Data and analytics leaders

We explain the difference between machine learning bias and variance, why it’s so hard to fix both at once, and how they can impact your model.

In this post, we explain data acquisition strategies, why your machine learning project depends on it, and how to plan for your team. The post The “IT” Factor: Why Alternative...

In this blog post, we share our top tips for effective predictive model deployment from training and testing to keeping your ML models fresh.

Before we turn the page on 2020, let’s look back at our top ten blogs of the year that was.

In this article, we explain how to know if you need a data science platform, how to approach questions of costs and savings, and what to consider when calculating your TCO.

In this blog post, we look at the key questions you need to ask to make sure you’re using the data preparation tools you really need.

In this article, we explain how CDOs and Data Leaders can use data science to help all parts of the business stay ahead of the curve.

With so much data in your own stores, it’s tempting to think you have all you need to start producing great predictive insights. This might be The post What Is Augmented Data Discovery with...

Many organizations see data acquisition and management as a business expense. You may hear questions like, what does it cost to collect and capture this data? The post Turning Data from a Cost...

Data privacy continues to be a major hurdle for risk officers. In this article, we explain how the global increase in data surveillance creates short term opportunities but long term risks.