Whitepapers/ebooks

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

    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.

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

    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.

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

    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.

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

    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.

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

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    The current credit scoring model is outdated and in need of an upgrade.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

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  • Taking Control of Your Data: An Essential Guide for Marketers

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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  • How to Improve Your Training Data for Vastly Better Machine Learning

    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.

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

    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.

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  • Mitigating Risk With External Data, A Guide For CROs

    Mitigating Risk With External Data, A Guide For CROs

    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.

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  • Using External Data to Future-Proof Your Organization and Ensure Success Today and Tomorrow

    Using External Data to Future-Proof Your Organization and Ensure Success Today and Tomorrow

    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.

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  • Marketers and Data Science: Tapping Into The Data You Need to Build a Smarter Marketing Organization

    Marketers and Data Science: Tapping Into The Data You Need to Build a Smarter Marketing Organization

    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.

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  • How Data Scientists Can Get a Seat at the Strategy Table

    How Data Scientists Can Get a Seat at the Strategy Table

    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.

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  • The Essential Guide to Feature Selection

    The Essential Guide to Feature Selection

    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.

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  • Data Scientists and Augmented Data Discovery: A Match Made in Heaven

    Data Scientists and Augmented Data Discovery: A Match Made in Heaven

    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.

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  • The Complete Guide For Data Acquisition

    The Complete Guide For Data Acquisition

    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.

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