Unlike conglomerates, small businesses use small data for analytic purpose

Humans produce over 2.5 quintillion bytes of data every day. Unfortunately, not everyone has the luxury to access all of it. Most of the data we create belongs to big tech giants who use that to leverage data-driven decisions. When the world is busy running after big data and its advantages, we need to think about how small data can be utilised for big purposes. Unlike conglomerates, small businesses gather small data. However, this small data can be put to good use if they learn the tactics behind it.

Big data is continuing to be the buzzword in the tech industry since the past decade. Much has been written and spoken about big data, and not to forget that the technology has revolutionised business and decision making. For big organisations data is an easy catch. Henceforth, they often forget the small data that goes unused. For example, tech giants ignore information like marketing surveys of new customer segments, meeting minutes, spreadsheets with less than 1,000 columns and rows. But the scenario is different for small businesses. Small businesses have to take every bit of data into account in order to make it useful.

Small data is data that is ‘small’ enough for human comprehension. It is data that comes in volume and format that makes it accessible, informative and actionable for small businesses. Big data is very difficult to manage, and consequently, very few people within the organisation are capable of making sense of it. Small data contradicts the aspect as it comes in smaller, lighter packages that are much easier to use. Small data is generally about users, customers and their behaviours. In a nutshell, small data is big data which has been connected, organised and packaged by complex algorithms in order to appear easy and actionable for humans. Roughly plotting, among the top 100 biggest innovations today, perhaps around 60-65% of them are based on small data.

Small data is for small businesses

Small data is concerned with identifying causations in data that are small and logical enough to be understood in the context of a given business and can be analysed for insights that lead to bigger decisions. Relevant small data can typically be identified through the analysis of business processes, both internal and external, as well as through the analysis of key resources to the business. For small business owners, discovering the hidden patterns of potential customer’s behaviour is an appealing pursuit. That’s why they have to come up with the right technique to get the maximum out of minimum data.

Exploratory analysis: Both in big and small data, the only goal for business organisations is to get deep insights from it. This includes calculating simple descriptive statistics, like count, means, quartiles, the minimum, the maximum and so on. Exploratory analysis can go one step further into more complex information like histograms, scatterplots, pie charts, etc. Small businesses can use this kind of exploratory analysis to avail data-driven decisions.

Transfer learning: Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on the second task. Small businesses can leverage transfer learning to transfer the knowledge learner in one dataset and apply it in another. As a result, businesses don’t have to start from scratch; they can just abide by training machine learning models with far fewer data.

AutoML: Automated machine learning (AutoML) involves automating the end-to-end process of applying machine learning to real-world problems that are actually relevant in the industry. Small businesses can use AutoML to quickly deploy AI without needing big data. AutoML comes with automated machine learning solutions that are pre-trained on big datasets.