Shelf Blog

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...

When it comes to data quality, unstructured data is a challenge. It often lacks the consistency and organization needed for effective analysis. This creates a pressing need to address data quality issues that can hinder your ability to leverage this data for decision-making and innovation. As you...

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...

As companies work to ensure the accuracy, compliance, and ethical alignment of their AI systems, they are increasingly recognizing the importance of AI audits in their governance toolkits.  What Is an AI Audit? An AI audit is a comprehensive examination of an AI system that scrutinizes its...

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...

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...

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...

Data modeling is an important practice of modern data management. It involves creating abstract representations of data to better understand and organize your information. This lets you design databases and other data systems that are efficient, reliable, and scalable.  What is Data Modeling?...

Few-shot prompting is a powerful technique that enables AI models to perform complex tasks with minimal data. This method is valuable for organizations looking to leverage AI capabilities without the extensive data requirements and training costs typically associated with traditional AI...