We rely on data to inform decision-making, drive innovation, and maintain a competitive edge. However, data is not static, and over time, it can undergo significant changes that impact its quality, reliability, and usefulness. Understanding the nuances of these changes is crucial if you aim...
Historically, we never cared much about unstructured data. While many organizations captured it, few managed it well or took steps to ensure its quality. Any process used to catalog or analyze unstructured data required too much cumbersome human interaction to be useful (except in rare...
From the Library of Alexandria to the first digital databases, the quest to organize and utilize information has been a reflection of human progress. As the volume of global data soars—from 2 zettabytes in 2010 to an anticipated 181 zettabytes by the end of 2024 – we stand on the verge of a new...
Large language models analyze datasets to derive patterns and rules as a method of learning and replicating human intelligence. As you can probably guess, the dataset used in a model can dramatically alter its understanding. We’ve used a number of analogies to explain the significance of this, but it boils down to the same principle: the inputs in LLMs greatly influence the outputs.