Big Data Analytics – What Is It and Why It Matters?

You probably know about the Apollo Guidance Computer, the computer used for the Apollo 11 Mission, which helped launch the astronauts to the Moon and back. But did you know that it had just 72KB as read-only memory (ROM)?

At that time (1969), being able to store 72KB of data was an incredible feat. Fast forward 50 years, humans generate more than 2.5 quintillion bytes of data per day. (One quintillion is 1,000,000 trillion)

Managing such colossal amounts of data is nearly impossible for traditional computers, let alone for humans. Thus enter big data analytics, a revolutionary method for organising and analysing massive sets of data.

What Is Big Data Analytics?

Types of big data. Image Credit:

Big data analytics refers to the usage of advanced analytical methods to collect, organise and analyse vast and diverse sets of data. Such datasets may include three types of data:

  1. Structured data has a fixed format and is generally be numerical. It is grouped into rows and columns and can be quantitative data such as age or mobile numbers.
  2. Unstructured data is unorganized and doesn’t have a predefined format. It can be anything, for example, it can be books (text), images, or videos.
  3. Semi-structured data can contain both structured and unstructured data.

Big data can be seen as a large and complex set of data stretching up to terabytes or zettabytes. This humongous volume of data cannot be captured, processed, or analysed using traditional relational databases or applications, and this is where big data analytics comes into play.

Big data comes from multiple sources, such as and grow exponentially in size with time.

And its applications are endless. Using big data analytics, researchers can predict the possibilities of terrorist attack occurrences and even determine the Facebook ads that you are more likely to click on.

Four Types of Big Data Analytics

Types of big data analytics. Image Credit:

Big data analytics helps in determining the “what, why, if and how” of events.

1. What Happened – Descriptive Analytics

It is the most basic form of analytics by which historical data is analysed and interpreted to better understand the changes that occurred. Almost every business intelligence tool relies on descriptive analytics and can be considered as the starting point of your analytics strategy.

Techniques like data mining and data aggregation are used for this type of analytics and help businesses understand what has happened, rather than make guesses. One simple example is the preparation of monthly profit and loss statements.

2. Why Did It Happen – Diagnostic Analytics

Diagnostic analytics is more sophisticated than descriptive and will allow analysts to identify the root causes of events. This analytics method primarily uses techniques like data mining, correlation, data discovery and drill-down, to determine what factors or circumstances led to an outcome.

For example, diagnostic analytics will help you understand why sales increased or decreased for a particular month. But just like descriptive analytics, diagnostic analytics also looks at historical data.

3. What Is Likely to Happen – Predictive Analytics

Predictive analytics determines what is likely to happen in the future and is all about forecasting. However, it doesn’t predict an event per se. Instead, it forecasts the probability of an event occurring.

A simple representation of how social media networks perform sentiment analysis. Image Credit:

One use case of predictive analytics is sentiment analysis, which collects and analyses an individual’s data from social media posts and interactions (existing data) and predicts whether the individual will be positive or negative towards a particular subject.

4. How to Make It Happen – Prescriptive Analytics

Prescriptive analytics is the most advanced level of big data analytics in that it allows you to determine how to make certain things happen by identifying trends, causation and correlations.

This means prescriptive analytics will examine “what has happened”, “why it has happened” and “what might happen” scenarios to determine the best actions to take.

Google Maps suggests multiple routes considering the distance, real-time traffic and the mode of transportation.

One such example is is the Google Maps app that suggests the best routes to take by taking into consideration the distance and real-time traffic conditions.

Read more about the 4 types of data analytics.

What Are the 3 Vs of Big Data?

1. Variety

Variety in big data refers to structured, unstructured and semi-structured data, collected from multiple sources. In the past, data could be obtained only in the form of spreadsheets and databases; but today, any kind of unstructured data such as images, audios or videos can be collected.

2. Velocity

Velocity in big data refers to the rate at which data is generated and collected. The flow of data is enormous and continuous and how fast the data is collected and processed determines its usability – the faster, the better. Every second, around 6000 messages are tweeted on Twitter.

3. Volume

The term big data itself signifies that the data collected is enormous. The volume of the data collected plays a crucial role in determining whether it will be valuable. And the volume of data determines whether it will be considered big data or not. Facebook alone generates four petabytes of data per day.

Why is Big Data so Important?

The incorporation of newer technologies such as artificial intelligence (AI), smartphones, social media networks and the Internet of Things (IoT) means that businesses or researchers need to deal with high volumes of data in various forms and from multiple sources.

