Big Data vs. Data Analytics: What’s the Difference?

You may encounter the terms “big data” and “data analytics” used interchangeably — yet they do, in fact, have distinct definitions. Big data and data analytics both belong to the larger field of data science, responsible for driving the lion’s share of decisions made in businesses and other types of organizations operating today. 

The following sections aim to eliminate confusion around these terms by defining big data and data analytics, examining their differences and considering why both are vital to modern businesses and careers. 

What Is Big Data?

Every day, data is generated from data sources like sensors on machines, Internet of Things (IoT) devices, websites, apps, social media, healthcare, scientific research, and financial transactions. Big data refers to these enormous and puzzlingly complex raw data and datasets that are too large and intricate for traditional data management systems and tools to handle. In addition to the data itself, the field of big data encompasses the systems and tools used for data collection, storage, and organization.

The 3 V’s of Big Data

Three characteristics — volume, velocity, and variety — set big data apart.

• Volume – As its name implies, the primary characteristic of big data is its volume. Big data includes a massive amount of available information that is continuously produced and collected (for example, the volume of data gathered daily from individuals using smart devices connected to the IoT).
• Velocity – Big data is also characterized by a high speed of real-time (or near real-time) data generation. Big data requires systems and tools that enable collection, processing, organization, access, and analysis at the same fast pace. For instance, user data from streaming platforms is tracked as people click, watch, pause, or stop shows. 
• Variety – Big data is heterogeneous, too, as it comes in several different forms from countless sources. A manufacturer’s diverse datasets, for example, include text, images, audio, and video from a variety of sources like operator logs, machine sensors, quality control cameras, vendor management, customer response management, and financial management systems. 

In recent years, three additional V’s have emerged that further describe big data and define its quality:

• Veracity – Big data sets can be unwieldy, full of noise, and prone to errors. Smaller data sets may be incomplete. As a result, controlling the quality of data (by maintaining its veracity) is critical to its usability. 
• Variability – Collected data can be highly variable in its meaning due to constant changes in the context, means, or purpose of its collection. 
• Value – Big data has the potential to reveal invaluable insights that drive decision-making in organizations. Effective data analysis is essential when determining the value of an organization’s data. 

Types of Big Data

Adding to its variety, big data consists of three primary types:

• Structured data – Structured data is highly organized, can be easily stored in spreadsheets and relational databases, and typically consists of numerical or standardized text content. Structured data is the simplest type of data to organize, track, map, and use. Examples include financial transactions and customer response management data.
• Unstructured data – Unstructured data has no predefined structure or format, and it typically cannot be organized into a traditional column-and-row relational database management system. This data requires specialized tools for management, such as data lakes. Examples include social media posts, Word documents, PDFs, videos, text messages, phone calls, or emails. 
• Semi-structured data – Semi-structured data is a hybrid of structured and unstructured data, containing characteristics of each. This data features some organizational properties (such as tags) but lacks a consistent or well-defined structure. Examples include highly formatted web pages, XML files, or reference texts that include topics and subject lines.

Why Big Data Matters

Across industries, sectors, and types of organizations, big data enables data analysis and actionable insights for leadership decision-making. To put it in the context of an art metaphor, similar to paint or clay, big data is the medium utilized in data science. Although it is raw material, big data is important because it provides a true wealth of information that helps: 

• Drive research
• Solve problems
• Boost efficiency
• Optimize operations
• Cut costs
• Enhance experiences
• Reveal trends
• Predict future outcomes

What Is Data Analytics?

Data analytics is another branch of data science — the practice of processing and interpreting raw data to extract valuable, actionable, meaningful insights. Big data analytics refers to the process of analyzing enormous datasets to identify trends, patterns, and relationships to then extract meaning from these massive, complex data pools. 

Types of Data Analytics

Data analytics may be broken down into four main categories:

• Descriptive analytics – This type of analytics aims to identify and describe what happened over a set period of time. For example: Did click-through rates increase or decrease?
• Diagnostic analytics – This type of analytics focuses on why something happened. For instance, it would attempt to explain what led to the increase or decrease in click-through rates. 
• Predictive analytics – This combines historical data with other factors (such as market trends and economic conditions) to attempt to determine what could happen in the future. For example, based on patterns, trends, and consumer analysis, predictive analytics could attempt to determine how click-through rates will change in the near future.
• Prescriptive analytics – Prescriptive analytics focuses on determining what an organization should do by suggesting several potential courses of action and their projected outcomes. For instance, findings would help identify possible changes to a digital ad campaign to increase click-through rates. 

Why Data Analytics Matters

Without data analytics, big data (or essentially any data) lacks meaning. Analytics transforms raw data into valuable information, providing insights that help identify risks, lower costs, predict trends, and develop better products and services. Simply put, data analysis supports: 

• Improved decision-making 
• Greater operational efficiency
• Enhanced and personalized experiences
• A competitive edge alongside more successful outcomes

Big Data vs. Data Analytics: Key Differences

With all this in mind, big data and analytics are different but intrinsically related branches of data science. Here’s a quick recap: 

Definition

Big data refers to the systems and tools used to collect, store, and organize massive amounts of complex, raw data. Data analysis or analytics refers to the tools and methods used to examine and extract meaningful insights from datasets. 

