The Disadvantages of Having Too Little Data You might think it is rather apparent that a project needs large quantities of data, but sometimes, even large businesses with access to structured data fail to procure it. Training on limited or narrow data quantities can stop the machine learning models from achieving their full potential and increase the risk of providing wrong predictions.While there is no golden rule and rough generalization is usually made to foresee training data needs, it is always better to have large datasets than suffer from limitations. The data limitation that your model suffers from would be the limitations of your project. What to do if you Need more Datasets
Although everyone wants to have access to large datasets, it is easier said than done. Gaining access to large quantities of datasets of quality and diversity is essential for the project’s success. Here we provide you with strategic steps to make data collection much easier.Open Dataset Open datasets are usually considered a ‘good source’ of free data. While this might be true, open datasets aren’t what the project needs in most cases. There are many places from which data can be procured, such as government sources, EU Open data portals, Google Public data explorers, and more. However, there are many disadvantages of using open datasets for complex projects.When you use such datasets, you risk training and testing your model on incorrect or missing data. The data collections methods are generally not known, which could impact the project’s outcome. Privacy, consent, and identity theft are significant drawbacks of using open data sources.Augmented Dataset When you have some amount of training data but not enough to meet all your project requirements, you need to apply data augmentation techniques. The available dataset is repurposed to meet the needs of the model.The data samples will undergo various transformations that make the dataset rich, varied, and dynamic. A simple example of data augmentation can be seen when dealing with images. An image can be augmented in many ways – it can be cut, resized, mirrored, turned into various angles, and color settings can be changed.Synthetic DataWhen there is insufficient data, we can turn to synthetic data generators. Synthetic data comes in handy in terms of transfer learning, as the model can first be trained on synthetic data and later on the real-world dataset. For example, an AI-based self-driving vehicle can first be trained to recognize and analyze objects in computer vision video games.Synthetic data is beneficial when there is a lack of real-life data to train and test your trained models. Moreover, it is also used when dealing with privacy and data sensitivity.Custom Data Collection Custom data collection is perhaps ideal for generating datasets when other forms do not bring in the required results. High-quality datasets can be generated using web scraping tools, sensors, cameras, and other tools. When you need tailormade datasets that enhance the performance of your models, procuring custom datasets might be the right move. Several third-party services providers offer their expertise.To develop high-performing AI solutions, the models need to be trained on good quality reliable datasets. However, it is not easy to get hold of rich and detailed datasets that positively impact outcomes. But when you partner with reliable data providers, you can build a powerful AI model with a strong data foundation.Do you have a great project in mind but are waiting for tailormade datasets to train your models or struggling to get the right outcome from your project? We offer extensive training datasets for a variety of project needs. Leverage the potential of Shaip by talking to one of our data scientists today and understanding how we have delivered high-performing, quality datasets for clients in the past.