Mastering the Power of Big Data Analytics


  • Big data analytics uses huge, varied data sets for strategic insights.
  • Your data infrastructure is often different when handling big data (see 5Vs)
  • Offers benefits like better decisions and customer service.
  • Faces challenges like privacy concerns and skill shortages.
  • A career in the field requires math, coding, and big data tool skills.

What is Big Data Analytics?

Besides being a buzzword often used by managers and executives, big data analytics examines large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other helpful information.

Big data analytics is transforming the way organisations operate and make decisions. By increasing their capabilities to collect, process and analyse large amounts of data, businesses can uncover patterns, correlations, and other insights to help drive better-informed strategic decisions. Let’s look at the world of big data analytics.

What Makes it “Big” and What is the Difference to Data Analytics

The distinction between “Big Data” and “Data Analytics” is central to handling, utilising, and interpreting large volumes of information. Big Data signifies enormous volumes of data that exceed the capabilities of typical data processing software. This data can manifest in structured forms, such as:

  • Numbers and Dates
  • unstructured formats like text and images
  • semi-structured documents, as seen in XML files.

To clarify the concept of big data, let’s consider your monthly financial management as an example. Here is the process you can follow:

  • You review your bills
  • make the necessary payments
  • mark them with the current date. 

With limited scale and complexity, this simple method suffices.

However, this scenario changes dramatically when managing a multinational corporation’s finances. The sheer volume and intricacy of paperwork would demand an evolved strategy for data processing. This is where the “big” in big data comes into play. You are forced to upscale your data management to cope with the growing amount of data, as illustrated by the concept of the 5 Vs (below)

On the other hand, Data Analytics is the process of inspecting, cleansing, altering, and transforming data to extract valuable insights, draw inferences, and facilitate decision-making. Data analytics can be used on smaller data sets, vital to handling big data. Big data without effective analytics is merely a pile of information lacking guidance or objective. With efficient analytics, the data can be transformed into actionable insights.

The Five Vs

To identify “big data,” engineers will often look at characteristics known as the 5Vs

  • Volume: The amount of data being handled matters. This is a crucial delineator between data and big data. With all the internet browsing, social media posting, and the Internet of Things gadgets we use daily, we create a massive amount of data every second, which is only expanding.
  • Velocity: The speed at which new data is generated and moves around. With the growth of the Internet of Things, data is streamed at an unprecedented rate, necessitating real-time processing and analysis.
  • Variety: Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data, and financial transactions.
  • Veracity: Refers to the trust a business can place in the data’s accuracy. More accurate data means leaders can make more confident decisions.
  • Value: There are challenges in setting up the proper infrastructure for big data, so all this data needs to be transformed into something valuable that directly impacts the business to justify the time and effort.

A 6th V is sometimes mentioned, Variability, which describes the changing meaning of the data, a topic that is often important in

Benefits of Big Data Analytics

  • Improved Decision Making: Big data analytics enables businesses to make data-driven decisions. It provides insights based on data analysis, leading to more informed and effective decision-making.
  • Real-time Insights: Businesses can gain real-time insights into their operations. This can help identify issues and opportunities as they occur, enabling immediate action.
  • Enhanced Customer Experience: By analysing customer data, businesses can better understand their customer’s needs and preferences, leading to better product recommendations and personalised customer service.
  • Cost Efficiency: Big data tools like Hadoop and cloud-based analytics can bring significant cost advantages when storing large amounts of data. Furthermore, they can identify more efficient ways of doing business.
  • Risk Management: Big data analytics can identify hidden patterns and correlations in data that can help organisations detect fraud and mitigate risks.
  • Product Development: With insights gained from big data analytics, companies can create products tailored to customer needs and market demand.
  • Competitive Advantage: Companies that leverage big data analytics often have a competitive edge over others because they can make quicker and more informed decisions.

Challenges of Big Data

  • Data Privacy and Security: As we’re amassing and dissecting more data than ever, there’s a growing wave of concern from the public and advocacy groups about how we keep that data secure and respect privacy. Making sure we gather, store, and analyse this information in a way that keeps privacy intact is a substantial hurdle we need to overcome.
  • Data Quality: Poor data quality can lead to inaccurate analysis and decision-making. Ensuring the data is accurate, consistent, and clean is challenging.
  • Lack of Skilled Personnel: There is a high demand for professionals with skills in big data analytics, but there is a shortage of such professionals in the market.
  • Data Integration: Big data comes from various sources in different formats. Integrating this data and ensuring it can interact is a challenging task.
  • Storing Large Amounts of Data: Companies are now gathering data at a skyrocketing rate, bringing its own challenges. Ensuring this ever-growing mountain of information remains safe, easy to scale up, affordable to store, and readily available for use is no small feat.
  • Understanding the Data: More than simply having data is required. Businesses need to understand the data and know what questions to ask to get the insights they need.
  • Regulatory Compliance: Numerous laws and regulations, such as GDPR and HIPAA, govern data collection and usage. Ensuring compliance with these laws while still maximising the value of the data can be a significant challenge.

History and Evolution of Big Data Analytics


Development of Relational Databases

The concept of relational databases was introduced by E.F. Codd while at IBM. This was a significant step forward in organising data in a structured way.


Google’s MapReduce

This programming model was instrumental in handling large-scale data processing. It laid the groundwork for big data analytics by showing how large datasets could be processed in a distributed and parallel manner.


