The advent of artificial intelligence has also significantly accelerated the data revolution. Big data has become even more significant as businesses must collect increasingly large amounts of data to power the growing and complex artificial intelligence models they are using.
Although conventional wisdom typically holds that bigger means better, that’s not necessarily the case when it comes to artificial intelligence. The level of data and energy required for large models can render them inefficient in terms of cost, resource usage and time. In fact, in some cases, smaller models might be able to outperform larger ones, especially for highly specialized use cases. So, the amount of data used to train a model is not as important as the quality and freshness of that data.
But what do we mean when we talk about the “quality” and “freshness” of data used to train artificial intelligence models? Data must exhibit several characteristics to be considered “high-quality”: accuracy, completeness, consistency, uniqueness and relevance. Data freshness adds another dimension, requiring data to be both timely and current.
Why Are Data Quality and Freshness Important for AI
High-quality and fresh data are essential for artificial intelligence because they improve model accuracy, reduce bias, enhance adaptability and prevent costly retraining. Rather than relying on massive outdated datasets, effective AI models perform better when trained on timely, accurate and relevant data.
Why Do Data Quality and Freshness Matter?
In machine learning, the principles of data quality and data freshness are paramount, as this can determine how effectively representative the output of the artificial intelligence model is of the real world. Key benefits of using high-quality and fresh data for AI training include:
Accuracy and Reliability
Using high-quality data ensures that the output of artificial intelligence models is accurate and reliable. Because AI models rely on the data they are trained on, any errors in that data — including factual errors, gaps and biases — will be reflected in their outputs.
Reduced Bias
Training data that lacks diversity can cause algorithms to perpetuate or even amplify existing social biases. This tendency is frequently observed in examples like facial recognition systems and hiring tools. High-quality training data can help mitigate the shortcomings of artificial intelligence.
Cost Effectiveness
Training artificial intelligence on low-quality data can lead to project failures that necessitate retraining the model. Using high-quality data from the beginning can avoid this costly and time-consuming process.
Adaptability
One of the biggest challenges for artificial intelligence models is maintaining their accuracy and relevance over time. New trends and changing conditions can render training data obsolete. Fresh data allows artificial intelligence models to maintain their relevance for as long as possible.
Improved Generalization
Although AI bases its responses on pre-existing data, many models have begun to pick up on the process of generalization — going beyond the examples they were trained on. When the data used to train the model is of high quality, the model is better equipped to handle unknown situations.
This leaves many businesses with the question of how to collect and process this fresh and high-quality data, however. Due to the process of data decay, which can occur at varying rates depending on the intended use of the data, businesses are essentially faced with a situation where they require a steady stream of new, incoming data to maintain data quality and freshness.
This situation is where the process of public data scraping comes into play. Public data scraping uses automation tools to read, collect, organize and store information that is available online to anyone with an internet connection. When pulling data from the internet, where information is constantly refreshed and updated, businesses can rest assured that they are using the most up-to-date data available.
Public data scraping is instrumental in the proliferation of artificial intelligence scraping, as it enables AI model users to create extensive, cost-effective and timely data sets that can be used during the training process. Additionally, since public data scraping draws only on publicly available data, this process avoids many of the ethical concerns associated with AI training. As a result, companies that use public data scraping to power their AI training gain a competitive edge thanks to the relevance, diversity and ethicality of their data.
A Real-World Example of Data Quality and Freshness
For example, we once worked with a polar navigation NGO that had the mission of lowering the risk of ship-iceberg collisions. Although the NGO already had a route optimization engine in place, it was based on government ice charts that were anywhere from four to six years old and far too slow for captains threading dynamic ice fields. The solution? Creating an end‑to‑end satellite‑data pipeline and a purpose-built computer vision model that would deliver live, bandwidth-friendly and real-time iceberg detection intelligence.
By using fresher, higher-quality data, the NGO was able to enhance its capabilities and provide real-time iceberg detection alerts. The time of an average reroute cycle was trimmed significantly, as was blind-zone sailing distance.
This case study is just one example of how using high-quality data can help models become more accurate, efficient and cost effective. In cases like this, where millions of dollars and potentially even lives are at stake, using high-quality data is not a luxury but a necessity.
Indeed, in the era of artificial intelligence, data quality and freshness are among the most crucial factors a business utilizing AI models can achieve. Rather than using large, broad models with potentially irrelevant or outdated databases, companies should prioritize smaller models with fresher, higher-quality data collected through methods such as public data scraping. In doing so, they can prepare themselves to yield the best possible results from their AI models.
