Small Data is the new buzzword in the world of data analytics. Discover everything you need to know about what lies behind this trendy term.

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What is Small Data?

We could say it’s the data we’ve always worked with. The term emerged after the rise of Big Data in the 1990s to describe datasets that are understandable to humans. In other words, Small Data is a dataset small enough for us to grasp.

We can identify it as structured data that meets the following criteria:

• Accessibility (it can be collected).
• Information (it can be understood).
• Processable (it can be reviewed).

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Small Data vs. Big Data

Big Data refers to datasets of such magnitude that a human being cannot handle them. Generally, data is considered Big Data if it meets these conditions:

• It involves massive volumes.
• It is obtained very quickly.
• It comes from multiple sources.

It’s often said that Big Data is for machines, while Small Data is for people. The former seeks correlations among millions of data points, while the latter seeks the causes and their reasons.

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Why is Small Data Important?

Small Data allows us to understand—and can therefore be an essential part of Big Data. In fact, the only way to make sense of massive amounts of data is by grouping and reducing them. Small Data enables the representative visualization of datasets.

In the words of Allen Bonde, Small Data connects people with timely, meaningful insights (derived from Big Data or other sources) by organizing and packaging the data (often visually) to make it accessible, understandable, and actionable for everyday tasks.

Big Data provides knowledge across many parameters, but despite its necessity, we must not overlook the importance of “small data.” According to Martin Lindstrom, the business world is becoming blinded by massive volumes of data, yet it is very difficult to describe emotions using data. We are obsessed with proving everything with numbers, when in reality, a high percentage of the best innovations in recent years have come from a single conversation with a customer, an act of creativity, or even by chance.

The trend of the future is to find that delicate balance between Big Data analysis and Small Data: being able to analyze vast amounts of data without losing sight of real-world experience.

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Data Analysis in SMEs

In small and medium-sized enterprises, analyzing massive amounts of data can be prohibitively expensive. One way to approach the world of Big Data is by using tools and techniques designed for analyzing smaller datasets. With a much lower investment, organizations can carry out predictive and advanced analytics to prepare for inevitable change. Additionally, businesses can begin to make informed decisions and maintain close control over key business metrics.