Updated on 29th Apr, 22 3531 Views

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Today, big data is one of the most important topics of discussion among business leaders and industry captains. We are living in a digitally-driven world, due to which every enterprise is going after big data in order to derive valuable insights out of large amounts of raw data. So, in this blog, we will learn what is the meaning of big data analytics, why it is so important, and what are the different features of big data analytics.

What is Big Data Analytics?

With big data analytics, we can answer a new range of diagnostic questions about our business needs. Big data analytics provides more data and sophisticated analytics to deliver actionable results to business teams.

Big data analytics lets us explore deeper diagnostic questions—some of which we may not have even thought of asking—to reveal a new level of insight and identify steps that have to be taken to improve the business performance.

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History and Evolution of Big Data Analytics

The idea of big data has actually been around for many years. Way back in the 1950s, before anyone even knew of the term big data, businesses were using some form of basic analytics consisting of numbers in a spreadsheet that had to be manually examined to uncover insights and trends.

Most enterprises now understand the significant value of applying analytics to captured data that streams into their businesses. Today, there are new benefits that big data analytics brings to the table; they are speed and efficiency.

While a few years ago, a business ran analytics on gathered information and uncovered insights that could be used for future decisions; today, insights can be identified for immediate action. This speed and agility gives enterprises a competitive edge that they did not have earlier.

Why is Big Data Analytics Important?

Big data analytics may look simple, but there are a large number of processes that are composed in it. We can think of big data as something that has huge volume, velocity, and variety. Big data analytics tools can make sense of the large volumes of data and convert it into valuable business insights.

The need for big data analytics comes from the fact that we are generating data at an extremely rapid pace, and every enterprise needs to make sense of this data. By the year 2020, we were generating a staggering 1.7 MB of data every second.

This indicates the importance of big data analytics for making sense of large volumes of data. Big data analytics helps us organize, transform, and model the data based on the requirements of an enterprise. Big data analytics also allows us to identify patterns and draw conclusions from the data.

The larger the size of the data, the bigger the problem. So, big data may be defined as the data where its size itself poses the problem and newer ways of handling the same are needed. The analysis of data that is high in volume, velocity, and variety means that the traditional methods of working with data are not applicable here.

Benefits of using Big Data Analytics

The following are some of the benefits of using big data analytics:

  • Analysis of large volumes of data from disparate sources in a variety of forms and kinds in a timely manner
  • Quickly making well-informed judgments for successful strategizing to enhance the supply chain, logistics, and other tactical decision-making sectors
  • Savings due to the increased efficiency and optimization of business processes
  • More informed risk management techniques based on large data sample sizes
  • Greater knowledge of consumer behavior, demands, and sentiment can result in better product development data and strategic management processes

Characteristics of Big Data Analytics

Conceptually, big data projects can be extremely challenging for enterprises as such projects often fail. Big data characteristics are primarily defined by the four Vs. Let us now take a look at the four Vs of big data analytics:


Big data volume is greater than the volume of processed data in a normal system of an enterprise. This results in newly-designed systems. The reason for such volumes of data varies with developments.

One reason for big data volume is that the data from different IT systems, which are merged together, 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 processed data of different systems results in veracity challenges about data accuracy. For example, if different records show the same data with different dates and timestamps, it is hard to determine which record is the correct one.

Alternatively, if data is incomplete and we do not know about it, then 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, the data that was not logged and overridden before can be stored in big data scenarios. The data is like record updates, history changes, and can allow for new use cases, such as 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, and semi-structured customer interaction data.

Big data systems and landscapes find it difficult to handle this variety in data and allow users to combine the data in order to make sense of it.


As the business models of enterprises are depending on IoT data more and more, IoT data is continuously resulting in the increasing speed in data generation. Data generation is not static records in a database solely; a continuous stream of data generation is necessary.

This further leads to concerns about data storage as well as computation and reaction to events in data streams. Batch processing enough for large volumes of data cannot keep up with the 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

The following are the four types of big data analytics:

Prescriptive Analytics

This type of analytics talks about an analysis that is based on rules and recommendations, to prescribe a certain analytical path for an enterprise. At the next level, prescriptive analytics will automate decisions and actions—how can we make that happen?

Building on the previous analytics, neural networks and heuristics are applied to the data to recommend the best possible actions that will derive the desired outcomes.

Diagnostic Analytics

In diagnostic analytics, most enterprises start to apply big data analytics to answer diagnostic questions such as how and why something happened. Some may also call this behavioral analytics.

Diagnostic analytics 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.

Predictive Analytics

This type of analytics ensures that the path for the future course of action is predicted. Answering the how and why questions will reveal specific patterns to detect when outcomes are about to occur.

Predictive analytics builds on diagnostic analytics to look for these patterns and see what is going to happen. Machine learning is also applied as new patterns emerge to continuously learn.

Descriptive Analytics

In this type of analytics, work is done based on incoming data. For the mining of this data, we deploy analytics and come up with a description based on the data.

Many enterprises 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.

