Difference between Big Data and Data Analytics

Big Data and Data Analytics are related concepts, but they have clear differences. Let’s make a small breakdown in order to better understand and draw our own conclusions:

Big Data and Data Analytics
Big Data and Data Analytics


Big data

Big Data refers to the massive volumes of data that are generated, collected, and stored through various sources, such as social media, sensors, devices, and transactions. Big Data is characterized by its volume, speed and variety, which makes it difficult to process and analyze with traditional data processing methods.

Data Analytics

Data analysis, on the other hand, is the process of examining, interpreting, and deriving meaningful insights from data. It involves various techniques, tools, and methods for exploring, analyzing, and visualizing data in order to uncover patterns, trends, correlations, and insights that can inform decision-making and drive business outcomes.


Big Data is mainly focused on the handling, storage and processing of complex and large-scale data sets. It involves technologies such as distributed databases, data lakes, and data warehouses, as well as tools such as Hadoop and Spark for distributed data processing and analysis.

Data analytics, on the other hand, focuses on extracting insights from data to gain insights, identify patterns, and make informed decisions. It involves techniques such as descriptive analytics (summarizing and visualizing data), diagnostic analytics (exploring causes and relationships in data), predictive analytics (forecasting future outcomes), and prescriptive analytics (providing recommendations and optimizing decisions).


Big Data deals with the large amount of data that is generated from various sources and requires specialized infrastructure and tools for its storage, processing and analysis. It is commonly used in applications such as customer analytics, fraud detection, personalized marketing, and scientific research.

Data analytics, on the other hand, can be applied to both large and small data sets. It can be used in various sectors and fields, such as business, healthcare, finance, sports, etc., to obtain information, drive decision-making and achieve strategic objectives.


The purpose of big data is to capture, store and process large amounts of data to identify patterns, trends and insights that can be used for various purposes, such as improving operational efficiency, improving customer experience and driving innovation.

Data analytics, on the other hand, focuses on the use of analytical techniques to extract insights from data for decision making, problem solving and strategic planning. It aims to uncover hidden patterns, relationships, and insights that can drive actionable outcomes and create value.

Features of Big Data

Big Data Features
Big Data Features

Big data refers to large and complex data sets that cannot be easily managed, processed or analyzed with traditional data processing tools or methods. Features of big data include:

  • Volume: Big data is characterized by its enormous size, which usually ranges from terabytes to petabytes or even exabytes of data. They can be generated from various sources, such as social networks, sensors, transactional data, etc.
  • Speed: Data in big data is generated and accumulated at high speed. Data flows in real-time or near-real-time from sources such as social media, online transactions, and sensor data, requiring rapid processing and analysis to extract meaningful insights.
  • Variety: Big data comes in a variety of formats, such as structured data (e.g., databases, spreadsheets), unstructured data (e.g., text, images, videos), and semi-structured data (e.g., XML, JSON). The management and analysis of these different types of data require specialized tools and techniques.
  • Veracity: Big data can pose problems of quality, accuracy and reliability. Data can be noisy, incomplete, or inconsistent, which can affect the accuracy and validity of analyses and insights derived from the data. Addressing data quality and veracity is a critical challenge in big data analytics.
  • Variability: Big data can present variability in terms of volume, velocity and variety of data. The flow of data can be irregular, and the pace of data generation can change over time, making it difficult to predict data patterns and trends.
  • Complexity: Big data can be very complex and involve intricate relationships and dependencies between data points. Big data analytics often requires sophisticated algorithms and models capable of managing complexity and uncovering hidden patterns and insights.
  • Value: Big data has the potential to reveal valuable insights and create business value. Extracting meaningful insights from big data can enable organizations to make data-driven decisions, gain competitive advantages, and drive innovation.
  • Privacy and security: Big data can contain sensitive information that raises privacy and security concerns. Protecting privacy and ensuring data security are important considerations in big data management and analytics.
  • Scalability: Big data systems need to be highly scalable to handle the sheer volume, velocity, and variety of data. Scalability is crucial to ensure efficient processing, storage and analysis of big data.
  • Real-time processing: Big data analytics typically requires real-time or near-real-time processing to extract information in a timely manner. Real-time processing capabilities are crucial for applications such as fraud detection, predictive maintenance, and personalized recommendations.

