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  • Essilor Case Study: Harnessing Large, Heterogenous Datasets to Improve Manufacturing Process

    Read this case study to learn how you can stay competitive in a high-speed industry with data streaks and an intelligent data science strategy.

  • The Analyst of the Future

    With the increase in popularity in artificial intelligence (AI) tools, it becomes unclear how much analytics job roles will remain the same. Read this white paper to begin carving out your analytics niche in an industry being disrupted constantly by technology.

  • How To: Address Churn with Predictive Analytics

    Churn is when a company loses a customer - and mostly every business will have to deal with this sooner than later, because it has the power to plateau growth. Read this white paper to learn how to use predictive analytics to predict and prevent customer loss.

  • An Introduction to Machine Learning Interpretability

    Unfortunately, the more accurate a ML model becomes, the less interpretable its predictions become - an inherent dilemma for analysts and data scientists working in regulated industries. Read this white paper to learn how to surmount this catch-22.

  • Executing Data Privacy-Compliant Data Projects

    Dataiku has compiled a data privacy guidebook to keep companies regulation compliant while planning data storage or analytics initiatives by pitting myth vs. reality. Read it here.

  • How to Thrive in the Enterprise AI Era

    Mike Gualtieri (VP & Principal Analyst, Forrester) and Florian Douetteau (CEO, Dataiku) discuss the growing enterprise AI landscape and how machine learning (ML) fits into it. Watch this webcast for more information.

  • The Importance of AutoML for Augmented Analytics

    Read this white paper to learn about the potential of augmented analytics & enterprise AI and the shifting role of the Data Scientist.

  • Enabling AI Services Through Operationalization + Self-Service Analytics

    Read this white paper to learn how to build a holistic data management architecture to support current and future business intelligence, advanced analytics, machine learning, and AI initiatives.

  • Enabling AI Services Through Operationalization and Self-Service Analytics

    Gartner says self-service analytics and BI users will produce more analysis than data scientists will by 2019. Read this extensive guide for all the pitfalls, how-tos and whys in perfecting a data-powered business.

  • Why Teams Need Data Science Tools

    The tools to create a scalable and optimized data science team are readily available, but which provider will you choose? Read this white paper to learn if Dataiku is right for you.

  • Profit from AI and Machine Learning

    Data science is the starting point for AI. Projects in either sector require the same scientific discipline for thoroughly vetting, testing, and comparing results for candidate models and data sets. This eBook by Ovum starts from the ground up. Read on to incorporate AI into your model now.

  • Machine Learning Basics: An Illustrated Guide

    This easy-to-read, entry-level e-book explains all the basics of machine learning, without the technical lingo that bogs it down. Read on for more information.

  • Building Production-Ready Predictive Analytics

    After analyzing results from a recently done survey, Dataiku isolated four different ways companies are dealing with data science production today, and compiled a series of recommendations on how to build production-ready data science projects. Read on for more details.

  • Improving Fraud Detection by Evangelizing Data Science

    With the help of Dataiku and Data Science Studio, leading bank BGL BNP Paribas created a new fraud detection prototype in just 8 weeks, all without compromising data governance standards. Read this case study to learn how Data Science Studio can bring anomaly detection into a business.

  • Future-Proof Your Operations with Predictive Maintenance

    Predictive maintenance (PdM) systems can play a crucial role in reducing burdensome maintenance costs and minimizing machine failures—but is the steep price tag really worth the hype? Download the e-guide now and see why those already implementing PdM technology seem to think so.

  • A Guide to Open Source in Data Science

    This report examines the importance of open source tools in enterprise data science, including the technical and business advantages of open source languages and platforms used by data scientists in the development of ML and AI applications, as well as core platforms including Apache Hadoop and Apache Spark.

  • How to Execute Anomaly Detection at Scale

    A strong data science team and tools can provide top-notch anomaly detection for your organization. This resource provides an overview of anomaly detection, how it works, and when to apply it.

  • Why Teams Need Data Science Tools

    Organizations need to focus (or refocus) their efforts and resources towards establishing competent data teams to make full use of their data. Download this white paper to explore the benefits of a data science platform for providing predictive analytics, transparency, reproducibility, and more.

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