Shelf Blog: Data Management

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...

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...

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...

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...

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?...

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...