Resource Center

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.

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.

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.

The right data is a competitive edge. Over the past several years fintech, hedge funds, and investment companies have started to augment their conventional data sources with alternative data.

Are your KYC processes streamlined enough to get the answers you need, fast? Or are valuable customers dropping off before you get the chance to onboard them?

It’s no secret that external data can transform organizations’ data science and advanced analytics, but finding it is easier said than done. See how a data acquisition strategy helps

In this whitepaper, we cover some of the most common errors in ML initiatives, and best practices to avoid them

How can marketers leverage all their data for better predictive insights? It’s all about knowing what you need, how it can help, and the right platforms and tools that can help achieve your goals.

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.

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.

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.

The current credit scoring model is outdated and in need of an upgrade.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.

Explore our resource Rethinking Data Acquisition with Explorium. Without the right technology – and the right data – many analytics and ML projects never get off the ground.

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.

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 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.

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.