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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 big data analytics tutorial, we will learn Big Data Analytics meaning is, why it is so important, and what are the different features of big data analytics.
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 in 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 relational databases. Most modern computers are able to make sense of structured data.
Unstructured data, on the other hand, is the one that 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 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 XML data, JSON files, and others.
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|
Introduction to 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 a large number of processes that are composed 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.
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.
Characteristics of Big Data Analytics
Conceptually, Big Data projects can be extremely challenging for businesses and often fail, Big Data characteristics are defined primarily, by 4 Vs. Let’s take a look at the four Vs of big data analytics:
The immense volume of data is larger than the processed data in a normal system of an enterprise. Thus, results in newly designed systems. The reason for such volumes of data varies with developments.
One reason for Big Data volume is the data of different IT systems that are merged with what multiplies the amount of data. Alternatively, a crawler procures or extracts third-party data with the objective to merge it with its own systems.
The ingestion and the processed data of different systems result in veracity challenges about the data accuracy. For example, if different records show the same data with different date timestamps, it is hard to determine which record is the correct one.
Alternatively, if data is incomplete, one does not know about it and there can be a system error. Hence, big data systems need concepts, methods, and tools to overcome the veracity challenge.
Along with the different source systems, data that were not logged and overridden before can be stored in big data scenarios. The data are like record updates, history changes, and can allow for new use cases like time-series analytics that are otherwise impossible on old override data.
There are new data sources that generate enormous volumes of data. The more simplistic versions include data from social media or smartphone apps with new insights into customer interactions.
The variety in such data ranges from unstructured social media text data to structured operative enterprise system data. It can go over computable financial time series data, time series commit logs, app usage as well as semi-structured customer interaction data.
Big data systems and landscapes find it difficult to handle this variety in data and allow users to combine them to make sense.
As business models of enterprises are depending on IoT data more and more, IoT data is increasing continuously resulting in the increasing speed in data generation. Data generation is not static records in a database solely. A continuous stream is necessary.
This further leads to concerns about data storage as well as computation and reaction to events in the data streams. Batch processing enough for large volumes of data cannot keep up with increasing velocity, anymore. This is why modern big data analytics landscapes should be able to store this fast-generated data quickly and execute data computations and movements efficiently.
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Types of Big Data Analytics
- Prescriptive Analytics: This 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 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 that need to be taken.
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Big Data Analytics Tools
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.
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Major Sectors Using Big Data Analytics
Now let us learn more about big data analytics services and the role it plays in our day to day life.
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 omnichannel experience. Customers might make contact with a brand on one channel, then finally buy it through another channel, meanwhile going through more intermediary channels. Retailers 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 about 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 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 in healthcare also helps improve patient care and increase the efficiency of the treatment and medication processes. Some diseases can be diagnosed before their 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 the 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 fossil fuels.
So, tremendous amounts of Big Data Analytics go 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, deploying predictive maintenance, and optimally using the resources in order to reduce the capital expenditure.
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Big Data Applications
Here are some examples of the applications of Big Data Analytics:
- Customer Acquisition and Retention: Consumer information helps tremendously in marketing through data-driven actions on trends for the purpose of increasing customer satisfaction. For example, personalization engines for Netflix, Amazon, and Spotify helps with improved customer experiences and gain customer loyalty.
- Targeted Ads: Personalized data about interaction patterns, order history, and product page viewing histories can help immensely to create targeted ad campaigns for consumers on a larger scale and at the individual level.
- Product Development: Big data analytics has the ability to generate insights on development decisions, product viability, performance measurements, etc., and direct improvements that positively serve customers.
- Price Optimization: Pricing models can be modeled and used by retailers with the help of diverse data sources in order to maximize revenues.
- Supply Chain and Channel Analytics: Predictive analytical models help with B2B supplier networks, preemptive replenishment, route optimizations, inventory management, and the notification of potential delays in deliveries.
- Risk Management: Big data analytics help in the identification of new risks with the help of data patterns for the purpose of effective risk management strategies.
- Improved Decision-making: The insights that are extracted from data can help enterprises make sound and fast decisions.
Big Data Challenges
Big data analytics doesn’t just come with wide-reaching benefits, it also comes with its own challenges:
- Accessibility of data: With larger volumes of data, storage and processing become a challenge. Big data should be maintained in such a way that it can be used by less-experienced Data Scientists and Data Analysts.
- Data quality maintenance: With high volumes of data from disparate sources and in different formats, management of data quality requires considerable time, effort, and resources to properly maintain it.
- Data security: The complexity of big data systems poses unique challenges when it comes to security. It can be a complex undertaking to properly address such security concerns within complicated big data ecosystems.
- Choosing the right tools: Choosing Big Data Analytics tools from wide range that is available in the market can be confusing. One should know how to select the best tool that aligns with user requirements and infrastructure.
- The supply-Demand gap in skills: With a lack of data analytics skills in addition to the high cost of hiring experienced professionals, organizations are finding it hard to meet the demands.
Big Data Scope in Future
Data Analytics will have a significant role to play in the market in the coming years. In fact, it has already gained traction and most big organizations have started making use of Big Data Analytics to run their business operations. The IoT has been predicted to see tremendous growth and data will have a superior place in the market.
Here is how the future will look like for Big Data Analytics:
- It is a golden time to witness tremendous growth in cognitive analysis
- Companies will make the most out of data for securing financial gain, which further affirms the future scope of big data analytics
- The open-source solution is expected to have relevance in the market again
- Companies will pay more attention to data accuracy and security
- There will be a steep rise in the demand for data scientists
Plenty of job profiles are currently available in the data analytics domain. Companies have started strictly shifting their focus on the skill sets of an individual during the recruitment process instead of the traditional way of just looking at degrees.
There are over 3,000 Big Data Analytics job openings available in India according to LinkedIn and 42,000+ in the United States of America.
Anyone with strong analytical and numerical skills will have a better scope in the field. Some of the major job profiles in Data Analytics are Data Architects, Data Analysts, Database Administrators, Data Scientists, Data Engineers, and Statisticians.
Data Analytics is one of the most vital aspects that is driving some of the biggest and best companies forward today. Enterprises that can convert data into information and information into insights are the ones that 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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