Apache Hadoop

The Apache Software Foundation developed Hadoop, an open-source framework that allows for the distributed processing of large data sets across clusters of computers.


NoSQL Databases

The introduction of NoSQL databases provided a way to store and retrieve data that was modelled in means other than the tabular relations used in relational databases. This allowed for handling unstructured data, a significant part of big data.


Apache Spark

As an open-source, distributed computing system, Apache Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Apache Spark has become a popular framework for big data analytics due to its speed and ease of use.


Emergence of Data Science

Data Science uses various techniques and theories from mathematics, statistics, computer science, and information science to extract knowledge and insights from data. As a multidisciplinary field, it has contributed significantly to advancing big data analytics by enabling the efficient processing and analysis of large volumes of data to obtain valuable insights​.


How Big Data Analytics Works

Big data analytics typically involves four key steps:

  • Collect Data: Data from various sources is collected, including business transactions, social media, and information from sensors or machine-to-machine data.
  • Process Data: This involves processing large amounts of data in real-time or batch processing.
  • Clean Data: Data cleaning involves the removal of noise and inconsistencies in the data to improve its quality.
  • Analyse Data: Clean data is then analysed using statistical and mathematical models to find meaningful insights.

Sounds simple, right? Well, you need to consider the 5Vs mentioned above. Careful cross-functional planning and product management are required to ensure you have an appropriate technological solution for your use case. A company that needs to work with millions of photos in a cloud environment might have to approach big data differently than a manufacturing company with millions of supply chain transactional records being churned out of SAP.

Types of Big Data Analytics with Examples

Descriptive Analytics

This type of analysis answers the question, “What happened?” It includes simple operations like clustering, summarisation, and classification.

This is where you start by understanding what has already happened. For instance, a business might analyse data on income and manufacturing to understand what happened in each product category. This could involve examining the income per product category, revenue per month, and the total number of products produced in each category per month.

Diagnostics Analytics

It tries to answer the question, “Why did it happen?” which includes drilling, data discovery, correlations, and data mining techniques. This involves digging deeper to understand why something happened. For instance, a business might analyse why a certain profit was or wasn’t achieved, including an investigation into sales and gross profit data to gain a deeper insight into an event.

Predictive Analytics

Predictive Analytics tries to answer the question, “What is likely to happen?” It uses statistical models and forecasting techniques to understand future behaviour.

This involves using data to forecast what might happen in the future. For example, a business considering repositioning its brand might use predictive analytics by considering past events and related data, analysing what happened (descriptive analytics) and why it happened (diagnostic analytics), and then comparing different variables based on these analyses to check for the best-case scenario.

See this post about predictive analytics in supply chain.

Prescriptive Analytics

This type of analytics tries to answer the question, “What should we do?” It suggests decision options using optimisation

This involves using data to suggest a course of action. For example, a multinational company might want to identify opportunities based on past purchases. The company could analyse trends in assets or past sales reports, and then if they find negative results, they could take steps to eliminate the problematic factors in the future.

Big Data Analytics Tools and Technology

There are various tools and technologies available for big data analytics. Below is a list of commonly used descriptors and technologies:

  • Hadoop: An open-source software framework for storing data and running applications on clusters of commodity hardware.
  • Stream Analytics: Real-time analytics and complex event-processing platforms designed to analyse and visualise streaming data in real time.
  • Data Preprocessing Software: These tools help clean, transform, and manipulate data to improve its quality and usability.
  • Distributed Storage: A method of storing data across multiple nodes or devices to ensure data redundancy and improve access speed.
  • NoSQL Databases: Non-relational databases that are ideal for storing unstructured data.
  • A Data Lake: A storage repository that holds vast raw data in its native format until needed.
  • Data Integration Software: Tools that combine data from different sources and provide users with a unified view of the data.
  • Data Virtualisation: An approach to data management that allows an application to retrieve and manipulate data without requiring technical details.
  • In-memory Data Fabric: A distributed data management platform that boosts business performance and provides high-speed access to data.
  • Data Warehouse: A system used for reporting and data analysis, considered a core business intelligence component.
  • MapReduce: A programming model and an associated implementation for processing and generating large datasets with a parallel, distributed algorithm on a cluster.
  • YARN: A resource-management platform responsible for managing computing resources in clusters and using them for scheduling users’ applications.
  • Spark: An open-source, distributed computing system for big data processing and analytics.

How to Start Your Career in Big Data Analytics

Starting a career in big data analytics often requires the following: 

  • Strong foundation in mathematics and statistics
  • Functional skills in programming languages like Python or Java. 
  • Knowledge of big data tools and platforms like Hadoop, Spark, and others mentioned above is also crucial. 
  • Certifications in big data analytics can also enhance your credibility in the field. It’s a rapidly evolving field, and continuous learning is a must to stay updated with the latest technologies and techniques.

Wrapping up, the role of big data analytics in changing how businesses run and make decisions is a big deal. In fact, it’s like a treasure chest of insights and opportunities, however it takes work. Therefore, as your organisational needs continue to grow and shift, we’ll need more folks who know how to use big data analytics. In summary, if you’re a company trying to get the most out of big data or someone thinking about diving into this field, getting your head around the basics of big data analytics is where you start to unlock its magic.

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