Focus

Big data focuses on developing tools and systems to collect and manage vast amounts of raw data, while data analysis focuses on transforming it into information. 

Tools and Systems

Both branches of data science use systems and tools for operations. Big data relies on technology that collects, organizes, and stores large amounts of raw data. Data analytics relies on technology for transforming data into information through analysis. 

Goals

The goal of big data is to collect, organize, and store vast datasets in a way that preserves and maintains their quality. The goal of data analytics is to evaluate datasets using different methods of analysis to produce meaningful insights for specific purposes — from scientific research to product development. 

Tools, Systems, and Technologies

The tools, systems, and technologies used in big data and data analytics differ. However, they are designed to complement each other, as big data technology organizes and stores datasets in a way that makes them accessible and usable within data analytics technology. 

Big Data Tools and Systems

Big data leverages tools for processing and storing large datasets. Examples include NoSQL databases, cloud-based data warehouses, data lakes, and more. These may be offered by cloud providers (like Azure, AWS, or Snowflake) and even open-source solutions (e.g., Apache Hadoop).

Data Analytics Tools and Technologies

Data analytics can use a wide range of tools and systems to manipulate, visualize, and analyze, including: 

• Programming languages
• Big data frameworks
• Data visualization software
• Business intelligence software
• Machine learning libraries
• Database management systems
• Statistical analysis packages 
• Spreadsheet software 

How Big Data and Data Analytics Work Together

As intertwined branches of data science, big data analytics couldn’t exist without big data, and the latter would be worthless without deeper analysis and synthesis. Further explore the value of big data and analytics through the lens of their practical applications in the real world: 

Big Data in Healthcare

Data analytics in healthcare has the potential to enhance patient care, diagnostics, and outcomes as well as facilitate real-time patient monitoring. Big data in healthcare can be applied at a population scale, enabling population health analysis that directs and supports healthcare policy improvements. 

Big Data in Finance

In finance, big data and data analysis team up to analyze vast datasets (like market prices, transaction records, and customer behavior) — then deliver actionable insights to business leaders for boosting operational efficiency and outcomes along with improved financial services. Examples of uses include: 

• Risk management
• Fraud detection
• Credit scoring
• Algorithmic trading

Big Data in Retail

To support a seamless flow of data, big data and optimized analysis tools in retail provide real-time data via technologies like enterprise resource management systems, the Internet of Things, and radio frequency identification. In a highly competitive and volatile landscape, advanced analysis technology enables retailers to: 

• Identify market changes
• Streamline supplier management
• Optimize inventory management
• Elevate customer experience 

Why These Concepts Matter for Your Career

Due to its high relevance and value across industries and sectors, mastery of big data and data analytics concepts and tools could help support professional advancement. These popular careers in data science rely on knowledge of both big data and data analysis:

Data Analyst

Data analysts are responsible for gathering, cleaning, and using several methods and tools to perform the four different types of data analysis. 

Business Intelligence Specialist

Business intelligence specialists analyze data on behalf of businesses and other organizations to help them understand it and make data-driven decisions that elevate overall operations, strategies, and outcomes. 

Data Scientist

Data scientists draw from computer science, mathematics, algorithms, statistical analysis, database knowledge, and programming to build predictive models, extract meaningful insights from data, and communicate their findings. 

Risk Analyst

In data science, risk analysts leverage insights gained from vast datasets to identify, assess, manage, and mitigate risks. They rely on descriptive, diagnostic, predictive, and prescriptive analyses to understand risk and offer recommendations for risk management. 

Marketing Analyst

Marketing analysts refer to consumer and market data to perform analyses for the purpose of understanding consumer behavior, predicting trends, and providing actionable insights to enhance marketing strategy and performance. 

Big Data vs. Data Analytics: Takeaways

Big data and data analytics are separate yet closely interconnected branches of the data science field. Whereas big data is the raw material, data analytics actually unlocks its value. Both are integral to business intelligence and modern decision-making. 

Prepare for Leadership Roles in Data Analytics at JWU Online

Whether you work (or aim to work) in the public, private, or nonprofit sector, in today’s world, success largely hinges on the strategic use of data — supported by big data and the practice of data analytics. Studying to earn a Master of Science in Data Analytics provides aspiring professionals with the opportunity to explore and develop expertise inthese branches of data science. In this graduate degree program through JWU Online, students complete coursework designed to teach the integration of computer science, mathematics, and statistics for applications within big data and data science across industries. 

For more information about completing your degree online, complete the Request Info form, call 855-JWU-1881, or email [email protected]. 

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