Data analytics features

Data analytics features
Data analytics features

Data analytics, the process of examining, cleaning, transforming, and modeling data to extract insights and support decision-making, encompasses several traits or characteristics, including:

  • Descriptive analysis: Descriptive analytics focuses on understanding and summarizing historical data to provide insight into what happened in the past. It involves data visualization, data aggregation, and basic statistical analysis to identify patterns, trends, and correlations in the data.
  • Diagnostic analysis: Diagnostic analysis consists of identifying the root causes of past events or results. It uses techniques such as data breakdown, data cutting, and data filtering to investigate the data and discover the reasons for certain trends or patterns.
  • Predictive analytics: Predictive analytics involves using historical data and statistical algorithms to make predictions about future events or outcomes. It leverages techniques such as regression analysis, time series analysis, and machine learning to predict future trends, patterns, and behaviors.
  • Prescriptive analytics: Prescriptive analytics goes beyond predicting future events and provides recommendations or actions to optimize outcomes. It uses advanced techniques such as optimization, simulation, and decision modeling to suggest the best course of action based on intended outcomes.
  • Exploratory analysis: Exploratory analytics involves exploring and analyzing data to identify new patterns, trends, or insights that were not previously known. It often involves data visualization, data mining, and machine learning techniques to discover hidden patterns or relationships in the data.
  • Real-time analytics: Real-time analytics is analyzing data in real-time or near-real-time to gain insights and make real-time decisions. It is commonly used in applications such as fraud detection, online advertising and IoT (Internet of Things) analytics, where data is generated and processed in real time.
  • Big data analytics: Big data analytics involves the analysis of large and complex data sets, often characterized by high volume, speed, variety, and complexity. It requires specialized tools and techniques to handle and process massive amounts of data in order to uncover meaningful insights and values.
  • Data visualization: Data visualization is the use of graphical representations, such as tables, graphs, and dashboards, to visually present data and make it easier to understand and interpret. Data visualization is a fundamental feature of data analysis, as it helps identify patterns, trends, and outliers in data.
  • Data cleansing and transformation: Data analysis often requires cleaning and transforming data to ensure its accuracy, consistency, and integrity. Data cleansing involves identifying and correcting errors, inconsistencies, and duplicates in the data, while data transformation involves converting the data to a common format or structure for analysis.
  • Data integration: Data integration is the process of combining data from multiple sources and integrating it into a single, unified view for analysis. Data integration is crucial in data analysis to ensure that data from different sources is effectively combined and analyzed to gain insights and make informed decisions.

Application of Big Data and Data Analytics

Let’s see what are the possible fields of action of these two fields of study:

  • Business and finance: Big data and data analytics are widely used in business and finance to gain insights into customer behavior, market trends, financial performance, risk management, fraud detection, and investment decision making. Organizations use data analytics to optimize pricing strategies, customer segmentation, supply chain management, and financial forecasting.
  • Healthcare and life sciences: Used in healthcare and life sciences to analyze large volumes of patient data, medical records, and genomic data to improve patient outcomes, optimize treatments, and develop new drugs. Data analytics is also used for disease surveillance, health tracking and personalized medicine.
  • Marketing and advertising: Both play a crucial role in marketing and advertising, helping organizations understand customer preferences, behavior, and engagement. Data analytics is used to segment, target and personalize marketing campaigns, as well as to measure the effectiveness of marketing strategies and advertising campaigns.
  • Manufacturing and supply chain: Together they are applied in manufacturing and supply chain operations to optimize production processes, improve product quality and reduce costs. Data analytics is also used for supply chain optimization, demand forecasting, and inventory management.
  • Transportation and logistics: Both are used in transportation and logistics to optimize route planning, fleet management, and transportation scheduling. Data analytics is also used for predictive maintenance of vehicles and assets, as well as to optimize logistics operations in order to reduce costs and improve efficiency.
  • Energy and utilities: Big data and data analytics are used in the energy and utilities sector for smart grid analysis, energy consumption optimization, and predictive equipment maintenance. Data analytics is also used for demand management, energy forecasting and renewable energy integration.
  • Government and public sector: Similarly, both are widely used in administration and the public sector for policy planning, decision-making and public service delivery. Data analytics is used for social analysis, crime prediction, transportation planning, and disaster management, among other applications.
  • Sports and entertainment: Its use in sports and entertainment is very frequent as well, for the analysis of player performance, fan participation and audience understanding. Data analytics is used for sports performance analysis, game strategy optimization, and revenue optimization in the entertainment industry.
  • Education and research: In education and research they are employed for learning analysis, educational assessment and research perspectives. Data analytics is used to analyze student performance data, optimize learning pathways, and generate insights from research data.
  • IoT and smart cities: In IoT (Internet of Things) and smart city applications they are used to analyze data from devices, sensors and connected systems. Data analytics is used for smart city planning, infrastructure optimization and urban analytics.

