How big is it & how can your company benefit from it?

Big Data is one of the most popular buzzwords of the 21st century. It is reshaping how companies look at their data and what they chose to do with it. According to the Global Opportunity Analysis and Industry Forecast, 2021-2030, the global Big Data and Business Analytics market size is projected to reach $684.12 billion by 2030, growing at a CAGR of 13.5% from 2021 to 2030. But what is Big Data? And how can it help your business?

Data is at the core of every business, big or small. The 24/7 connected ecosystem across devices and a post-pandemic demand for e-commerce and Fintech have generated real-time data in huge volumes. Vast amounts of customer data and operational data are created at every point. This data is mission-critical in the digital transformation journey of businesses, as it drives insights and business value within a digitalised environment. It is helping businesses gain real-time insights for data-driven decisions. That’s why more companies are now quick to implement Big Data-driven use cases for intelligent reporting and analytics.

If you are wondering whether you should look at your business through the lens of a Big Data ecosystem, you are already on the right track. When done right, your investments in Big Data are worth it. They accelerate the change transformation of your company by solving your business challenges for a competitive edge in the industry. From discovering the operational silos in your logistics company to gaining insights on high-value customers and product categories for your e-commerce provider, there are countless ways to generate the right data and crunch the same for valuable insights.

Let’s explore how to use the data business-wise for various purposes.

Big Data Defined

Big Data refers to large datasets which contain structured, unstructured and semi-structured data and grow exponentially over time. Structured data refers to standardised data such as geolocation coordinates and credit card numbers, while unstructured data are typically text files, images, videos, social media feeds, etc. Examples of semi-structured data are web server logs and streaming data from sensors in an IoT network.

These datasets have a greater variety, volume and velocity as the data continues to be generated at high speeds from disparate sources and in multiple formats. Data must also be cleaned and organised to understand their veracity and business value.

How ‘big’ is Big Data?

The volume of Big Data depends upon the data processed by the organisation. Is the data live and streaming? How much unstructured data does it handle? And so on.

Although data is measured in ‘bytes’, it is only a fuzzy understanding of how ‘big’ the data can be: from tens of terabytes of data in one organisation to hundreds of petabytes in another!

One aspect remains. Regardless of the size or characteristics, datasets must be analysed computationally to reveal patterns, trends, and associations.

Business Values from Big Data

Big Data helps companies generate valuable insights, develop products, design marketing campaigns and make data-driven decisions for a business edge. The Big Data ecosystem also helps enterprises implement cutting-edge machine learning models and advanced analytics applications for continuous insights.

As Big Data technology can process large volumes of data within short curves, it has emerged as a new frontier for analysis and data modelling.

You may wonder what Big Data does for you.

Let us examine the business value Big Data helps you generate.

Businesses are becoming more and more data-driven. But are they prepared to handle this shift in complexity and volume of data sets? Do they consider what to do with all the data to maximise business value?

A well-strategised Big Data implementation plan is a critical element of the Big Data roadmap of an organisation. The data must lend itself to gaining insights and addressing business challenges for a higher ROI. To achieve the above objectives in the shortest curve, consider these questions before implementing any Big Data solution.

What data types to use to fulfill the organisational goals? Which data is unrelated to the purpose? What data to use or discard? How to clean the data and make it in a usable format? How will this data help achieve the objectives? Does the same align with the business goals and given resources? Consider these and other questions before implementing an appropriate Big Data solution.

Are all datasets suitable? Are they all valuable? And from where to get the data?

Not all datasets are available in a suitable format. The contextual aspect is foremost and requires merging internal data with external context to render it more useful for analysis. For instance, for a telecommunication network provider, the most important is customer experience. So only data relevant to the customer experience is gathered: time-series data of billing lifecycles, fulfilment touch points, subscription plans, data usage, location access, customer feedback, and so on.

Business types and industries differ in their challenges and end products or services. Different scenarios and digital transformation trajectories require customised deployment and solutions for successful outcomes. And each organisation must develop a clear scope of the business problem and expected outcome or revenue gain.

How does Big Data work?

