video, and other sorts of data which comprise such a big chunk of the Big Data today.
The semi-structured data includes both structured and unstructured data. This type of data sets include a proper structure, but still it might not be possible to sort or process that data due to some constraints. This type of data includes the XML data, JSON files, and others.
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Comparing Big Data Analytics with Data Science
|Criteria||Big Data Analytics||Data Science|
|Type of Data Processed||Structured||All types|
|Types of Tools||Statistics and data modeling||Hadoop, coding, and Machine Learning|
|Domain Expanse||Relatively smaller||Huge|
|New Ideas||Not needed||Needed|
Processing Big Data
In order to process Big Data, you need to have cloud and physical machines as well. Today, due to the advancements in the technology, we might include Cloud Computing and Artificial Intelligence within the ambit of Big Data processing. Due to all these advancements, manual inputs can be reduced and automation can take over.
Data Analytics refers to the set of quantitative and qualitative approaches to derive valuable insights from data. It involves many processes that include extracting data, categorizing it in order to analyze various patterns, relations, and connections, and gathering other such valuable insights from it.
Today, almost every organization has morphed itself into a data-driven organization, and this means that they are deploying a data-driven approach in order to collect more data that is related to the customers, markets, and business processes. This data is then categorized, stored, and analyzed to make sense out of it and derive valuable insights from it.
Understanding Big Data Analytics
With Big Data Analytics, you can answer a new range of diagnostic questions about your business needs. It provides more data and sophisticated analytics to deliver actionable results to your business teams. You may start with a general question, one your traditional descriptive analytics has revealed.
Further, Big Data Analytics lets you explore deeper diagnostic questions—some of which you might not have even thought of asking—to reveal a new level of insight and identify steps that have to be taken to improve business performance. Many definitions on the topic of Big Data focus on a bottom-up view, using the three Vs of data—volume, variety, and velocity.
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The term ‘Big Data Analytics’ might look simple, but there are large number of processes which are comprised in Big Data Analytics. We can think of Big Data as one which has huge volume, velocity, and variety. Big Data Analytics tools can make sense of the huge volumes of data and convert it into valuable business insights.
Though the term ‘Big Data Analytics’ might seem simple, it is anything but simple. Data Analytics is most complex when it is deployed for Big Data applications. The three most important attributes of Big Data include volume, velocity, and variety.
The need for Big Data Analytics comes from the fact that we are generating data at extremely high speeds and every organization needs to make sense of this data. As per confirmed sources, by the year 2020, we will be generating a staggering 1.7 MB of data every second, contributed by every individual on earth.
All this tells us the importance of Big Data Analytics for making sense of all the huge volumes of data. Big Data Analytics helps us organize, transform, and model the data based on the requirements of an organization and identify patterns and draw conclusions from it.
Watch this insightful video to find out what a Big Data Analyst does in real life:
The larger the size of the data the bigger the problem. So, Big Data may be defined as the data where the size of it itself poses the problem and it needs newer ways of handling the same. The analysis of data that is at high volume, velocity, and variety means that the traditional methods of working with the data would not apply here.
Types of Big Data Analytics
- Prescriptive Analytics: This is the type of analytics talks about an analysis, which is based on the rules and recommendations, to prescribe a certain analytical path for the organization. At the next level, prescriptive analytics will automate decisions and actions—how can I make it happen? Building upon the previous analytics, neural networks and heuristics are applied to the data to recommend the best possible actions that derive desired outcomes.
- Predictive Analytics: This type of analytics ensures that the path is predicted for the future course of action. Answering the how and why questions will reveal specific patterns to detect when outcomes are about to occur. Predictive analytics builds upon the diagnostic analytics to look for these patterns and see what is going to happen. Machine Learning is also applied to continuously learn as new patterns emerge.
- Descriptive Analytics: In this type of analytics, we work based on the incoming data. For the mining of this data, we deploy analytics and come up with a description based on the data. Many organizations have spent years generating descriptive analytics—answering the ‘what happened’ questions. This information is valuable, but only provides a high-level, rearview mirror view of the business performance. In Diagnostic Analytics, most organizations start to apply Big Data Analytics to answer diagnostic questions—how and why something happened. Some might also call these behavioral analytics.
- Diagnostic Analytics: This is about looking into the past and determining why a certain thing happened. This type of analytics usually revolves around working on a dashboard. Diagnostic Analytics with Big Data helps in two ways: (a) the additional data brought by the digital age eliminates analytic blind spots, and (b) the how and why questions deliver insights that pinpoint the actions need to be taken.
Regardless of the type of Big Data Analytics you want to deploy, algorithms play a key role. Read this insightful blog to find out more.
How Does Big Data Analytics Help Derive Business Insights?
There are various tools in Big Data Analytics that can be successfully deployed in order to parse data and derive valuable insights out of it. The computational and data-handling challenges that are faced at scale mean that the tools need to be specifically able to work with such kinds of data.
The advent of Big Data changed analytics forever, thanks to the inability of the traditional data handling tools like relational database management systems to work with Big Data in its varied forms. Also, data warehouses could not handle data of extremely big size.
The era of Big Data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of Big Data, cost-effectively.
While data warehouses are certainly a relevant form of Data Analytics, the term ‘Data Analytics’ is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity. Check this informative blog that talks about how Big Data Analytics is driving the best Formula 1 teams ahead.
