Research & Resources

• TDWI Checklist Report | The Modern Data Hub

  This TDWI Checklist Report presents seven recommendations for successful data hub design and use. It should help you understand the new direction that the data hub has taken as well as what you should demand when evaluating products and deploying a modern data hub. more

• TDWI Checklist Report | Achieving High-Value Analytics with Data Virtualization

  This Checklist Report discusses six areas that are critical to achieving high-value, business-driven analytics and the role data virtualization plays in realizing success in these areas. more

• TDWI Checklist Report | The Cloud Data Integration Primer

  A number of newly mature trends are making cloud-based data integration platforms, technologies, and user best practices more relevant than ever. more

• TDWI Pulse Report | Developing Data Strategy for IoT

  This TDWI Pulse Report discusses some best practices for developing an IoT data strategy. It examines the organizational as well as the data and analytics aspects of such a strategy. This includes organizational alignment, understanding the unique nature of IoT, and other issues at play when managing and analyzing this “new” kind of data. more

• TDWI Checklist | Five Data Management and Analytics Best Practices for Becoming Data-Driven

  It can be difficult to create an organization that thrives on data and analytics. This TDWI Checklist Report discusses best practices to build a program and an infrastructure for becoming data-driven. more

• Best Practices Report | Practical Predictive Analytics

  TDWI research indicates that it is in the early mainstream stage of adoption. Yet, even as organizations continue to adopt predictive analytics, many are struggling to make it stick. The purpose of this report is to explore concrete best practices for building and deploying predictive models in organizations. more

• TDWI Checklist | Strategies for Improving Big Data Quality for BI and Analytics

  Organizations dependent on big data for a wide range of business decisions need data quality management that can improve the data so it is fit for each desired purpose. This TDWI Checklist Report offers six strategies for improving big data quality. more

• TDWI Checklist Report | The Automation and Optimization of Advanced Analytics Based on Machine Learning

  Machine learning is being used today to solve well-bounded tasks such as classification and clustering. Note that a machine learning algorithm learns from so-called training data during development; it also learns continuously from real-world data during deployment so the algorithm can improve its model with experience. This report will drill into the data, tool, and platform requirements for machine learning with a focus on automating and optimizing ML's development environment, production systems, voracious appetite for data, and actionable output. more

• TDWI Checklist Report | Data Architecture for IoT Communications and Analytics

  The foundation of a successful IoT implementation is a technical architecture that blends network connectivity with an information architecture for streaming, ingesting, filtering, and capturing data. This checklist explores some fundamental aspects of the data architecture necessary for IoT success. more

• TDWI Checklist Report | Seven Best Practices for Machine Learning on a Data Lake

  As organizations collect and analyze increasing amounts of data, they are turning to the data lake as the platform to perform more advanced analytics such as machine learning. This TDWI Checklist Report presents best practices for advanced analytics on a data lake. more

• TDWI Checklist Report | Modernizing Data Warehouse Infrastructure

  Users ignore the modernization of deep warehouse infrastructure at their peril. Without it, they may achieve complete, clean, and beautifully modeled data, but without the ability to scale to big data, iterate data models on the fly, enable flexible self-service access, operate continuously and in real-time (as warehouses must in global businesses), and handle new data types and workflows for advanced analytics. more