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  • The Data ROI Toolkit: How to Determine the Value of Your Data Initiatives

    This white paper by Dataiku gauges your company’s ultimate ROI—balancing pros like time saved and reduced data expenditure against cons like workflow disruption and the cost of additional training. Download it to see ways that your company can make sure to improve your ROI and derive true value from your data.

  • 2020: AI TRENDS FOR THE NEW DECADE

    Read this analyst report to learn the 6 AI trends to watch for in 2020 and beyond to make sure your organization is not left behind, as well as key factors that will evolve the role of the data scientist.

  • Defining a successful AI project

    Leveraging a centralized data platform might be the secret to staying ahead of the technological curve. Access this white paper to read the 5 Ws and an H AI prioritization check list, learn how to avoid false starts on AI projects that are ill-defined, and achieve an environment of success.

  • Powering the Future: Use Cases for an AI in Utilities & Energy

    Resource and utility companies that hope to remain competitive should be aggressively pursuing the next technological frontier. Access this white paper to learn the 3 immediate steps that companies looking to advance in the race to AI can take as well as some of the highest-value AI use cases to date.

  • Predictive Web Applications for Better Forecasting, Pricing, and Management

    By leveraging a collaborative and flexible AI-based tool, Europcar was able to build a predictive web app as well as dashboards that forecasted market activity at a very granular level. Access this case study to learn more about the technology Europcar used and the positive impact the solution had.

  • Fraud and Anomaly Detection in Banking

    AI-based systems can augment regulatory alert systems and improve analysts’ workflow by reducing noise without discarding results. Access this paper to view the 3 types of anomalies to look out for, understand examples of how banks use fraud and anomaly detection, and learn how to build a basic, machine learning-based fraud detection system.

  • Fraud Detection in Healthcare

    There are many AI-based use cases that span the healthcare industry, all with the goal of improving patient care. Access this white paper to learn the 3 basic types of anomalies that may be detected and to learn how to confront the challenges of fraud and anomaly detection with 4 AI and ML-based approaches.

  • AI in Banking Challenges, Solutions, & Steps to Get Started Now

    Understand this: AI is a journey and cannot happen overnight, but, if you start now, it can deliver huge efficiency boosts that will help you own the massive data flows that business intelligence and analytics projects demand. Read this white paper to learn about the top current AI challenges—and how to address them.

  • Data Architecture Basics: an Illustrated Guide for Non-Technical Readers

    Don't assume that only CIOs or data architects should understand data structure, it's a skill that every member of an organization should be familiar with if true data democratization is the goal. Read this white paper for data architecture basics that can integrate any team member into the mission for data-driven decision-making.

  • Use Cases for AI in Human Resources Plus the Challenges and Solutions for Execution

    Businesses are leveraging intelligent analytics programs to more effectively recruit and retain high-quality employees, programming the tech to independently work through myriads of human applicants and discern the best fit(s). Read this white paper to find out what else businesses are using AI for in their HR teams.

  • Data Science Operationalization

    Read this white paper to find common ground between data science and IT teams for the benefit of your data and analytics projects as a whole by ensuring consistent packaging and deployment of models.

  • How and Why IoT is Shifting Enterprise AI Strategy

    Businesses will not be able to yield the full benefits of IoT integration without a better structure for their data and analytics—and this means mastering AI and ML too. Read this white paper to learn how to enable true innovation through data and IoT.

  • Practical Deep Learning for Cloud & Mobile

    You may be surprised to discover just how graspable machine learning and deep learning algorithms really can be. Read this e-book preview for a glimpse into the deep learning universe using Keras—a powerful, easy-to-use framework with predictive analytics capabilities.

  • Marketing Artificial Intelligence for Dummies

    As things currently stand, marketing teams must use data to continuously innovate and disrupt their business. This means introducing transparency and democratization to empower everyone throughout the enterprise and the marketing team to draw and use data insights on their own—read this white paper to learn how.

  • GE Aviation: From Data Silos to Self-Service

    In the case of GE Aviation, they were able to boast innovative new products and huge efficiency boosts by implementing a self-service analytics solution—ensuring that all business efforts could be quantified at scale. Read this case study to learn how.

  • From The Trenches: A Survey Report

    Over 100 data professionals at the EGG Conference in New York City were surveyed about their current data science and machine learning roadblocks on the path to enterprise AI—read this white paper for the results to learn how you can avoid the same struggle points.

  • Why Enterprises Need Data Science, Machine Learning, and AI Platforms

    Ultimately, it's about saving time and getting organizations into the AI game sooner rather than later—read this white paper to learn more about data science and ML platforms and how they allow for more productive data initiatives.

  • Enabling Chief Data Officers, Data Leaders, and the Data Revolution

    CDOs can drive data strategy, support machine learning analysis, and generate business value with data. Read this white paper (including insights from over 50 CDOs worldwide) to learn how to create an organizational path to data leader success.

  • Advanced Analytics in Healthcare Toward Efficient, Precise, and Personal Medicine

    Read this white paper to learn how NLP, AI, machine learning, IoT, and other recent data science innovations can alter (for the better) the way healthcare is run.

  • 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.

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