Big Data

What is Big Data?

Big Data is the enormous amount of structured, semi-structured, and unstructured data that organizations collect to analyze and extract meaningful information and insights. This term has been in use since the 1990s. This huge data collection is believed to be complex and can’t be managed or analyzed using traditional data processing tools, such as spreadsheets anymore. It includes structured data like bank and financial transactions to unstructured data like social media posts or YouTube videos, making it all the more difficult to deal with simple processing tools.

With technological advancements, storage and compute costs have been reduced drastically leading to an increase in the storage of extensive data sets. The size of Big Data is believed to be increasing with every passing year, helping organizations to make more accurate business decisions based on their data. Let us now understand why Big Data differs from traditional data.

Differences between Big Data and Traditional Data

The major difference between Big Data and traditional data and their analysis lies in the type of data the organizations handle and the tools they use to analyze it.

Traditional Data

Traditional data contains structured data that is typically stored in relational databases. The relational databases organize data in such a way that it is easily understood by the computers. The traditional data is analyzed using statistical methods such as structured query language (SQL).

Big Data

On the other hand, Big Data contains massive sets of structured, semi-structured, and unstructured data. As the data is available in various formats and complex in nature, it requires more sophisticated analysis techniques. Advanced techniques like machine learning and data mining are used to extract information from complex data sets. Further, the volume of data is so huge that it requires distributed processing systems such as Hadoop to manage it.

The Evolution of Big Data: Timeline of Crucial Developments

Big Data, as a concept, has evolved significantly over the decades. This timeline highlights all the important milestones of development from early statistical data analysis to the sophisticated Big Data tools and techniques used today

What are the Types of Big Data?

Earlier, the data stored in databases and warehouses was structured which could be easily understood by computers. However, as Big Data evolved, various types of data came into existence. In 2023, almost 80 to 90% of data stored was unstructured.

Primarily based on their structure and feasibility to index, data is classified into three – structured, semi-structured, and unstructured.

Structured Data: Highly organized structured data is easily stored, accessed, and analyzed in relational databases due to its clearly defined elements.

Unstructured Data: Unstructured data’s varied formats and lack of schema make it complex to analyze using traditional methods.

Semi-Structured Data: Semi-structured data combines structured data with partial schema and unstructured data with metadata, offering schema flexibility and nested structures.

Structured Data

Structured data is the easiest form of data to store and access. As the data is highly organized, it can be stored in relational databases or spreadsheets. Retrieval of data is also simple as each element is clearly defined and can be accessed from a specific field in a record or file. Database designers and administrators can define simple algorithms for search and analysis of such data. The following are a few structured data samples.

• Customer data in a customer relationship management (CRM) system
• Transactional data in financial records
• Employee data in human resources databases
• Inventory records
• Retail loyalty program data, etc.

Structured, though already existing in enormous quantities, doesn’t qualify as Big Data as it doesn’t meet the criteria of Big Data alone.

Unstructured Data

Unstructured data is quite complex and difficult to collect, store, process, and analyze using traditional databases as they don’t have defined or rigid schema. They are found in variety of formats and hence, sophisticated methods of indexing, searching, and analyzing are required. Some of the examples of unstructured data are:

• Text formats, such as documents, emails and social media posts
• Multimedia content, including images, audio files and videos
• Data from IoT devices
• Open-ended customer comments, etc.

Unstructured data has lot of value for extracting meaningful information, as such there was never a question regarding using it for deriving insights. However, using traditional tools for collecting, storing, and analyzing was becoming a difficult task. Sometimes, even the results became obsolete by the time unstructured data was analyzed and delivered. With technology advancements, unstructured data is now stored in data lakes, data warehouses, and NoSQL databases.

Semi-Structured Data

Semi-structured data, as the name suggests, lies between structured and unstructured data formats, where partial data is having rigid schema and can be organized easily, and the rest is completely unstructured and difficult to organize in relational or traditional databases. Semi-structured data features schema flexibility, hierarchical or nested structures, and contains tags or metadata. Examples of semi-structured Data are:

• JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) files
• HTML (Hypertext Markup Language) files
• IoT sensors
• Webpages
• Emails, etc.

This type of data structure is more flexible than structured data and easier to analyze than unstructured data. NoSQL databases can store and manage semi-structured data efficiently.

What are the Examples of Big Data?

Big Data is generated from a vast range of sources across different industries and domains. These sources include everything from CRM systems to social media platforms. Below is a categorized list of common Big Data sources along with their descriptions.