Databases for Big Data Analytics
Non-relational databases are used for working with unstructured data. Here, the data cannot be stored in the regular tabular column. JSON files and XML are some of the most important unstructured data types. With JSON, you can write tasks in the application layer and this allows enhanced cross-platform functionalities.
When it comes to Big Data processing engines like Hadoop, the speed at which the processing happens is extremely low, thanks to the constant read and write access that is needed with respect to disk storage. But with the high-speed in-memory processing, you can do read and write at a much higher pace. This is where the in-memory processing engines like Apache Spark and SAP HANA come into the picture.
Hadoop Hybrid: Data Storage and Processing
You can think of Hadoop as a hybrid processing engine that can work for both data storage and processing systems. The storage arm of Hadoop is the Hadoop Distributed File System, and the processing arm of Hadoop is MapReduce. Due to the need for hybrid processing engines in today’s digitally disruptive world, Hadoop is finding increased acceptance. Apache Hadoop is a hybrid data storage and processing tool that can be harnessed even by small organizations since it is part of the open-source platform.
Importance of Data Mining
Data mining can be used for reducing costs and increasing revenues. Data mining is one of the fundamental steps in the Data Analytics process. It is the step wherein you perform the Extract, Transform, and Load for getting the right data into data warehouses. It also takes on the task of storing and managing data based in multidimensional databases. Within data mining, we have some recent phenomena that are based on contextual analyzing of big data sets to discover the relationship between separate data items. The objective is to use a single data set for different purposes by different users. Finally, data mining is also assigned with the task of presenting the data which has been analyzed in a simple yet effective way.
Top Tools Used in Big Data Analytics
In this section, we will be familiarizing you with various aspects of the Big Data Analytics domain. Here, we include a list of analytical courses that you can take up:
- Apache Spark: Spark is a framework for real-time Data Analytics which is part of the Hadoop ecosystem.
- Python: This is one of the most versatile programming languages that is rapidly being deployed for various applications including Machine Learning.
- SAS: SAS is an advanced analytical tool that is being used for working with huge volumes of data and deriving valuable insights from it.
- Hadoop: It is the most popular Big Data framework that is being deployed by some of the widest range of organizations from around the world for making sense of big data.
- SQL: This is the structured query language that is used for working with relational database management systems.
- Tableau: This is the most popular Business Intelligence tool that is deployed for the purpose of data visualization and business analytics.
- Splunk: Splunk is the tool of choice for parsing the machine-generated data and deriving valuable business insights out of it.
- R Programming: R is the Number 1 programming language that is being used by Data Scientists for the purpose of statistical computing and graphical applications alike.
Watch this insightful video to learn more about the job role of a Data Analyst:
Major Sectors Using Big Data Analytics
The retail industry is actively deploying Big Data Analytics. They are applying the techniques of Data Analytics to understand what the consumers are buying and offering products and services that are tailor-made for these customers. Today, it is all about having an omni-channel experience. Customers might make contact with a brand on one channel, then finally buy it through another channel, meanwhile going through more intermediary channels. Retailer will have to keep track of these customer journeys, and they must deploy their marketing and advertising campaigns based on that in order to improve the chances of sales and lower costs.
Technology companies, offering products and services, are also heavily deploying Big Data Analytics. They are finding out more how the customers interact with their websites or apps and gather key information. Based on this, they are able to optimize their sales, customer service, improve customer satisfaction, and more. This also helps them launch new products and services since today we are living in a knowledge-intensive economy, and the enterprises in the technology sector are reaping the benefits of Big Data Analytics.
Healthcare is another industry that can benefit a lot from Big Data Analytics tools, techniques, and processes. Healthcare personnel can diagnose the health of their patients through various tests, run it through their computers, look for telltale signs of anomalies and maladies, and more. Big Data Analytics also helps improve patient care and increase the efficiency of the treatment and medication processes. Some diseases can be diagnosed before its onset so that the measures can be taken in a preventive manner rather than a remedial manner.
Manufacturing is an industrial sector that is involved with developing physical goods. The life cycle of a manufacturing process can vary from product to product. The manufacturing systems are involved within the industry setup and across the manufacturing floor. There are a lot of technologies that are involved like Internet of Things, Robotics, and others, but the backbone of each of these is firmly based on Big Data Analytics. Using Big Data Analytics, manufacturers can improve the yield, reduce the time to market, enhance the quality, optimize the supply chain and logistics process, and build prototypes before the launch of products so as to understand all the implications. Throughout all these steps, Big Data Analytics helps the manufacturers.
Most of the oil and gas companies which come under the energy sector are big users of Big Data Analytics. When it comes to discovering oil and resources, a lot of Big Data Analytics is deployed. Also, the market is very volatile for the fossil fuels. So, there is tremendous amounts of Big Data Analytics that goes into finding out what the price of a barrel of oil will be, what the output should be, and if an oil well will be profitable or not. Big Data Analytics is also deployed in finding out the equipment failures, deploy predictive maintenance, and optimally use the resources in order to reduce the capital expenditure.
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Data Analytics is one of the most vital aspects that is driving some of the biggest and best companies forward today. Enterprises which can convert data into information and information into insights are the ones which will own the future in a hyper-competitive world. For example, Uber disrupted the taxi hailing business, and Airbnb disrupted the hospitality business. Both these organizations are thriving on the sheer power of their deep data analytical mindset. So, the way forward for any company worth its salt is to have a clear data-driven approach and harness the power of Big Data using transformational data analytical techniques.
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