Is My Dataset Good? Checking the Quality of your Collected Data


In this tutorial, we will be discussing the different ways to check the quality of your dataset and determine whether is good or bad. We are all well aware of the fact that in order to effectively use a Machine Learning (ML) or AI model, it is imperative to have high-quality training data for the model itself. No matter how efficient or accurate the model is, if it is provided with and trained upon a poorly constructed dataset, it will never produce the desired or correct output. The goal of this tutorial is to discuss and devise a number of ways in which one can ensure that the data that he/she has collected for different ML and AI projects are of high quality. We will also be suggesting a number of techniques that you can use at an individual or organizational level to drastically improve the quality of your datasets. So let’s begin!

Why Check the Quality of a Dataset?

As we discussed before, having a good quality dataset is of utmost importance in achieving the highest possible accuracy of your model. Moreover, it is paramount that the dataset is processed in such a way that the model can make complete sense of the information that is provided. In short, if you do not provide the right data to your ML or AI models, you will not only get wrong but also, at times disastrous results. Consider the example of Autonomous Vehicle driving. If a Computer Vision algorithm from your vehicle is trained with incomplete or poor data, it will not only produce incorrect outputs but can also put human lives at stake. Now can you get the idea of how important it is to have a good quality dataset? Good. Now let’s see how we can keep a dataset’s quality in check.

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General Methods for Checking a Dataset’s Quality

The methods to check a dataset’s quality varies from project to project as ML and AI projects can be very different from each other. Thus, quality assessments are customizable according to the specific needs of the project itself and to meet certain goals and objectives. The following methods, where applicable, can be used to assess the quality of one’s data:

  1. Setting a Benchmark
    You can measure the accuracy of your dataset by comparing it with the dataset which is already carefully annotated. This way of setting a benchmark through a vetted example and comparing your collected data with it can help you to see exactly how much of your data differs or deviates from the benchmark. You measure how much a set of annotations from a group or individual meet or miss the benchmark. You can even use different mathematical formulas to get an exact number or percentage.
  2. Consensus Method
    The Consensus Method, also known as the Overlap Method, is a way of checking the consistency of data within a group; it is quite common in assessing a dataset’s quality. In this method, the sum of the total number of consistent or agreeing data annotations is divided by the total number of annotations to calculate the accuracy.
  3. Audit Method
    Here, you get the data labels reviewed by an external party of experts. In this method, usually, the auditors keep on continuing the process of reviewing data levels until a certain level of accuracy is met. It can be highly accurate and specifically useful; yet, it is extremely expensive in both time and budgets.
  4. Data Monitoring
    In this method of quality assurance, the project management team constantly monitors the data on a monthly, weekly, or at times on a daily basis to check its quality. Again, it is quite expensive to manage such a team.
  5. Hybrid Approach
    This is a multi-layered approach towards quality assurance in which multiple different methods like the ones mentioned above are applied altogether to measure data quality.

Suggestions to Improve Data Quality

To improve the quality of your dataset on a smaller or even a larger level, you can adopt different ways and techniques. On a smaller or individual scale, you can focus on the following steps:

  • Planning
    Before actually collecting the data itself, it is very important for you to understand and assess the context of the problem that needs to be solved and then decide upon the best possible route to build a dataset for the problem in hand. For instance, if the problem that you are trying to solve is quite common, says a handwritten digit classification problem, you can easily find many sources for open datasets e.g. MNIST in this case.
  • Gathering Data
    While gathering data, it is important for you to consider diversity in the dataset. For example, if you are collecting images for image dataset needed to solve a classification problem, you have to make sure that your training dataset is as varying and diverse as possible by taking pictures from different angles, under different conditions, changing the object sizes, the distance of the camera lens from the object, etc. The same concepts of diversity can apply to other types of data as well.
  • Data Filtering
    To have a dataset of high quality, it is important for you to filter your dataset. In this process, you get rid your dataset of duplicate data e.g. in the case of image datasets, you should delete duplicate images and poor quality data i.e. images that are low in resolution, too small in size, etc. This manual pruning is at times inevitable to improve the quality of your dataset.
  • Some other factors
    To ensure that your dataset is of high quality, you need to consider a number of other factors as well. These include choosing the right amount of data. As a general rule, for Machine Learning projects, the size of the dataset should be at least 10 times the number of features per class. In the case of Deep Learning projects, the size should be at least 100 times the number of features per class. The data should also be well balanced i.e. the sample quantities should be equally distributed among classes. The samples should be representative of real situations where you are going to apply your model. Lastly, the samples should have as maximum variety and diversity as they possibly can.

On a larger scale i.e. at an organizational level, to ensure high-quality data, you need to actively build diversity into the data teams by defining goals, metrics, and roadmaps and algorithms used to develop datasets. You should also use the right tools to do so, as, without proper tools, you cannot do a good job.

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In this tutorial, we started off by talking about how important it is to have a dataset that meets a certain standard of quality, as without setting a boundary for quality, your model is severely handicapped and cannot do its job properly. We then discussed the different practices that you can follow to test the quality of your dataset for different ML or AI learning projects. These include methods like setting a benchmark to compare your data items to, using the consensus method, the audit method, continuous data monitoring, and a combinational approach of the aforementioned methods. Lastly, we suggested different ways in which one can improve data quality by focusing on the planning phase of data collection, filtering the collected data, choosing the right amount of data and including diversity in the dataset, etc. In a nutshell, keeping a check on your dataset quality and continuously looking for and implementing ways to improve is what makes you effectively solve a problem through ML or AI.

Need the Best Quality Dataset? Let us HELP you out!

Creating and maintaining the best quality dataset is not an easy task. Thinking about and maintaining all the things mentioned above is quite a burden. Especially, for a small- to medium-sized companies, managing human resources and technical specialties are very challenging. Therefore, it is often more efficient to find another service that does laborious works (including both collection and preprocessing) for you. For that, we could be your perfect solution!

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Here at Selectstar, we crowdsource our tasks to diverse users located globally to ensure the quality and quantity simultaneously. Moreover, our in-house managers double-check the quality of the collected or processed data! Check us out at for more information! Let us be your HELP!

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