Productivity

Big Data and Small Data: What are they and how are they different?

Understanding how Big Data and Small Data influence your business strategy is an absolute necessity in today’s world.

Sure, most organizations understand the importance of data, but few really understand the relationship between the two types.

In the world of business intelligence, Big Data is often discussed and you may have already put it into practice in your own organization. But when was the last time you thought about her little sister, the Small Data?

The key is to understand the difference between the two and find value in both.

Big Data vs. Small Data: An Overview

Big data is data created in incalculable ways, such as through transactions, clicks, radio frequency identification (RFID) readers, sensors, financial data, and an ever-increasing number of IoT-connected devices (Internet of Things or in Spanish, Internet of Things).

Small Data, on the other hand, is the data we collect through primary research. It is not only obtained from qualitative research (focus groups, home ethnographies, online communities, etc.), but also from quantitative survey research. It is where we ask or observe people directly to discover their attitudes, motivations and values.

But now, the detailed explanation of each one.

What is Big Data?

Big Data are high-volume, high-velocity, and/or wide-variety information assets that demand cost-effective and innovative ways of processing information that enable better understanding, decision-making, and process automation.

The volume of Big Data (the amount of data) is represented in large quantities; often terabytes, petabytes, and more. It requires advanced computing power and new processing techniques to manage and visualize.

In contrast, the speed of Big Data is the speed at which the data appears.

And finally, the variety in Big Data is how the data is presented. It consists of structured (date, time, GPS location, database), semi-structured (clickstream) and unstructured (text, image, voice, video) details.

Here is a short video that explains a little more about Big Data and how it is used:

What is Small Data?

On the other hand, we could say that Small Data is nothing more than small clues that reveal big trends.

It is connecting people with valuable and timely information (derived from Big Data and/or “local” sources), organized and packaged (often visually) so that it is accessible, understandable and actionable for daily tasks.

This idea of ​​it being “small enough for human comprehension” is key. Compared to Big Data, the volume of Small Data is more manageable and is measured in megabytes and gigabytes.

The speed of the data is slower and it is collected over days and weeks. Finally, it consists entirely of known details, the data is structured (ie numeric) and/or unstructured (ie text, images, video).

We have also attached an explanatory video about Small Data:

10 Differences between Big Data and Small Data

We have listed below the top ten differences between Small Data and Big Data:

1) Goals

Small Data is usually collected for a specific purpose. Big data, on the other hand, may have a goal in mind when it’s first started, but things can evolve or take unexpected directions.

2) Location

Small Data is usually found in one place and is often collected in a single computer file, while Big Data can be stored in multiple files, servers, computers, or even in different geographic locations.

3) Structure/Content

Small Data is often structured like an Excel spreadsheet, with rows and columns of data. But Big Data can be unstructured, with many formats and files involved, and can be linked to other resources.

4) Preparation

Small Data tends to be prepared by the end user for their purposes, but Big Data is prepared by a whole group of people, analyzed by a second group, and then even used by a third group, with different purposes and disciplines.

5) Longevity

Small data is kept for a specific time after a project ends because there is a clear end point. With Big Data, data is extracted for specific projects extended for a longer lifespan and is often reused and continued, mainly due to its difficulty and cost of extraction.

6) Measurement

Small Data is usually measured by a single protocol using set units and is usually done at the same time. while Big Data is often measured on a large scale with many protocols, which need to be converted for consistency, because you can have people in different places, organizations and times measuring the same information.

7) Reproducibility

Small data sets can be reproduced in their entirety (or almost entirely) if something goes wrong in the analysis process. Unfortunately, those larger groups may not be able to be extracted a second time as they come from different sources.

8) Risk

The costs related to Small Data are limited in the hypothetical case that something goes wrong. But big data-backed projects can cost hundreds of millions of euros if data is lost or corrupted. This could damage an entire organization or a researcher’s career.

9) Introspection

In a small data set, the information tends to be already quite organized and understandable from its first points of entry. However, a larger-scale extraction of data, files, and formats may end up with information that is unidentifiable, untraceable, or makes little sense.

10) Analysis

Last but not least, with small data it is generally possible to analyze all the data at once in a single procedure, from a single device or program.

However, Big data, because the files are so large and spread across different sources, additional extraction, reduction, transformation, and other steps may need to be performed before data analysis is manageable.

Use of Small and Big Data

Why is it so important that you learn and apply these concepts today?

Basically, knowing how to capture, organize and analyze Big Data and Small Data will go a long way in preparing, sharing and capitalizing on complex information, which also means that, independently, it provides a good understanding of your customers’ experience.

But the real power comes, however, from combining this data set. If you do this, you will be able to bring the entire experience of each customer to a greater and more insightful benefit.

How do they say out there: “The closest and best approximation of who we are as humans comes from blending our online and offline selves, and combining big data with small data.”

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