With big data analytics, businesses, researchers, and analysts can make fast and accurate decisions that would have been previously been impossible. More precisely, big data analytics helps companies in the following ways:

  • It allows companies to offer better products and customer service.
  • It gives businesses a competitive edge over rivals.
  • It increases the effectiveness and reduces the costs of marketing.
  • It empowers businesses to gauge customer satisfaction and needs and release products accordingly.
  • It allows app creators to find the right set of target audiences and take proactive action to decrease churn rates.
  • It will enable human resource departments to find the right talent quickly by providing comprehensive data, collected from multiple sources.
  • It helps insurance agencies with fraud detection.
  • It allows the banking sector to determine the credit risks associated with individuals.

How Does Big Data Analytics Work?

Big data analytics companies will require stable storage infrastructures to handle a high volume of data. For this, a single server won’t do; instead, there must be clusters of hundreds or thousands of machines.

For this purpose, technologies such as Hadoop, Apache Spark, NoSQL databases, and data lakes are used. Once the needed infrastructure is set up, big data will go through four significant stages as follows.

1. Data Collection

The process of data collection will vary across different organisations. Both structured and unstructured data will be collected from multiple sources. For example, data can be obtained from mobile apps or even from IoT devices.

Some of the data collected will be stored in data warehouses, where business intelligence tools can access it. Unstructured or raw data that is too complex to be stored in warehouses will be stored in data lakes instead.

2. Data Processing

Once the data is accumulated, it must be organised to be utilised. One method to do this is to implementing batch processing, which looks at large blocks of data over time. This method is ideal if there is a longer turnaround time between the collection and analysis of data.

Another way to do this is through stream processing, which looks at small blocks of data at a time, significantly reducing the time between data collection and analysis. However, stream processing can be an expensive and complicated process as well.

3. Data Cleaning

The data collected may contain irrelevant or duplicated data, which can result in flawed insights. For that reason, any collected data is cleansed of redundancies and errors, and the entire data set is formatted.

4. Data Analysis

Once the data is cleansed, it is analysed using one or more analytics methods previously mentioned, which include:

  • Descriptive Analytics
  • Diagnostic Analysis
  • Predictive Analysis
  • Prescriptive Analysis

Other big data analysis methods include:

  • Data mining: This method sorts through large datasets by identifying anomalies and creating data clusters. This helps analysts identify patterns and relationships.
  • Deep learning/Cognitive analytics: This method uses artificial intelligence and machine learning to imitate human behavioural patterns. It is useful for finding patterns in extremely complex and abstract datasets.

5. Reporting and Data Visualization

Tabular report of hamburger preferences among Americans. Image Credit:

Once the data is analysed, you’ll be left with just numbers that may be difficult to understand or visualise, unless converted into reports. Reports help in understanding the effectiveness and return on investment (ROI) of a new venture, product, or marketing campaign, for instance.

Data visualization enhances the understandability of the analysed data. Image Credit:

Similarly, the data analysed may contain massive amounts of information, which can be made more comprehensible with the help of data visualisation. Data visualisation is the process of graphically representing information and data, in the form of graphs, charts or maps.

As humans are more attracted to colourful visual elements than bland tables, data visualisation is a faster way to convey information while still keeping an eye on the message. It is almost like storytelling and explaining the journey from point A to point B.

What are the advantages of big data analytics?

In addition to increasing decision-making and productivity, big data analytics provides the following benefits:

  • Informed decision-making: Companies can make better decisions based on data extracted from big and diverse information sources.
  • Operational efficiency: Big data analytics can enhance a business’s efficiency by detecting bottlenecks, streamlining operations, and reducing resource waste.
  • Enhanced customer experience: A deeper understanding of consumer behaviour and preferences enables businesses to provide a more tailored and personalised customer experience, increasing their satisfaction and loyalty.
  • Innovation: Big data analytics offers new insights into market patterns, allowing businesses to uncover potential for new goods or services.
  • Competitive advantage: Corporations can stay ahead of their competition by leveraging big data analytics to monitor market trends, make more informed business decisions, and respond to consumer demands more quickly and frequently.

What are the Applications of Big Data Analytics?

1. Banking

Banking Applications of Big Data Analytics
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Big data analytics helps the banking sector detect fraud early, report credit risk and prevent anti-money laundering. For example, the Securities Exchange Commission (SEC) uses big data to monitor the market and detect illegal trading activities.