What are the Sources of Big Data?

Technological advancements have also led to a tremendous increase in the sources of big data. Every piece of data, whether from a satellite or a simple home toaster, has its importance and plays a crucial role in arriving at data-backed decisions. However, for convenience, it is categorized into three main types

Social Data

Social data comprises all the data collected from different social media platforms, including social posts, images, comments, videos, etc. “With the growing reach of 4G and 5G networks to the remotest areas, almost 3.3 billion people have accessed video content on mobile phones in 2024.” (Source – Vidico) With the increasing usage of different social media platforms, the amount of social data will also grow exponentially, turning it into a consistent source.

Machine Data

The data that is generated by IoT devices and machinery equipped with sensors is called machine data. The sensors assist businesses in collecting and processing data from vehicles, machines, devices, and industrial equipment. Machine data is collected from a wide variety of sources such as traffic monitors, security systems, etc. ” By 2025, the data volume created by IoT connections is projected to reach a massive total of 79.4 zettabytes.” (Source – Statista)

Transactional Data

The third type of data is transactional data that is generated through financial and commercial transactions. A few examples of such transactional data are commercial online purchasing, banking activities, etc. Transactional data is said to have semi-structured data possessing images, customer comments, etc., that make it complex to manage and analyze.

Why is Big Data important?

Big data plays a significant role in businesses as it helps organizations derive meaningful insights from vast amounts of information. Artificial intelligence (AI), machine learning (ML), and advanced database technologies help organizations analyze Big Data in real-time and provide essential insights. It helps them to make data-driven business decisions, optimize operations, and attain growth.

The importance of Big Data can be easily understood from the quote of Geoffrey Moore, a well-known author and management analyst, “Without Big Data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” The quote summarizes the crucial role of Big Data in gaining competitive edge and driving innovation across various sectors.

How Does Big Data Work?

Organizations need to focus on how Big Data is flowing through various sources, systems, owners, and users, before even considering utilizing it. They need to follow 5 key steps prior to putting the Big Data to use, which contains structured data along with unstructured and semi-structured data.

Develop a Big Data Strategy

Developing a Big Data strategy is the first and most important step as it assists organizations in understanding how to collect, store, manage, share, and use Big Data within and outside an organization. This is very crucial step as it lays groundwork for business success in this digital era. While designing Big Data strategy, companies should always focus on the present and future business objectives and align with them. They should always treat Big Data as a valuable asset than just a byproduct of their applications.

Identify Big Data Sources

Data flows in from various sources such as IoT devices, equipment, or even cars and wearable devices. Organizations must analyze data that flows in real-time to check which data is valuable, which can be discarded, and which set needs a deeper analysis. Social data generated from social media platforms contain valuable data in the form of images, videos, etc. This unstructured or semi-structured data is particularly important for marketing, sales, and customer support teams, but has its own analysis challenges. Some parts of data can also be derived from publicly available Government data portals. Data can also be collected from other essential sources such as data lakes, cloud-based platforms, data from customers and suppliers, etc., to achieve business growth.

Access, Manage, and Store Big Data

Advanced computing technologies available today assist in accessing large and varied sets of data quickly. However, apart from accessing, organizations must establish systems for data integration, building data pipelines, ensuring quality, enforcing governance, and preparing data for analysis. Further, data can be stored in different types of storage mediums, such as traditional data warehouses, modern data lakes, cloud-based solutions, etc, and tools like Hadoop provide flexible and cost-effective storage options.

Analyze the Data

The next important step for businesses is to analyze the available Big Data which can be achieved leveraging high-performance technologies like grid computing and in-memory analytics. They can also pre-select data to focus on in future. Irrespective of the approach, analyzing Big Data is very essential as it provides meaningful insights and drives decision-making. It also powers advances analytics like machine learning (ML) and artificial intelligence (AI), further leading to business success.

Make Intelligent and Data-driven Decisions

Data that is well managed and trustworthy leads to equally reliable analytics and informed decision-making. In today’s digital landscape, businesses must harness their wealth of data to arrive at data-driven decisions than make business decisions based on intuition. Data-driven organizations always perform better than their competitors, achieve greater operational predictability, and enjoy increased profits.

How is Big Data stored and processed?

Storing Big Data in an easily accessible manner is very important for business success. There are many ways in which data can be stored. Let’s have a look at a few of them.

