Research & Resources

• TDWI Checklist Report | Six Steps for Generating Business Value from Operational IoT Data

  This TDWI Checklist focuses on how organizations can develop strategies for improving operational intelligence, particularly by realizing value from IoT data. more

• TDWI Checklist Report | Digital Transformation Using Machine Learning

  This TDWI Checklist Report discusses three broad areas of best practices for helping to make machine learning, and those involved with it, successful. more

• TDWI Checklist Report | Best Practices for Real-Time Analytics on High Data Volumes

  In this checklist, we explore the concept of hybrid transaction/analytical processing (HTAP), an alternative architecture that enables analytics to be performed in concert with transaction processing. We will present best practices for taking advantage of this alternative architecture to enable real-time analytics. more

• TDWI Insight Accelerator | The Cloud Lake: Transforming Business Analytics with Unlimited Scale at Lower Cost

  This TDWI Insight Accelerator focuses on the technical requirements, benefits, emerging trends, and recommendations for deploying a data lake on a cloud platform. more

• TDWI Insight Accelerator | Why the Data-Driven Marketer Needs a Cloud Data Warehouse

  Supporting the marketing function requires attention to a number of technical challenges. Addressing these challenges requires a platform that can adapt to the rapidly evolving universe of marketing tools, services, and data sources. Because many of these services are hosted in the cloud, the logical first approach would be to consider a cloud data warehouse. more

• TDWI Checklist Report | Five Benefits of Modern Data Catalogs

  In addition to automation using AI, next-generation data catalogs often contain new features such as crowdsourcing and collaboration. This TDWI Checklist describes five ways modern data catalogs drive business value. more

• TDWI Checklist Report | Seven Ways to Liberate Data with a Platform for DataOps

  Existing enterprise infrastructures are engineered in a way that complicates some types of data provisioning. In this checklist, we will consider the benefits of a platform-based approach to DataOps that addresses some of these complexities. more

• TDWI Checklist Report | Five Data Engineering Requirements for Enabling Machine Learning

  This TDWI Checklist Report discusses best practices for data engineering and management to support machine learning with a focus on collecting, cleansing, transforming, and governing new types of data for analysis. more

• TDWI Checklist Report | Six Keys to Maximizing Big Data Benefits and Project Success

  This TDWI checklist discusses six important issues that organizations should address to start big data projects off right and then manage them to achieve objectives faster and with less difficulty. more

• TDWI Checklist Report | AI for BI: Six Strategies for Augmenting Business Intelligence with AI and Machine Learning

  This TDWI checklist discusses six best practices for gaining greater value from AI for BI and self-service analytics. Our objective is to help organizations accomplish projects faster and provide relevant and accurate insights that users can trust. more

• Best Practices Report | Multiplatform Data Architectures

  To capture distributed enterprise data, big data, and other new data assets—plus leverage them fully for business advantage—user organizations are expanding and integrating their portfolios of data platforms and tools to create what TDWI calls multiplatform data architectures (MDAs). Download this report to learn what MDAs are and do. more