2. Healthcare

Health Applications of Big Data Analytics
image credit: Unsplash

The healthcare industry generates a substantial amount of data. As such, big data analytics in healthcare is useful for delivering personalised medicine and identifying patterns such as drug side effects.

With the incorporation of wearable devices in the industry, data is being generated at an exponential rate, far beyond the comprehension of traditional computing. Using geographical and historical data sets, predictive analytics makes it possible to predict diseases that will affect specific locations.

Read more about big data analytics in healthcare.

3. Transportation

Transportation Applications of Big Data Analytics
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Big data analytics is extensively used by both public and private sectors for route optimization and traffic control. For example, Uber uses big data to track and analyse the services that the users use the most. Uber also uses it to change cab fares, depending on the demand and supply of its ride services.

4. Retail

Heatmaps indicating the most visited aisles of a retail store. Image Credit:

Retailers like Walmart use big data and data mining to deliver personalized product recommendations to their customers. They also use Wi-Fi technology to track the location of customers inside the store and determine the aisles that users visit the most.

They also monitor what customers are saying about their brand i on social media networks, and tweak their marketing strategies accordingly.

5. Manufacturing

Manufacturing Application
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In the manufacturing industry, big data can be used for supply planning and to test and simulate new production processes. It will also allow companies to increase efficiency by monitoring product quality and improve sales by studying customer needs.

Big data analytics is revolutionizing the manufacturing industry by enhancing operational efficiency, product quality, and decision-making.

Predictive maintenance enabled by data analytics allows manufacturers to foresee equipment failures and schedule preemptive repairs, minimizing downtime and operational costs.

Supply chain operations are optimized through real-time data analysis, improving inventory management and production scheduling, and enabling manufacturers to swiftly adapt to market changes or supply disruptions.

Additionally, quality control is significantly advanced as data-driven insights help pinpoint production anomalies early, ensuring product consistency and customer satisfaction.

Energy consumption is also optimized through strategic management based on analytics, leading to cost reductions and sustainability improvements.

Furthermore, manufacturers are using big data to offer customized products tailored to customer preferences, enhancing market responsiveness and competitiveness.

Safety in manufacturing environments is bolstered by predictive analytics that identify potential hazards, improving working conditions.

Additionally, real-time tracking through data analytics provides enhanced visibility across the manufacturing process, improving logistical efficiency and reducing errors. Overall, big data not only streamlines manufacturing processes but also drives innovation and product development, aligning closely with evolving market demands and consumer preferences.

6. Entertainment

Entertainment Application
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OTT platforms like Netflix collect user data to determine the likes and dislikes of their users, helping them deliver personalised content recommendations – all made possible by big data analytics.

These platforms take into account multiple data sets such as search history, watch time, how often a user pauses or stops the movie, and ratings.

7. Marketing

Marketing Application
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Big data analytics allows marketers to gain a 360-degree view of their audiences. They can determine which content is more effective at a particular stage of a sales cycle and gain actionable insights to enhance customer acquisition strategies.

Also, contextual marketing, a method by which targeted ads are served based on a user’s recent search history, is made possible with big data analytics.

8. Education

With large numbers of students with different specialisations and performances, the “one-size-fits-all” approach simply no longer works. This is where big data analytics in education can make all the difference.

By aggregating student data, analysing it for improvement and delivering actionable insights to teachers, the education system will be able to vastly improve the education of different students, providing a more personalised learning experience that can help reduce dropout rates.

Read more about big data analytics in education.

Data Analytics Training 

Organizations should invest in data analytics training as it significantly boosts their capability to harness data for operational and strategic benefits. 

With the vast amounts of data generated in the digital age, having a workforce skilled in data analytics means that companies can better identify trends, optimize processes, and tailor services or products to better meet customer needs. 

This investment in training not only enhances individual employee skills but also contributes to the organization’s overall data literacy. It enables more cohesive and informed decision-making across the board. 

As a result, businesses that prioritize data analytics training are often better positioned to innovate and maintain a competitive edge in their respective industries.

Data Is Knowledge

Data is knowledge, and knowledge is power. As such, big data analytics can help organisations make data-driven decisions and use it, for instance, to understand why some customers behaved in a particular way and why some didn’t.

The usefulness of big data depends on how it is collected, processed and analysed. When done correctly, businesses will be able to effectively pinpoint the needs and wants of their customers and, thus, gain a competitive advantage over their peers.

Big data analytics also allows companies to improve the quality and efficiency of their products, foresee demands, and streamline their supply chain processes accordingly. Powered by data, businesses can make precise decisions the organisation as a whole.