Latest Content

Do you have enough data to get the insights you need? And if not, how can you fill the gaps? In part one of this series, we dive deep into auditing, discovery, and acquisition.

In part two of our series, we cover ETL, data wrangling, and data enrichment so you can ensure your data is ready to give you the insights you need.

Part three of our series gets technical with an in-depth look at the best ways to split your data for training, different testing methodologies, feature engineering, and monitoring.

Why are we still hung up on BI? It’s time to embrace a paradigm that empowers us to make smarter, better predictions using real data with machine learning.

ML has gone from buzzword to business necessity, and implementing it is quickly becoming mandatory. Here are 6 easy steps to follow to get going with the resources you have.

GlassesUSA.com increased both conversion rate and order value with Explorium, which lead to a 15-20% increase in the per-session value for their affected segments.

Making your training data better is much easier than you think, and you can use several easy strategies for quick wins.

The current credit scoring model is outdated and in need of an upgrade. Read how to go about building a smarter, more accurate credit scoring model - and the data you need to do so.

Feature selection is a key step in building powerful and interpretable machine learning models, but it’s also one of the easiest to get wrong.

Learn how Explorium fits into every part of the data workflow for machine learning for streamlined data discovery and enrichment.