What is the next phase of your data strategy?
In recent years, data strategy has been focused towards analysis of huge datasets in domains where data are easy to collect. On the other hand, there are domains where generating each point is very time consuming or expensive, as a result, they deal with much smaller datasets consisting of a few tens or hundreds of samples, also called small datasets. Therefore, we learned well how to analyze big datasets but Small datasets require a different set of algorithms and different set of skills.
Why is the future after Small Data, not Big Data?
Even though the focus of recent years was towards Big Data, most of the companies naturally possess small datasets not big ones. You can easily use Small datasets starting now. There are several reasons why smaller datasets are the future:
- Most organizations in the world will never have Big Data;
- In most cases small dataset is enough to solve a problem;
- It’s easier to focus on an issue using small datasets;
- In many instances, small datasets are more relevant because, in order to produce Small Data one needs to analyze it first;
- Using Small Data, organizations can get actionable results without obtaining Big Data analytics;
- Small Data matters more for IoT.
What are the examples of using Small Data?
Small datasets can arise in many situations. Here are a few examples:
– Scientific research: when you want to identify molecular markers of disease processes, you have a limited number of patients. In order to build predictive models, you need algorithms that work well with small datasets.
– Customer rating/segmentation: instead of making one solution for a thousand clients, you want to create several solutions for a relatively limited number of members of each segment, resulting in a smaller number of samples.
– Time series: time is limited too. It’s not always easy to increase sampling rate effectively, especially if you are collecting one data point for each day.
– Internet of things (IoT): in many cases sensors are configured to send small amounts of data.
– Any situation where the sample size is limited (or really expensive to get)
So to summarize, everything that can be processed in Excel is Small Data.
How to work with small datasets using TADA?
First of all, what is TADA? TADA is an automated machine learning solution for predictive modeling, and it has been designed specifically for working with small dataset.
The advantage of TADA is that it can be used by any professional, it doesn’t matter whether you have machine learning/coding skills or not.
To start, you will need to collect & prepare your dataset in CSV format and then simply upload this file into TADA and create your model in one click. It also allows you to visualize your data for easy preview & analyses. Start using TADA now.