AI Glossary
Smart Data – Short Explanation
Smart data refers to information that is collected, tagged, and structured at the source—making it ready for analysis without additional processing. Unlike raw or “big” data, smart data is actionable, high-quality, and relevant, enabling faster, more accurate decisions in AI and machine learning systems.
In a world where data volumes are exploding, quality matters more than quantity. Smart data reduces inefficiencies, cuts down on storage waste, and enhances real-time decision-making—whether in autonomous vehicles, healthcare, or enterprise systems.
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Smart Data in the Real World
When discussing smart data, think about it as data that formatted in a fashion that allows it to make sense. Traditionally data is compiled from multiple sources and then compiled, formatted, and analyzed on a fixed schedule. This often means that when the information is actually reviewed in person, the information is already out of date.
Smart data helps to transform this paradigm by analyzing the data at the collection point. This not only benefits companies and organizations in terms of time savings but can improve the ability of data to drive actions.
Many organizations believe that collecting data is by itself a virtue. They collect copious amounts of data on their customers, transactions, products, and services in data warehouses and data lakes. They believe that the collection of data with a future intent to use it is the right choice. In reality, a better decision is to collect data that is useful to their business. Data storage is expensive and takes time to gather. Gathering smart data that can be used is a more efficient use of time and resources. In exploring how machine learning is transforming the insurance sector, this detailed examination illustrates the vital role of high-quality, actionable data.
Tip:
Smart data is only as effective as its structure and labeling.
LXT provides custom, high-quality smart data solutions—collected, annotated, and validated by expert teams to accelerate AI training across industries.
Smart Data in the World of AI
When thinking about smart data, a great example is that of self-driving vehicles. Here sensor data needs to be acted upon instantaneously to ensure that the driver and passengers are protected and kept safe. Sensors that are able to receive data and analyze the information is only one step in the process. These sensors also need to make decisions so that a vehicles steering and braking are influenced. The integration of machine learning significantly enhances the accuracy and reliability of such data interpretations, further contributing to safer and more autonomous driving experiences.
However, smart data is both data at the edge (IoT and similar) as well as data that has been processed and categorized. The latter is ideal for ML as a tool to train and educate algorithms. Using smart data in this way lets ML algorithms process data in an unsupervised fashion.