Shelf Blog: Data Management

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...

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...

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...

By organizing complex data into an interconnected web that mirrors the complexity of the real world, knowledge graphs enable deeper, more actionable insights for use by Generative AI (GenAI). Knowledge graphs are more than supportive frameworks. They are fundamental operators that amplify the...

Self-supervised learning (SSL) is rapidly transforming the field of artificial intelligence by enabling AI models to learn from vast amounts of unlabeled data. This innovative approach lets AI systems create their own labels and uncover hidden patterns within the data. By leveraging SSL, you can...