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 it. So, in this blog post, we will learn what is Big Data Analytics, why is it so important and what are its various features and advantages.
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 variety. 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 finally semi-structured data is the one which does not conform with the model of 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 structured data.
The structured data is the one which is in the 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 the tabular databases. The examples of unstructured databases include audio, video, and other sort 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 set includes 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.
Check this insightful video on Big Data Analytics for beginners:
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, 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 technology we might include cloud computing and artificial intelligence within the ambit of big data processing. Due to all these advancements, you can reduce the manual inputs and automation can take over.
Data Analytics refers to the set of quantitative and qualitative approach in order to derive valuable insights from data. It involves many processes that include extracting data, categorizing it in order to analyze the various patterns, relations, connections and gathering other such valuable insights from it.
Today almost every organization has morphed itself into a data-driven organization and this means 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 in order to make sense of it and derive valuable insights out of it.
Understanding Big Data Analytics
Answering a new range of diagnostic questions about your business using 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.
Big data analytics lets you explore the deeper diagnostic questions — some of which you might not have thought about asking — to reveal a new level of insight and identify steps to take to improve business performance. Many definitions on the topic of big data focus on a bottom-up view, using the 3 Vs of the 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 comprise of big data analytics. We can think of big data as the one which has huge volume, velocity and variety. The 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 for 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 to organize, transform and model the data based on the requirements of the organization and identify patterns and draw conclusions from it.
This shows the amount of data that is generated and hence the need for big Data Analytics tools in order to make sense of all that data. It organizes, transforms and models the data based on the requirements in order to draw the necessary conclusions and for identifying patterns in the data.
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 is the problem. So big data may be defined as the data where the size itself poses the problem and this needs newer ways of handling the data. So the analysis of data at high volume, velocity and variety means that the traditional methods of working with the data do not apply here.
Types of Big Data Analytics
- Prescriptive Analytics – This is the type of analytics that talks about the analysis based on the rules and recommendations in order 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 drive 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 identify that detect when outcomes are about to occur. Predictive analytics builds upon the diagnostic analytics to look for these patterns and see what will happen. Machine learning is also applied to continuously learn as new patterns emerge.
- 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 on by the digital age eliminates analytic blind spots, and (b) the how and why deliver insights that pinpoint actions to take.
- Descriptive Analytics – In this type of analytics we work based on the incoming data and 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 what happened questions. This information is valuable, but only provides a high-level, rearview mirror view of the business performance. 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.
Regardless of which type of Big Data Analytics you want to deploy, algorithms play a key role. Read this insightful blog to find out more.
How Big Data Analytics helps to derive business insights?
There are various tools in Big Data Analytics that can be successfully deployed in order to parse the data and derive valuable insights out of it. The computational and data-handling challenges that are faced at scale means 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, the data warehouses cannot handle data that is 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
The non-relational databases are used for working with unstructured data. Here the data cannot be stored in the regular tabular column. JSON files, 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 engine like Hadoop, the speed at which the processing happens is extremely slow thanks to constant read and write access that is needed with respect to disk storage that is needed. But with the high-speed in-memory processing you can do the read and write at a much higher pace thanks to in-memory processing. 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 processing arm of Hadoop is the 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 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, Load for getting the right data into data warehouse. It also takes on the task of storing and managing data based in multidimensional databases. Within data mining we have some recent phenomenon 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 the various aspects of the big Data Analytics domain. So herein includes a list of analytical courses that you can take as follows:
- 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 advanced analytical tools that is being used for working with huge volumes of data and derive 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 system.
- 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 derive valuable business insights out of it.
- R programming – R is the one number 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 the omni-channel experience. So the customer might make contact with a brand on one channel, then finally buy it through another channel before going through more intermediary channels. The retailer will have to keep track of these customer journeys are deploy their marketing and advertising campaigns based on that in order to improve the chances of sales and lower costs.
Technology companies that are offering products and services are also heavily deploying Big Data Analytics. They are finding out more how the customers interact 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 they launch new products and services since today we are living in a knowledge intensive economy and the enterprises in the technology sector are reaping its benefits thanks to extensive use 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. Big Data Analytics also helps to improve patient care and increase the efficiency of the treatment and medication processes. Some diseases can be diagnosed before its onset so that the steps can be taken in a preventive manner rather than remedial manner.
Manufacturing is an industrial sector that is involved with developing physical goods. The lifecycle 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, the 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 product so as to understand all the implications. Throughout all these steps the 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 price of barrel of oil will be, what should be output be and if an oil well will be profitable or not. All this need extensive amounts of Big Data Analytics. Big Data Analytics is also deployed in finding out about 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. The enterprises which can convert data into information and information into insights are the ones which will own the future in a hypercompetitive world where your next competitor can come from any industry vertical. Uber disrupted the taxi hailing business, 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 harnessing the power of big data using transformational data analytical techniques.
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