From data chaos to clarity: How Hemlock improved operations with Spotfire

Hemlock Semiconductor transformed its operations by using Spotfire to turn disorganized, siloed data into clear insights. With visual analytics, the company improved decision-making across manufacturing and business functions, enabling faster problem-solving and more efficient use of data.

Back to school with NVivo and XLSTAT: What’s new this year

NVivo and XLSTAT have rolled out major updates over the past year (with more coming soon!)—if you haven’t checked in lately, now’s the time to catch up. These new features are designed to make data analysis faster, smarter, and more accessible—just in time for the new academic year.

Streamlining Information Search Using Gen AI

System Research in Japan used generative AI tools IBM Watson Discovery and IBM watsonx.ai to improve information search across internal documents. By enabling natural language queries and tuning the AI-powered search interface, the company reduced the time needed to find required information by about 50%, uncovering specific details more efficiently and enhancing overall retrieval accuracy.

SingleStore-V: An Integrated Vector Database System in SingleStore

As vector databases continue to gain significant attention due to the rise of Large Language Models (LLMs), there’s an even bigger customer demand for a vector database that maintains optimal performance and interoperability with SQL — all with the ability to combine vector search with filters, joins and full-text search.

The Biggest Technology Trends In Accounting And Finance

The accounting and finance industry is being reshaped by major technology trends such as artificial intelligence, automation, cloud computing, and advanced data analytics. These innovations are transforming how organizations manage financial processes, improve accuracy and compliance, enhance decision-making, and adapt to an increasingly digital and data-driven business environment.

Deploying Models Coverted to ONNX Format

The article explains how developers can deploy machine learning models that have been converted to the ONNX (Open Neural Network Exchange) format, which is an open-source standard for representing models from frameworks like PyTorch and TensorFlow. It highlights the benefits of ONNX such as improved cross-platform compatibility, performance optimizations, and interoperability and outlines basic steps for converting models and deploying them using ONNX runtime and deployment tools.