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

In recent years, the rapid expanse of digital technologies has fueled excitement around the potential of data to help businesses and analysts make informed, data-driven decisions. While big data applies large volumes of data to uncover trends, patterns, and relationships between variables, small data is another important and necessary piece of the data insights puzzle.

It offers a way to focus on specific, relevant information rather than general, overarching trends. While they both work to draw tailored, actionable insights from collected sets of information, the differences in the scale and focus of their respective data sets make big data more suitable for making predictions and small data more appropriate for understanding causations.

Explore more about big data versus small data, including their advantages and disadvantages and when to use them for maximum benefits.

What is big data?

As the name suggests, big data refers to a large, typically diverse, and growing collection of data. Big data usually comes from a wide variety of sources, such as sensor data or social media, meaning you can find a wide array of features in the data set, often including unstructured, structured, and semi-structured data. 

Because big data can encompass many different types of information, it might be defined by the “3Vs” of big data: volume, velocity, and variety. Volume refers to the high quantity of information, velocity reflects the speed of new data generation, and variety indicates the diverse nature of the data set. 

What is big data used for?

Organizations harness big data to uncover trends, patterns, and associations. It’s widely used in medicine, retail, and finance to make predictions, drive decision-making, and address long-standing challenges.

Within different industries, you can use big data for various tasks, generating insights, making predictions, mitigating risks, and analyzing current trends and patterns. For example, some ways you might use big data include:

• Analyzing current stock market trends to predict future movements and make informed trades

• Estimating the rate of return on different investment decisions, including risks associated with each option

• Detecting unusual bank activity consistent with fraud to prevent customers from being taken advantage of or losing money

• Analyzing large volumes of written clinical notes to discover patient trends and identify new and improved treatment processes

• Personalizing consumer product recommendations based on behaviors and previous purchases

• Identifying high-risk patients based on previous outcomes from patients with similar genetic variants 

Advantages of big data

You can find the benefits of big data in everyday life, from personalized streaming service recommendations to GPS directions to your favorite restaurant. Beyond the daily perks, big data provides a platform for rapid advancement, giving rise to exciting innovation across sectors. Some advantages of big data you might find include:

Enhanced research practices. The large volume of information in big data paints a clearer picture of how variables relate. You can leverage these correlations that big data makes visible in areas such as health care, creating better models for disease prevention and treatment efforts. 

Optimized resource allocation. Government entities and consumer organizations can use big data to optimize how they distribute and utilize their resources, including more effective planning and management efforts. 

Personalized experiences. Perhaps one of the more widely known functions of big data is the constant stream of data allows for personalized predictions about individual users or customers. Predictive analytics uses big data to make predictions about what you might like or need according to your characteristics or previous actions, and these predictions are the basis for the content recommendations or service suggestions you see all the time. For example, big data can tailor your learning experience using adaptive learning strategies in education. As a consumer, big data can recommend products based on your shopping habits and what similar consumers have purchased.

Disadvantages of big data

Like using any tool, considering potential limitations can help you decide how to best leverage big data for different purposes. You might, for instance, find limitations related to the storage and management of your data sets. Because big data sets contain a high volume of information and data, you’ll need advanced storage solutions to properly contain your information. 

Likewise, because big data often comes from various sources, you may need advanced technical knowledge and expertise to clean and format your data appropriately for analysis. Then, once it’s properly arranged and you can analyze it, you’ll need to ensure the information is accurate and unbiased so that the insights you glean are reliable. All of this can be pretty challenging depending on your background and the resources you have available. So, unless you aim to make precise predictions to craft organizational strategies, you might prefer to work with small data.

What is small data?

Small data, as you might guess, comprises data sets small enough for human comprehension and analysis. It concerns identifying precise causations within an isolated ecosystem and is often used to address immediate needs or answer specific questions. Small data typically operates on an individual (n=1) basis, whether one individual, community, city, or health care system. It’s the data you’re likely to encounter in everyday business operations, such as vendor purchase information or customer behavior, and it provides actionable insights and direct impact without the need for costly storage and analytics systems.

What is small data used for?

Small data can be used to improve business models, fine-tune marketing campaigns, make clinical decisions, and more. Its insights can be instrumental for businesses looking to understand immediate consumer behaviors and preferences. 

Small data often provides a more realistic opportunity to collect and interpret data. For example, while big data can provide powerful insights and predictions, some applications may need large-scale labeled training data, which can be inaccessible. In this case, small, labeled data sets provide a way to train your machine-learning models with controlled, effective information.

One application of small data is “small data apps,” which are a growing market sector focused on using an individual’s data to create tailored recommendations and plans. For example, if you want to lead a healthier lifestyle, a small data app might use your exercise history, grocery purchases, and browsing history to create a personalized plan that fits your lifestyle.

Advantages of small data

Small data has several advantages, including its accessibility and easy comprehension. Explore some of the key advantages below:

• Access insights easily. Many businesses can collect this data type without needing more expensive big data analytics systems.

• Take action. Because small data is more targeted to specific individuals or topics, businesses and organizations can make quicker decisions regarding the data's impact.

• Collect more tailored feedback. Small data allows organizations to speak directly to consumers, giving insights into their specific consumer base that big data might miss or bypass.

• Reduce bias. When drawing insights from a large sample size, your insights might skew to favor economic or racial majorities, which may or may not reflect the community you’re hoping to learn about. Isolating your data collection to the community you serve can reduce the risk of biases that might make your findings less accurate.

• Explore the “why.” While big data might show correlations and trends from the past, small data can allow you to dig into the cause of specific patterns and investigate the thoughts and actions preceding certain outcomes. 

• Focus on the most important information. Instead of amassing a large volume of irrelevant information, businesses can focus on the most important details and develop targeted data collection and analysis plans to answer a specific question.

Disadvantages of small data

Like big data, small data has its own set of disadvantages. One of the main disadvantages is the lack of generalizability. Because small data focuses on a specific area or individual, you might be unable to transfer it to other applications. In addition to this, small data may have slower velocity, less flexibility, and less power than big data sets. When analyzing your data, it is important to choose methods that reflect the sample size and characteristics most appropriate to your collected information. 

Other things to consider with big data vs. small data

When exploring characteristics and use cases of big and small data, consider how each can serve your specific needs. Building data management and analytics skills can open up career opportunities in data management and analytics, data science, and business intelligence.

By showing proficiency in big and small data, you can make your resume stand out and demonstrate the versatility needed in the data-driven job market. When starting, consider building data-related competencies in data visualization, data cleaning, data analytics, and cloud-based competencies relevant to small and big data.

Getting started with data analytics on Coursera

While big data provides broad, comprehensive insights from large, complex data sets, small data focuses on targeted, specific insights that organizations easily understand and act upon. Understanding both nuances can help you determine the best ways to use data in your organization. Continue expanding your expertise with data with exciting courses and Professional Certificates on Coursera. Begin by completing the Google Data Analytics Professional Certificate, which can help you build in-demand data skills at your own pace. You may also be interested in the Data Visualization in Excel course by Macquarie University or Duke University’s Data Analysis with R Specialization.