Whitepapers/ebooks

In our interconnected, globalized world, it’s harder and harder to track the weak spots in your supply chain. That’s why we created this handy guide to understanding and mitigating supply chain risk.

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.

It’s no secret that while most organizations understand the importance of machine learning, most initiatives never make it off the ground. Follow this guide to guarantee you make it to production.

There’s no one way to start using data science. This guide walks you through the pros and cons of each approach and discusses how to allocate your budget efficiently.

In this in-depth guide, we reveal the real reasons your machine learning risk models are falling short, what you can do to fix them — and exactly how to tackle the problem.

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

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.

Your organization’s risk management strategies are going to need a major overhaul. Insights from your historical data simply won’t be enough to help you assess the risks that are coming your way.

If the current economic crisis has shown the business world anything, it’s that no amount of data analysis can prepare you for the event of having the financial market flipping upside down.

In these increasingly uncertain times, marketing leaders who start thinking data science-driven will not only stay ahead of the pack but also keep their organizations afloat.

How can you earn yourself a seat at the table when decisions are being made? It starts with making yourself accessible and invaluable. Read how you can get started now.

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.

Data science automation has historically focused on hyperparameter tuning and model optimization but now it’s time to see how new tools can empower data scientists to use more and better data.

This complete guide breaks down data acquisition into six steps, including data provider due diligence and data provider tests to uplift your model's accuracy.

It's time to take feature generation - a subset of feature engineering - from an art to a science by opening up additional data sources to achieve breakthroughs in predictive models.