Research & Resources

Research Reports


  • TDWI Advanced Analytics Maturity Model TDWI Advanced Analytics Maturity Model Guide Cover

    The TDWI Advanced Analytics Maturity Model is designed to help assess your current use of advanced analytics and how that compares to others. It provides a framework for companies to understand where they are in the journey, where they’ve been, and what they still need to do to succeed with technologies including predictive analytics and machine learning. Read Now

  • TDWI Self-Service Analytics Maturity Model

    The TDWI Self-Service Analytics Maturity Model Guide is designed to help you understand the phases of maturity in self-service analytics, interpret your assessment scores, and provide best practices to move you forward. Its insight and advice can help you quickly advance your self-service analytics initiatives to gain more value. Read Now

  • TDWI Hadoop Readiness Assessment

    TDWI's Hadoop Readiness Assessment is an online questionnaire that asks how prepared you and your organization are to get full value from Hadoop. When you complete the questionnaire, the assessment tool immediately shows you scores that quantify your readiness for Hadoop. It is designed to help answer your questions about where to focus your best efforts with Hadoop. Read Now



  • Practical Predictive Analytics – Results of New TDWI Best Practices Research

    Predictive analytics is now part of the analytics fabric of organizations. TDWI research indicates that it is in the early mainstream phase of adoption. Yet, even as organizations continue to adopt predictive analytics and machine learning, many are struggling to make it stick. Challenges include lack of skills, executive and organizational support, and data infrastructure issues. June 21, 2018 View Now

  • Modern Data Architectures to Support Modern Analytics

    Many organizations today are scrambling to meet the needs of new data types and analytics. TDWI research shows that companies are often analyzing data from multiple sources, including structured data, unstructured data, real-time streaming data, location data, and transactional data. They are making use of new techniques such as text analytics and machine learning, and they are moving towards self-service analytics. The traditional data warehouse or data mart is often limited in its ability to support these modern analytics in a fast and friendly way. July 12, 2018 View Now

  • How to Design a Data Lake with Business Impact in Mind

    A quarter of organizations surveyed by TDWI in 2017 say they already have a data lake in production, while another quarter say their lake will be in production within 12 months. Although data lakes are still rather new, user organizations have adopted them briskly. Why has the data lake gotten so popular, so fast? July 24, 2018 View Now

  • Achieving High-Value Analytics with Data Virtualization

    Analytics projects are critical to business success, and as a result, they are growing in size, number, complexity, and perhaps most important, in their data requirements. TDWI finds that data scientists, business analysts, and other personnel need to view and access data that resides in multiple sources, both on premises and in the cloud, to draw insights from data relationships and discover important patterns and trends. July 31, 2018 View Now

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