What Is Small Data?
Simply put, small data is data that is simple enough in format and small enough in volume to be processed by a single machine or understood by an individual. Considered a derivative of big data, small data can provide timely, meaningful insights that are organized and packaged in a way that makes them accessible, understandable, and actionable for everyday decision-making.
Small data allows businesses to get valuable insights without having to implement the kinds of systems needed to perform big data analytics. Because small data mainly comes from transaction systems, most businesses moving to an analytics strategy already have access to the small data they can use to make informed decisions before moving onto more advanced analytics using big data.
Common examples of small data include:
- Data from customer relationship management systems (CRMs)
- Purchase information for marketing materials, raw materials, and equipment
- Customer and product sales information
- Data on customer behaviors
- Online shopping cart data
- Customer satisfaction surveys
- One-on-one interviews
The Characteristics of Small Data
Generally speaking, small data is defined by three characteristics. Small data is:
- Accessible: Big data comprises large volumes of complex data that are difficult to manage. In contrast, small data comes in smaller volumes that are easier to use.
- Understandable: Small data summarizes big data into smaller data sets that are easier to understand without the use of powerful algorithms and analytics programs.
- Actionable: Small data gives insight into users, customers, and their behaviors that can be useful for making short-term decisions.
Big Data vs. Small Data
Big data refers to the large volumes of structured and unstructured data generated by today’s business processes. Big data sets are typically hard to access, understand, organize, and analyze. Because this data is too large to be represented on a single machine, it typically requires powerful computing hardware, software, and algorithms to discover patterns, trends, and insights that could be helpful to business operations.
Unlike big data, small data comprises smaller, more usable chunks of data and is easy for humans to understand, access, and analyze. It accumulates much more slowly than big data and is often used to provide answers to specific questions or address a specific problem. Small data is usually stored on a single machine, like a local server or laptop. As a result, businesses can easily derive valuable insights from small data without having to invest in high-performance technology and the use of complex algorithms.
That said, both small data and big data have the ability to impact businesses and can work together to address different audiences and organizational levels.
What Is the Importance of Small Data?
For companies with limited resources, it makes sense to work with data at a scale that can make an immediate impact on the business. As noted, it’s easier to process and interpret smaller data sets than massive amounts of big data information.
Further, since small data can be presented in a more relevant and compact way, it’s often easier to analyze and more actionable for professionals, digital marketers, and managers.
In many cases, small data supports stronger real-time results, helping businesses to improve standard practices, solve current problems, and uncover innovative ideas that lead to new ways of doing business. Like big data, small data can also power machine learning and artificial intelligence models, essential for automating key internal processes.
The Benefits of Small Data
Higher Availability
Most data consumed is small data. Anyone with a computer or smartphone creates small data, which makes it more readily available than big data. Data from social media or performance ads, for example, are valuable resources for information on buyer decisions and customer lifetime value (LTV).
Simplicity
Small data can be easily understood and interpreted by humans, making it easy for stakeholders and decision-makers to understand. Big data often requires professional interpretation, but small data can be used by anyone to create business value.
Immediate Business Intelligence
In many cases, small data is the right data for the problem at hand. Because small data is easy to understand, the time between small data generation and the ability to use it to inform business decisions and reach customers can be quite short.
A Customer-Focused Approach
Small data allows you to better understand what your end users need from your business. By observing small samples of customer data, you can uncover detailed information on the reasons customers behave the way they do, which can translate into important business insights.
Cost Savings
Small data can be accessed from applications and services, wherever it’s stored, without having to build costly data stores and warehouses as data volume grows.
Small Data Use Cases
Consider the following potential use cases for small data:
Customer service: Detailed information about customers can help businesses provide faster issue resolution. Phone number recognition or prior knowledge of an issue (e.g., a delayed flight) can help customer service representatives better handle a problem or redirect it to an automated response service.
Expense and asset management: Businesses often have a difficult time accurately tracking expenses and reporting fixed assets. Small data gives you a clearer picture of your overall organizational efficiency that can be used to help align your business activities with your top priorities.
Employee retention: Being able to observe minor changes in employee activity, such as underutilization of accrued vacation time and sick leave, can indicate employee motivation levels. This information can help inform retention methods and improvements to company culture and the workplace environment.
Personalized shopping: Small data sets created from wearable and handheld devices, sensors, surveillance cameras, and retail IoT devices can be used to improve the in-store customer experience.
Small Data Trends
Interestingly, Gartner’s Top 10 Data and Analytics Trends for 2021 report shows that small and wide data—rather than big data—is being used to solve problems in organizations related to challenges with insufficient data on specific use cases and complex AI questions.
Wide data supports the analysis of several small and varied unstructured and structured data sources for better context and decision-making, while small data can make use of data models that offer useful insights with less data.
The report also suggests that AI technologies need to be able to use smaller data sets and small data techniques instead of traditional historical data—much of which may now be irrelevant, given the changes to the business environment caused by COVID-19.
