By Mathieu Zamanian – CEO MyDataModels.
Data, Data, Data. Data has been the true black gold of companies for the past ten years. Analyzing large amounts of data, i.e. Big Data, is now the specialty of many start-ups and tech giants. Precious and a source of growth, Small Data is less known than Big Data, but is already changing the way we make decisions, work and manage businesses. According to a study by Gartner, this small operational data generated by companies represents 80% of the total amount of data in the world.
Let’s first start by defining Small Data
Big Data involves terabytes or even petabytes of data stored in the cloud with the need to automate its analysis to obtain results. Small Data, on the other hand, focuses on operational data that is understandable, accessible and quickly usable by experts without specific training. They are also ubiquitous. Also, Small Data datasets focus on a specific topic, such as a sales report or medical analysis.
Besides, you probably use Small Data without realizing it in your daily professional life, when you work on Excel or CSV reports. In other words, when you work on an Excel spreadsheet of 300 rows and 15 columns, so-called small datasets, to analyze the latest reports from your operations and sales team and find patterns and recurring ideas, you are performing a Small Data analysis!
Big Data relies on data scientists and huge IT infrastructures. Meanwhile, real experts are working on their Small Data sets on their computers every day.
A classic comparison is that Big Data is processed by machines while humans work on Small Data. The analogy is partially true because human intervention is mandatory for Big Data projects. But the particularity of Small Data projects is that they are much closer to the daily work of business experts. Thanks to Artificial Intelligence, lighter but just as effective as that used for Big Data projects, the analysis of Small Data is operational and actionable faster than for Big Data projects. Investments are also less expensive and much shorter. This is a real lever for Chief Data Officers (CDOs) who sometimes find it difficult to legitimize their investments in Artificial Intelligence with their management and this is easily understandable since 85% of Big Data projects fail, again according to Gartner .
A marketing manager analyzes his customer surveys. An R&D expert tests different combinations and doses of chemicals. A supply chain manager should hire temps based on the demand of their warehouse. Do these use cases sound familiar? They all rely on Small Data analytics, regardless of the size of their business.
Introducing innovative artificial intelligence and machine learning techniques into small data analytics and making them available to all professionals will revolutionize the way we work. For data-driven businesses, Small Data is the ideal solution to enable value creation by giving them access to easy-to-understand and actionable information to guide their decisions.
Small Data, Big Impact! Discover decision intelligence platforms to improve your business using your own Small Data!
About the Author
Mathieu Zamanian, CEO of MyDataModels
Mathieu holds a master’s degree in engineering from Polytech Paris Sud, France, and a master’s degree in business administration from Pepperdine University, California. As an entrepreneur, he brings a wide range of experience gained from developing businesses internationally. His impressive career spans over 25 years, including as Regional Vice President of Sales for Europe (EdCast); Managing Director for France (EF Corporate Solutions); chief operating officer (Zags); Chief Commercial Officer (GenomeQuest Inc.); co-founder and managing director (BSEEN International); Global Account Manager (IBM).
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