Big Data VS. Small Data

Index

The Big Data eCours I Co-Developed

In 2018, I co‑developed a Big Data eCourse together with Laudius and another instructor. The course covers the fundamentals of Big Data, the ethical considerations surrounding its use, and methods for analyzing Big Data. Since Big Data typically consists of quantitative information, its analysis relies heavily on statistical techniques.

The book Small Data

In Small Data, author Martin Lindstrom explores how Small Data is used in real‑life cases to uncover meaningful insights. He argues that our modern obsession with digital, large‑scale data risks overshadowing the value of close observation and human‑centered understanding. According to Lindstrom, society needs Small Data because it provides the deep, qualitative insights that Big Data alone cannot deliver.

Small Data 

Small Data refers to the rituals, habits, gestures, preferences, and other subtle details that reveal how people live and behave. It includes likes and dislikes, hesitations, speech patterns, home décor, passwords, tweets, status updates, and more. Examples of Small Data might be a family’s shoe collection or the photos they share in a Facebook album. 

Big Data  

According to the book Succes met Big Data, which is used in my Big Data e‑course, Big Data has the following characteristics:

• Large amounts of data: datasets so extensive that they exceed the capacity of traditional tools.

• No checks on completeness: individual data points may be missing or inconsistent.

• Lack of organization: the data is often unstructured and not arranged in a predefined format.

• Constantly changing volume: the amount of data can grow or shrink at any moment.

In the e‑course, students learn two definitions of Big Data. The first definition states that Big Data is such a large, unstructured, and sometimes incomplete set of data that it cannot be processed using conventional database systems. The second definition describes Big Data as collections of data characterized by large quantity (volume), the high speed at which new data is generated (velocity), and the significant variation within the data (variety). Together, these definitions and attributes show that Big Data is primarily about quantity, scale, and hypothesis‑driven analysis, rather than about fine‑grained details.

Big Data vs Small Data

Understanding the difference between Big Data and Small Data is essential for choosing the right approach in research. Not every study requires both; depending on the research question, a researcher may rely on Big Data, Small Data, or a combination of the two. Big Data faces a fundamental challenge: a mismatch between technology and human behavior. Technology operates predictably and consistently, while humans do not always behave as expected. Big Data also tends to live in databases that are often defined too narrowly to generate deep insight. This is where Small Data becomes valuable. Small Data provides the emotional, human‑centered context that Big Data lacks. By enriching Big Data with Small Data, researchers gain a more complete understanding of people’s motivations, feelings, and behaviors. In short, Big Data offers scale and patterns, while Small Data offers meaning and emotion. Small Data is most of the time Qualitative data. Together, they create richer and more accurate insights than either could provide alone.

Small Data Needs Big Data  

A single piece of Small Data is not enough to form a solid hypothesis or build a business strategy. Big Data provides the large‑scale foundation companies need, but when it comes to truly understanding consumers, Big Data alone offers valuable yet incomplete answers. The combination of Big Data and Small Data is essential. They function as partners, each compensating for what the other lacks. Big Data helps automate processes, identify large‑scale patterns, and streamline decision‑making. As technology evolves, humans adapt alongside it, navigating the changes it brings. However, Big Data often struggles to uncover meaning, relevance, or deep insight. Those insights emerge when Big Data is enriched with Small Data. Small Data captures the details, often personal, emotional, or behavioral, such as a grocery list, a household routine, or a subtle preference. These small clues provide the human context that Big Data cannot detect on its own. Together, Big Data and Small Data create a more complete and meaningful understanding of people and their behavior.

Related blogs on my website

• Answer The Public 

• Big Data analysis with Real-Statistics in Excel 

• Big Data eCourse 

• Big Data Hype & Beyond 

• Certificate Or Diploma Big Data course 

• Content of the eCourse Big Data 

• DataViz In Museums 

• Digital diarrhea 

• Door Ai Beïnvloedt Big Data Ook Het Netwerken

• Dutch Health Hackathon  

• Ethics In Big Data 

• Ethiek Is Nodig Om Uit Big Data Gevoelige Informatie Op De Juiste Manier Te Publiceren 

• Fieldwork agency VS research agency 

• Guiselaine & Big Data 

• How Big Data is creeping into my life 

• How Can I Help Promote The Big Data eCourse & Earn A Bit 

• How Can Small Enterprises Benefit From Big Data? 

• How Laudius asked for a Big Data eCourse and I and another teacher made it 

• How to buy the Big Data eCourse on Laudius platform 

• Kennis Up To Date Houden In De Zorg Of Na Een Zorgtraject 

• Kwalificatie-criteria Ontwikkelen Big Data cursus Laudius 

• Leven Lang Leren 

• Management tools 

• Open data, where can I find it? 

• Privacy & Big Data 

• Research 

• Should face to face learning be compared to online learning 

• SPSS 

• Storytelling With Data VS DataViz 

• The Big Data eCourse Has a Forum Full Of Additional Information

• The most important Podcast about data  

• Using a decision tree to determine where to go 

• What is Big Data? 

• What is statistics? 

• Which Big Data Analysis is included in the eCourse Big Data on Laudius 

• Which companies apply Big Data? 

• Wish To Obtain A Doctorate 

• Workshop Excel Beginners