Data Lakes

Big Data is often stored in data lakes which allows storing all types of data built on Hadoop clusters, cloud storage services, NoSQL databases, or other Big Data platforms. Data lakes are more flexible than data warehouses that store only structured data in relational databases. These data lakes are flexible and store unstructured and semi-structured data as well.

Distributed Architecture

In a few organizations, Big Data environments combine different systems in a distributed architecture, where there may be a central data lake established and integrated with relational databases or data warehouses. The data available in such systems may be raw and be further filtered or organized based on the requirement, such as Business Intelligence (BI). Or data may be pre-processed with data mining and preparation tools for regular application use.

Cloud-based Big Data Systems

In a few organizations, Big Data is processed using clustered systems as it requires significant computing power. Technologies like Hadoop and Spark distribute workloads across multiple servers. Cloud-based Big Data systems are popular as they are cost-effective. Further, they also allow organizations to scale compute power based on requirement, paying only for the storage and compute power used. They also have the flexibility to turn off the instances when not in use.

Characteristics of Big Data – Knowing the Vs

Big Data doesn’t refer to large sets of data as commonly thought. There are certain characteristics that the data needs to fulfil to be called Big Data. The following are the set of characteristics that Big Data must meet, popularly known as Vs.

Volume

Size does matter! Volume is the first key feature of Big Data. Enormous sets of data must be stored securely by organizations so that they can be analyzed using advanced algorithms and AI-driven analytics.

Velocity

Earlier, data used to be entered into systems manually. However, Big Data technologies allow real-time processing and analysis, that too in milliseconds. This allows businesses to respond quickly, capitalize on financial opportunities, meet customer requirements, prevent frauds, and do more.

Variety

Big Data comprises of variety of data collected from diverse sources. It contains structured, unstructured, and semi-structured data and can’t be stored in traditional databases.

Veracity

Data is valuable only if it is accurate, relevant and timely. Traditional databases posed syntactical errors at times, and the new unstructured data brings more sophisticated challenges, such as human bias, social noise, and data provenance concerns, that affects the overall data quality. Modern databases offer flexible solutions and take care of all such concerns.

Value

Big data is said to be valuable when it provides insights that help organizations stay ahead of the competition, become resilient, and enhance customer service. Modern technologies have made it easy to analyze data and enhance operational efficiencies. The value of big data also helps in understanding how data types and formats evolve within Big Data.

Variability

Big Data includes variability aspect where data types, formats, and sources change over time. As the structure and quality of data change, businesses would be required to adapt to the evolving nature of data by enhancing their processes and analytics methods.

What are the Uses of Big Data?

Data analysts examine relationships between various data types, such as demographic data and purchase history, to identify correlations. These assessments can be performed in-house or by third parties specializing in processing Big Data into actionable insights. Businesses use this analysis to turn Big Data into valuable information for decision-making, benefiting multiple departments, from HR to marketing and sales. The main goals of Big Data are to accelerate product time-to-market, reduce the resources required for market adoption, target the right audiences, and drive customer loyalty.

What are the Benefits of Big Data?

Modern Big Data management solutions enable companies to transform raw data into actionable insights with great speed and accuracy. In 2024, the global Big Data and Analytics market was valued at over $348 billion, reflecting its critical role in business operations. (Source: Exploding Topics)

• Product and Service Development:

  Big Data analytics allows product developers to analyze unstructured data, such as customer reviews and cultural trends, and respond quickly.

• Predictive Maintenance:

  In an international survey, McKinsey found that the analysis of Big Data from IoT-enabled machines reduced equipment maintenance costs by up to 40%.

• Customer Experience:

  In 2024, Gartner identified that companies prioritizing customer experience are more actively collecting and analyzing customer feedback compared to those that are not.” (Source: Superoffice) Analyzing this Big Data allows businesses to improve and personalize their customers’ experience with their brand. In addition to Big Data, CX teams are increasingly taking “thick data” into account. These qualitative insights into customers’ observations, feelings, and reactions are enhancing Big Data and giving companies a more comprehensive understanding of their customers.

• Resilience and Risk Management:

  The COVID-19 pandemic was a sharp awakening for many business leaders as they realized just how vulnerable their operations were to disruption. Big Data insights can help companies anticipate risk and prepare for the unexpected.

• Cost Savings and Greater Efficiency:

  When businesses apply advanced Big Data analytics across all processes within their organization, they are able to not only spot inefficiencies, but to implement fast and effective solutions.

• Improved Competitiveness:

  The insights gleaned from Big Data can help companies save money, please customers, make better products, and innovate business operations.