Skills required for Big Data and data analysis

To excel in the field of big data and data analysis, several competencies are necessary. These skills can be broadly classified into technical skills, analytical skills, and domain-specific skills. Here are some of the key competencies needed for data and big data analytics:

Programming skills: Mastery of programming languages such as Python, R, Java, Scala or SQL is essential for big data and data analysis. Strong coding skills are required for data extraction, transformation, and analysis tasks using programming languages and frameworks commonly used in big data ecosystems such as Hadoop, Spark, or NoSQL databases.

Data visualization and reporting: Data visualization is a crucial skill for presenting complex data in a visually appealing and understandable way. Mastering data visualization tools such as Tableau, Power BI, or D3.js, and creating visually compelling reports and dashboards are important for effectively communicating data-driven insights to stakeholders.

Statistical analysis and machine learning: A solid understanding of statistical analysis and machine learning techniques is vital for data analysis. Knowledge of statistical concepts such as hypothesis testing, regression analysis, and machine learning algorithms such as decision trees, random forests, and clustering is essential for analyzing data and gaining meaningful insights.

Data cleansing and processing: In the real world, data is often messy and needs to be cleaned and sorted before analyzing. Mastery of data cleansing techniques such as data imputation, data normalization, and data integration, using tools such as OpenRefine or Trifacta, is important to ensure data quality and accuracy in big data and data analytics projects.

Data mining and exploration: Exploring and discovering patterns, trends, and insights from large data sets requires competencies in data mining and exploration. Mastering techniques such as data profiling, data visualization, and exploratory data analysis (EDA) using tools such as Pandas, Numpy, or Matplotlib in Python is essential for discovering hidden patterns and insights from data.

Data engineering and data integration: Big data projects often require skills in data engineering and data integration. Understanding data integration techniques, ETL (Extract, Transform, Load) processes and big data technologies such as Apache Spark, Apache Kafka or Apache Flink is important to handle large volumes of data and process them efficiently.

Domain-specific knowledge: Depending on the industry or field of application, domain-specific knowledge is crucial for big data and data analytics. Understanding specific data requirements, data sources, and data-related challenges in industries such as healthcare, finance, marketing, or transportation can greatly improve the effectiveness of data analytics projects.

Critical thinking and problem solving: Big data and data analytics projects often involve complex data challenges that require critical thinking and problem-solving skills. Being able to analyze data, identify patterns, formulate hypotheses, and develop data-driven solutions to real-world problems is essential to success in this field.

Communication and collaboration: Effective communication skills are vital for explaining complex data insights to stakeholders, collaborating with cross-functional teams, and presenting findings and recommendations. Strong written and verbal communication skills, as well as the ability to work in teams, are important to effectively communicate the value of data analysis to decision makers.

Continuous learning and adaptability: Big data and data analytics are rapidly evolving fields, and staying current with the latest tools, technologies, and techniques is crucial. Having a mindset of continuous learning, adaptability and being open to new approaches is important to remain relevant and succeed in the dynamic field of big data and data analytics.

Read also: Meaning of descriptive analysis examples; Relationship between logic and critical thinking; Meaning of descriptive analysis examples.

External resource: Bmc

Editions 2019-20-23

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