Shelf Blog

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...

Propensity score matching (PSM) is a statistical technique that reduces bias in observational studies. By calculating the probability of treatment assignment based on observed characteristics, PSM creates balanced groups for more accurate comparisons.  In business, PSM is used to evaluate the...

As your data ecosystem grows, so does its complexity and its need for careful organization. Data orchestration is the coordination and management of complex data workflows across various systems and platforms. This process is essential for organizations of all sizes, but particularly for those...

Building an ETL pipeline is crucial for organizations looking to effectively manage and analyze their data. An ETL pipeline automates the process of extracting data from various sources, transforming it into a suitable format, and loading it into a target system for analysis. Depending on the...

Managing large-scale datasets efficiently and effectively is crucial for any organization. Traditional table formats often struggle to keep up with the evolving demands of modern data analytics, leading to performance bottlenecks, data integrity issues, and increased operational...

Data minimization is a critical concept that ensures organizations collect, process, and store only the necessary data required for their specific purposes. This approach not only helps in complying with various data protection regulations but also enhances data security, reduces operational...

Data wrangling is an essential process in data analytics that transforms unstructured data into a clean and usable format. As businesses increasingly rely on data-driven decision-making, the importance of efficient data wrangling cannot be overstated. It can make all the difference between a...