A Big Data ecosystem integrates data, software applications, processing frameworks and tools that run on hardware capable of managing vast amounts of complex data. It includes collecting, processing, filtering/cleansing, and analysing extensive datasets throughout the Big Data management lifecycle. Organisations process the data to make sense, remove ‘noise’ and ‘outliers’ using several techniques and implement best practices for actionable insights.

These insights help businesses make predictions and model better products to maximise business value and improve customer experience. For instance, ecommerce providers use customer churn and cart abandonment rates to handle customer issues proactively, deliver personalised offers and drive revenues. Airlines look at various sectors to discover high profit-making destinations and target customers with promotional offers.

So how do you start using this technology?

A. To begin with, identify the business goal. What problems do you aim to solve? Are you planning to identify card fraud or create an insurance product?

B. Next, identify the critical data components that form part of the given Big Data environment. A customer’s credit card history: location, time of use, vendor or partner record, etc., form part of the Big Data strategy for mitigating card fraud.

C. Once you have discovered the data you need, identify the sources and methods of collection.

D. Next, lay out the roadmap for data cleansing, filtering, storage and processing. What tools to use for cleaning data? How to identify anomalies? Which database warehousing method to implement? What tools and applications to use for extracting and analysing data?

E. Train your application to understand the information you are extracting. It means translating it into a usable form of structured data. Some methods you may like to consider are text parsing, natural language processing (NLP), programming languages and developing content hierarchies via taxonomy.

Applications of Big Data in various industries

Most companies across various industries are working with Big Data every day. And the Big Data approach is driving analysis and decision-making from massive datasets. From driving operational efficiencies for sustainable business practices to leveraging predictive data modelling for superior products and services, Big Data applications are becoming more ubiquitous, from Location Intelligence, Disease Surveillance, Disaster Response, Election campaigns, and more.

Here are some popular examples of how Big Data is shaping businesses:

Social Media

One of Big Data’s most prolific use cases is in Social Media. The vast data from social media feeds, such as hashtags and user engagements, are used to understand customer sentiments. The insights gained power digital campaigns and target personalised ads for brand management.


The advertisements you see on Facebook are the result of microtargetting niche user profiles for engagement and sales. The promotions on Instagram and search engines are again Big Data at work. Netflix recommendations are another example where user data is collected and fed into their algorithms for a highly customised experience and customer satisfaction (CX).

Supply-Chain Management

Big Data supports superior logistics and eliminates wastage with data-driven insights. The application of Big Data analytics helps suppliers gain contextual insights across the supply chains for route analysis, and strategising warehousing and logistics. It also helps predict the production and shipment needs to ensure retailers have the right products, in the right volumes and at the right time.


Ecommerce companies such as Amazon collects vast amount of customer data: demographic profile, location, purchase behaviour, length of customer journey, cart abandonment rate and other metrics, to create highly-segmented user profiles for targeted marketing, cross-marketing and upsells for higher returns on marketing investment (ROMI)


The manufacturing sector generates vast amount of machine data, process data, sensor data, log book data and detailed information on outages. This Big Data helps support timely machine maintenance to ensure there is no loss of customers or revenue from wastage or operational silos. The data helps manufacturers keep track of their machine performance.

Challenges of Big Data implementation

Big Data is part of an organisation’s data ecosystem that handles large and complex data sets. It requires efficiency in data management and knowledge of Big Data software frameworks.

Some of the challenges of Big Data that companies face are:

  • Validating the integrity, accuracy and structure of the data
  • Storage within legacy systems and cloud
  • Processing: reading, transforming, extracting and formatting raw data
  • Maintaining security protocols
  • Fixing data quality
  • Handling missing values

At Algolytics, we help evaluate your Big Data solutions, and select the right Big Data frameworks for all your needs.

The Role of Algolytics

Today, an increasing number of companies in various industries are beginning to recognize the potential of Big Data in building their own competitive advantage. Don’t want to get left behind? Talk to us to define your Big Data strategy. At Algolytics, we have out-of-the-box Big Data and Machine Learning solutions that can back your business.

Contact us to accelerate the implementation of analytics solutions tailored to your business.

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