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Applications of Big Data Analytics

Here are some examples of the applications of big data analytics:

  • Customer Acquisition and Retention: Customer information helps tremendously in marketing trends, through data-driven actions, for the purpose of increasing customer satisfaction. For example, personalization engines for Netflix, Amazon, and Spotify help with improved customer experiences and gaining customer loyalty.
  • Targeted Ads: Personalized data about interaction patterns, order history, and product page viewing history can help immensely to create targeted ad campaigns for customers 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 the 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 notification of potential delays in deliveries.
  • Risk Management: Big data analytics helps in the identification of new risks with the help of data patterns for the purpose of developing effective risk management strategies.
  • Improved Decision-making: The insights that are extracted from the data can help enterprises make sound and quick decisions.

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Big Data Analytics Implementation in Major Sectors

Now, let us learn a bit more about the big data analytics services and the role they play in our day-to-day lives.


The retail industry is actively deploying big data analytics. It is applying the techniques of data analytics to understand what the customers are buying and then offering products and services that are tailor-made for them.

Today, it is all about having an omnichannel experience. Customers may make contact with a brand on one channel and then finally buy the product(s) through another channel, meanwhile going through more intermediary channels. The retailers will have to keep track of these customer journeys, and they must deploy their marketing and advertising campaigns based on that, to improve the chances of increasing sales and lowering costs.


Technology companies are heavily deploying big data analytics. They are finding out more about how customers interact with websites or apps and gather key information. Based on this, technology companies are able to optimize their sales, customer service, customer satisfaction, etc. This also helps them launch new products and services since we are living in a knowledge-intensive economy, and the companies 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 the computers, look for telltale signs of anomalies, maladies, etc. Big data analytics also helps in healthcare to improve patient care and increase the efficiency of the treatment and medication processes. Some diseases can be diagnosed before their onset so that 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. Manufacturing systems are involved within the industry setup and across the manufacturing floor.

There are a lot of technologies that are involved in manufacturing such as the Internet of Things (IoT), robotics, etc., but the backbone of all of these is firmly based on big data analytics. Using big data analytics, manufacturers can improve their yield, reduce the time to market, enhance the quality, optimize the supply chain and logistics processes, and build prototypes before the launch of products. Big data analytics can help manufacturers through all these steps.


Most oil and gas companies, which come under the energy sector, are extensive users of big data analytics. A lot of big data analytics is deployed when it comes to discovering oil and other natural resources. 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 equipment failures, deploying predictive maintenance, and optimally using resources in order to reduce capital expenditure.

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Big Data Analytics Tools

In this section, we will go through the various aspects of the big data analytics domain:

  • Apache Spark: Spark is a framework for real-time data analytics, which is a part of the Hadoop ecosystem.
  • Python: Python 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 used for working with large volumes of data and deriving valuable insights from it.
  • Hadoop: Hadoop is the most popular big data framework that is deployed by a wide range of organizations from around the world for making sense of big data.
  • SQL: SQL is used for working with relational database management systems.
  • Tableau: Tableau 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 machine-generated data and deriving valuable business insights out of it.
  • R: R is the no. 1 programming language that is being used by data scientists for the purposes of statistical computing and graphical applications alike.

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Big Data Analytics Challenges

Big data analytics does not just come with wide-reaching benefits, it also comes with its own challenges:

  • Accessibility of Data: With larger volumes of data, storage and processing becomes a challenge. Big data should be maintained in such a way that it can be used by less-experienced data scientists and data analysts as well.
  • Data Quality Maintenance: With high volumes of data from disparate sources and in different formats, the proper management of data quality requires considerable time, effort, and resources.
  • 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 the wide range that is available in the market can be quite confusing. One should know how to select the best tool that aligns with user requirements and organizational infrastructure.
  • Supply-demand Gap in Skills: With a lack of data analytics skills in addition to the high cost of hiring experienced professionals, enterprises are finding it hard to meet the demand for skilled big data analytics professionals.

Comparing Big Data Analytics with Data Science

CriterionBig Data AnalyticsData Science
Type of Data ProcessedStructuredAll types
Types of ToolsStatistics and data modelingHadoop, coding, and machine learning
Domain ExpanseRelatively smallerHuge
New IdeasNot neededNeeded

Future Scope of Big Data Analytics

Big 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 enterprises have started making use of big data analytics to run their business operations.

Here is what the future will look like for big data analytics:

  • It is a golden time to witness tremendous growth in cognitive analysis.
  • Enterprises 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.
  • Enterprises 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. Enterprises have started strictly shifting their focus on the skill sets of an individual during the recruitment process and are gradually doing away with the traditional way of just looking at degrees.

As per LinkedIn, there are over 3,000 and 42,000 big data analytics job openings available in India and the US respectively.

Anyone with strong analytical and numerical skills will have good scope in the field. Some of the major job profiles in big data analytics are data architects, data analysts, database administrators, data scientists, data engineers, and statisticians.


Big data analytics is driving some of the biggest and best enterprises forward today. Enterprises that can convert data into information and information into insights are the ones that will own the future in a hypercompetitive world. For example, Uber disrupted the taxi-hiring business, and Airbnb disrupted the hospitality business. Both these companies 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 analytics techniques.

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