Tech Tidbits: What is Big Data?

How much data do you currently have in your personal disposal? Do you have to purchase additional storage to store all of your digital assets?

Imagine that problem times 100, or even more, for businesses. As data doubles in size roughly every two years1, it has expanded beyond what the office’s typical storage or virtual database can contain. Hence, the term “big data” was born.

This raises the question of how to be effectively managed, protected, and utilised in businesses. Without proper solutions, many would risk losing their valuable assets and not fully leveraging the data potential to make more impactful decisions.

This article explains big data’s key characteristics, showcases available storage solutions, and defines steps to building a company culture that thrives on information-based decisions.

What is big data?

Big data refers to absurdly massive amounts of structured and unstructured data, reaching petabytes (1024 terabytes or a million gigabytes) or exabytes (one billion gigabytes), and it needs specialised processing methods2.

Read more: Big Data in Manufacturing

Structured data refers to any information that follows a standardised format and is searchable and analysable, like each row in a spreadsheet file. In contrast, unstructured data does not follow any conventional data models and takes on various forms (e.g., text, images, audio, videos, etc.), making it extremely difficult to manage and process.

Regardless of their forms, any piece of data is valuable as it contains insights that are useful for the decision-making process.

Traditional data vs Big data: Key differences

Traditional data management depends on structured, relational databases to store information in organised records, files, and tables. We often encounter traditional data in our daily operations and standard reporting.

Big data, on the other hand, brings a more dynamic schema (the data “blueprint” that defines how data is organised and related). This means that big data can be “noisy” and requires specialised expertise to process and understand.

In general, the differences between traditional and big data can be summarised as the following:

Traditional data

Big data

Volume

Smaller, more manageable datasets

Easily stored and processed using typical database systems

Massive datasets

Too large for conventional solutions to handle

Requires distributed storage and processing

Variety

Comprises of structured and organised data (e.g., tables, rows, columns, etc.)

Comes from relational databases and spreadsheets

Comprises of structured, unstructured, and semi-structured data

Comes from a variety of sources

Velocity

Periodically generated and updated

Batch processing

Continuously generated

High-speed processing

Complexity

Simple to manage and analyse

Processed using standard tools and techniques

Complicated to manage and analyse

Processed using specialised tools and frameworks (e.g., Hadoop, Spark)

Examples of big data across different industries

Healthcare businesses use big data analytics to analyse historical data to develop precise disease diagnoses and treatment strategies, which helps reduce unnecessary procedures and examinations.

The retail giant Amazon collects massive amounts of data about customer purchases, delivery methods, and priorities. This enables them to provide hyper-personalised recommendations for shoppers. Likewise, Netflix and Spotify are great examples of how the entertainment industry uses big data analytics for personalised content recommendations.

Banks and financial institutions leverage big data for fraud detection and risk management. They monitor credit card holders’ purchasing patterns to flag suspicious transactions and analyse operational processes to improve efficiency.

Weather forecasting has also evolved through big data. Meteorologists analyse satellite data and sensor information to study natural disaster patterns and create accurate weather predictions.

Government agencies like the IRS and Social Security Administration use big data analytics to spot tax fraud and scam disability claims.

What are the benefits of big data?

Big data combined with advanced data analytics provide organisations with valuable patterns from vast datasets that lead to more precise measurements and predictions that shape strategic choices.

Big data analytics can help businesses:

Create individual-specific shopping experiences based on purchase history

Develop targeted marketing campaigns

Track customer behaviour patterns

Create proactive customer service solutions

Enhance the supply chain by:

Live monitoring of supplier data

Preventing equipment failures through predictive maintenance

Optimising inventory level

Analysing transportation pattern analysis

Forecasting customer demand

Enhance financial processes by:

Identifying unnecessary spending

Better allocating resources

Optimising return on investment

Identify market gaps and emerging trends

Better meet consumer needs with improved product designs thus increasing customer lifetime value

Assess risks to create resilient controls and mitigation strategies

Automate repetitive tasks

Reduce operational costs and maximise profits

Adhere better to changing regulatory requirements

How is big data stored and processed?

Storing and processing big data requires a strong and special architecture with multiple components.

Core storage components

Big data storage infrastructure needs to have several essential elements:

Distributed file systems: Using solutions like Apache Hadoop Distributed File System (HDFS)3 to store and manage large datasets in distributed environments. These systems grow horizontally when you add more nodes to increase capacity.

NoSQL databases: Platforms like MongoDB group documents into collections with key-value pairs.

Data lakes: Extremely large repositories that allow organisations to store raw data in its original format to keep data integrity for future analysis.

Processing frameworks

Several techniques and frameworks can be used to process big data:

Batch processing

Handles static data sources through long-running jobs

Filters and combines data for analysis

Prepares data through transformation operations

Real-time processing

Analyses data streams as they arrive

Gives immediate insights from incoming information

Uses stream processing technologies for continuous data handling

Architectural approaches

Two main architectural patterns exist for big data processing:

Lambda architecture: This approach creates two paths for data flow:

Batch layer (cold path) keeps raw data and runs batch processing

Speed layer (hot path) analyses data instantly

Raw data stays unchanged so systems can recompute as they evolve

Kappa architecture: A simpler option that:

Processes all data through one path

Uses stream processing systems

Makes maintenance easier and reduces the complexity

Processing optimisation

Several methods boost big data processing efficiency:

In-memory computing: Data stored in RAM instead of disc runs much faster and enables up-to-the-minute data analysis.

