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



  • Speeding Analytics with Spark, Data Lakes and Change Data Capture

    Powerful new engines such as Apache Spark can drive real-time transformation and analysis of operational data from customers, transactions, supply chain events, and myriad other sources that feed modern data lakes. Change data capture (CDC) can also play a big role in getting data into the pipeline and data lake at the speed necessary for modern operational reporting and analytics. August 28, 2018 View Now

  • The Three Pillars of Data Governance: Compliance, Trust, and Transformation

    Many organizations initiate data governance programs because of pressing compliance issues that impact data usage. At the other end of the spectrum, some organizations begin by governing data that’s shared broadly through a community of consumers across the enterprise, each with their own perspective on what constitutes quality and usability. August 30, 2018 View Now

  • Putting Data Governance at the Heart: Shortening Healthcare’s Path to AI

    Organizations in healthcare and other industries are putting a priority on generating high-quality, data-driven insights for reducing costs, increasing quality, adhering to regulations, and building better patient relationships. Yet, that’s always easier said than done, especially as the complexity of collecting and integrating data only rises with time. In the healthcare industry, providers and payers are facing dramatic changes that affect every aspect of their businesses, from patient care to clinical cost management to payments to regulatory reporting and auditing. At the same time, competitive pressures from new players entering the industry—from retail, technology, and other sectors as well as digital start-ups—mean that to survive and prosper, organizations need to gather, integrate, and analyze more and different types of data. September 11, 2018 View Now

  • Improving Data Sharing Through Modernized Data Accessibility

    Organizations have struggled to easily provide access to sharable data assets ever since the early days of decision support systems. Traditional methods of data sharing are slow and cumbersome. Simple data sharing methods—such as emailing extracted files, opening up unsecured access to critical databases, or file exchanges via FTP or EDI—not only pose risks of inadvertent data exposure, they also fundamentally require manual steps for both the individual sending the data and the individual receiving the data. September 12, 2018 View Now

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