Implementing a knowledge management system or exploring your knowledge strategy? Before you begin, it’s vital to understand the different types of knowledge so you can plan to capture it, manage it, and ultimately share this valuable information with others. Populating any type of knowledge base...
Data decay is the gradual loss of data quality over time, leading to inaccurate information that can undermine AI-driven decision-making and operational efficiency. Understanding the different types of data decay, how it differs from similar concepts like data entropy and data drift, and the...
Retrieval-augmented generation (RAG) is an innovative technique in natural language processing that combines the power of retrieval-based methods with the generative capabilities of large language models. By integrating real-time, relevant information from various sources into the generation...
A data mesh is a modern approach to data architecture that decentralizes data ownership and management, thus allowing domain-specific teams to handle their own data products. This shift is a critical one for organizations dealing with complex, large-scale data environments – it can enhance...
The terms “data science” and “data analytics” are often used interchangeably, but they represent distinct fields with different goals, processes, and skill sets. Understanding the differences between these two disciplines is crucial for professionals who work with data, as...
A data lakehouse is a modern data management architecture that’s designed to handle diverse data types and support advanced analytics. It’s a valuable tool for data scientists, project managers, AI professionals, and organizations that rely on data-driven decision-making. As businesses...
Choosing the right data format can significantly impact how well you manage and analyze your data, especially in big data environments. Parquet, a columnar storage format, has gained traction as a go-to solution for organizations that require high performance and scalability. Parquet offers...
The ability to manage, store, and analyze vast amounts of data is critical to your organization’s success. As you generate more data from diverse sources, you must choose the right infrastructure to handle this information efficiently. Two of the most popular solutions are data lakes and...
Data littering refers to the creation and distribution of data that lacks adequate metadata, thus rendering it difficult to understand, manage, or reuse. In a world where organizations rely heavily on accurate and accessible information, data littering means your data quickly loses its...
Generative AI has presented businesses with unprecedented access to data and the tools to mine that data. It’s tempting to see all data as beneficial, but the older-than-AI rule, Garbage In, Garbage Out, still applies. To truly understand the effectiveness and safety of GenAI in your...
Machine learning (ML) systems often operate behind complex algorithms, leading to untraceable errors, unjustified decisions, and undetected biases. In the face of these issues, there is a shift towards using interpretable models that ensure transparency and reliability. This shift is crucial for...
The terms “AI interpretability” and “explainability” (XAI) are frequently used but often misunderstood. This confusion is an expected part of grappling with a field that is itself in a state of rapid development and debate. This article aims to clarify the distinction...