Whitepapers/ebooks

Explorium 2021State of External Data Acquisition

Optimize Your Analytics — Why You Need a Data Acquisition Strategy

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

The Most Common Errors in ML Projects and How to Avoid Them

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

2020 Data Science Review (and What to Expect in 2021)

In this 2020 wrap-up, we polled our in-house experts, drawing together their tips and insights for the end of the year and for what's to come in 2021.

How to Include Data Science Platforms in Your 2021 Budget

In this brand new guide, you’ll discover the key business benefits of switching to a data science platform, whether to buy or build your own, and top tips for calculating your TCO.

The Guide to External Data for Better User Experiences in Financial Services

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?

Part One - Making Sense of Data: Auditing, Discovery, and Acquisition

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.

Part Two - Making Sense of Data Prep: ETL, Wrangling, and Data Enrichment

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 - Making Sense of Deployment: Feature Engineering, Training, Testing, and Monitoring

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.

Building the Dream Team: Who Should Be Part of Your Data Science Organization?

Creating a data science team is about so much more than tracking down the right job titles or developing the right algorithms. Find out the key roles you need to build a rock star data science team.

Making Alternative Credit Scores the Norm: How to Create a New Scoring Model

The current credit scoring model is outdated and in need of an upgrade.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

Taking Control of Your Data: An Essential Guide for Marketers

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.

Where's the Weakest Link? Understanding Risk in Supply Networks

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.

AI is Making BI Obsolete, and Machine Learning is Leading the Way

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.

How to Deploy and Future-Proof Your Models: From Theory to Production

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.

Start Small and Scale Smart: Do You Need a Data Science Team, Platform, or Service?

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.

How Can You Enhance Your Risk Models? Finding Better Signals to Feed Them

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.

How to Improve Your Training Data for Vastly Better Machine Learning

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

6 Steps to Jumpstart Machine Learning Using the Resources You Already Have

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.