Data sharding: Large databases split into smaller pieces allow parallel processing and better performance.

Indexing: Better indexes speed up query responses and make data retrieval faster.

Infrastructure models

Big data infrastructure can be set up in various ways:

On-premises: The organisation’s data centres give complete control over infrastructure.

Cloud-based: Infrastructure as a service (IaaS) provides adaptable, managed resources through third-party providers.

Hybrid: A mix of on-premises and cloud resources balances control with adaptability.

The best big data storage system handles unlimited data volumes, manages high rates of random writes and reads, and works well with any data format. These systems solve volume challenges by spreading data across cluster nodes through distributed, shared-nothing architectures.

Choosing the right storage solution for big data

Insanely large data volumes require an equally insanely large storage. Thanks to technology advancements, there are more than a handful of solutions available for businesses to consider. Each solution detailed in the following section comes with unique advantages that depend on the business’s needs and operational limits.

Data lakes vs data warehouses vs data marts

Data lakes work as huge repositories for raw data in its native format. Organisations can store unstructured, semi-structured, and structured information without size limits. These repositories support many analytical approaches, from machine learning to visualisations. They work best for experimental analytics and finding new patterns in data.

Data warehouses combine data from multiple sources into one central repository that unifies data quality and format. Using extract, transform, and load (ETL) processes, data warehouses clean and structure information before storage with a ‘schema-on-write’ approach. This well-laid-out environment helps with:

Business intelligence activities

Performance-intensive reporting

Historical data analysis

Strategic decision-making

Data marts act as smaller versions of data warehouses and contain specific data slices for particular business functions or departments. These specialised repositories offer:

Department-specific analytics

Local data access

Focused business insights

Simplified processes

Check out the below table for a summary of each data storage option:

Features	Data lakes	Data warehouses	Data marts
Purpose	For exploration and future analysis	For reporting and business intelligence	Focused on a specific business unit or function.
Data type	Raw Unstructured Semi-structured And structured data	Structured Filtered And processed data	Structured Summarised And relevant to a specific department
Data processing	Schema-on-read (data is processed when it’s needed for analysis)	Schema-on-write (data is transformed and structured before being loaded)
Users	Data scientists Analysts And business users who need to explore raw data	Business analysts Executives And report users	Specific department users (e.g., marketing, finance, sales)
Flexibility	Highly flexible, can handle diverse data types and evolving business needs	Less flexible, designed for specific reporting and analysis requirements	Less flexible, designed for specific departmental needs
Cost	Relatively low storage cost due to raw data storage	Higher storage cost due to data transformation and structuring	Moderate storage cost
Data governance	Requires robust data governance to manage diverse data and ensure quality	Strong data governance is essential for data consistency and accuracy	Strong data governance is important but limited to the scope of the mart
Data source	Diverse sources, including IoT devices, social media, log files, databases	Relational databases, transactional systems, operational systems	Data warehouse, operational systems
Example use cases	Machine learning, data discovery, exploratory analysis, building data science models	Business intelligence, reporting, dashboards, historical analysis	Departmental reporting, specific analysis, performance monitoring

Cloud vs on-premise storage options

An organisation’s choice between cloud and on-premise storage substantially affects its data management capabilities. Cloud storage lets businesses keep data on remote servers, which offers flexibility and a lower upfront cost as there is no hardware investment required.

On-premise storage keeps data within an organisation’s physical infrastructure and provides direct control over security and compliance. This approach works best for businesses that handle sensitive information or must follow strict regulations.

On-premise solutions demand big upfront investments and ongoing maintenance costs. However, they remain the top choice for organisations in regulated sectors, defence contractors, and healthcare providers.

Key factors to consider when choosing big data storage solutions

Organisations should carefully evaluate the following key factors to choose the best possible solution:

Data volume and type:

Does your business have any specific requirements for data storage?

What is the current proportion of structured vs unstructured data? How will this impact the solution choice?

How does the business typically analyse data? What are the typical analytics workload patterns in the business? How will they influence the system’s capabilities?

Security requirements

Are there any specific regulatory and compliance requirements that the business must follow?

What are the sensitivity levels of the data?

Who should be allowed to access the data? What other access control systems are required?

Performance considerations

What is the required speed for big data processing?

What level of latency tolerance can the business accept?

What is the company’s available bandwidth, and how will it impact data processing and transfer?

Cost implications

What are the available investment funds for implementing a new solution?

What are the operating budget limits for implementation and ongoing maintenance and support?

What is the business growth plan?

The right storage solution shapes how an organisation can effectively manage and analyse big data. A careful review of business needs, technical limits, and plans for the future helps organisations select solutions that match their goals and operational needs.

From raw data to informed decisions

Big data marks a radical alteration in the way organisations handle, process, and utilise information, transforming massive data quantities into practical insights. To effectively handle big data needs, well-laid-out storage solutions plus reliable security measures, quality control methods, and detailed protection practices to ensure privacy and accuracy.

At TRG International, we offer Infor OS, the next-gen enterprise operating platform, which lays a strong and secure foundation for business software. Infor OS provides a multitude of built-in functions, one of which is Infor Data Fabric—an advanced repository for every data need. Together with Infor OS, the suite of solutions enables businesses just like yours to optimise operations, automate manual tasks, and leverage the latest innovations with just a few clicks.

Sources:

https://www.coursera.org/articles/5-vs-of-big-data
https://www.geeksforgeeks.org/top-7-big-data-applications-with-examples-in-real-life/
https://codilime.com/blog/big-data-infrastructure-essentials-and-challenges/