TDWI

TDWI Team Training & Enablement helps organizations scale their training to teach the skills, confidence, and execution capability to succeed with modern data, analytics, and AI. Purchase bulk seats to TDWI Workshops and not only earn volume discounts, but also empower entire departments to really make an impact. today to discuss group discounts. Why TDWI?

Dear , I would like to enroll in Hands-On Machine Learning with Python, a series of live, virtual masterclasses by TDWI. Each class meets for 2 hours per week over 6 weeks and includes take home assignments I will complete between sessions. Below are the masterclasses I’m interested in attending: Introduction to Machine Learning with Python – Aug. 11 – Sep. 15 Intermediate Machine Learning with Python – Oct. 1 3 – Nov.

Quantitative market research from TDWI shows that leading organizations are integrating AI, BI, and data management into modernized business processes that accelerate decision-making, automate work, and continuously improve business performance. The TDWI Virtual Summit in November will share the trends shaping intelligent enterprises, practical guidance from experienced practitioners on data-driven operating models, and real-world lessons from organizations that have made the shift.

How much is powering AI with poor-quality data costing your enterprise? Enterprise AI budgets are expanding at a pace that is making CFOs pay close attention. According to a 2026 survey of 2,360 senior executives, companies expect to spend approximately 1.7% of their revenue on AI this year, more than double the 0.8% average from 2025. Measuring ROI from AI is still a nascent practice.

Data Products for AI: Making Enterprise Data Trustworthy and Usable Webinar Speaker: Donald Farmer, Research Fellow Date: Thursday, June 25, 2026 Time: 12:00 p.m. PT / 3:00 p.m. ET Enterprises hold their most valuable operational data inside the ERP systems that run finance, supply chain, and manufacturing. As they turn to AI and advanced analytics, they want that data working in models for forecasting, planning, and day-to-day decisions.

TDWI Digital Dialogue | What Your Lakehouse Needs to Power Agentic AI Is your data truly ready to fuel agentic AI? If an autonomous agent consumed this data, would it understand what a given metric meant? Only about half of respondents to a recent TDWI survey agree that their data is easily accessible and integrated from diverse sources. For organizations that have not yet solved basic integration, agentic AI will compound the problem rather than solve it.

TDWI Blueprint Report | Building an AI-Ready Data Foundation As generative AI, copilots, and agentic AI systems move from experimentation into production, organizations are discovering that long-term AI success depends on the strength of the underlying data foundation. New research from TDWI finds that organizations achieving the greatest business impact from AI have stronger architectural, governance, and operational capabilities.

Upside: Contribute an Article The majority of our articles are written by staff or professional technology authors, but we always have room for thoughtful contributions from within the analytics and data management community. Whether you are a vendor, consultant, skilled technology writer, hands-on BI practitioner, manager, analyst, data scientist, or end user, we are interested in working with you if your article provides valuable, relevant insight for our audience. Why Write for Upside?

Expert Panel: How to Make Enterprise Data Fit for Autonomous Decision-Making Webinar Speaker: Fern Halper, TDWI VP Research, Senior Research Director for Advanced Analytics Date: Monday, June 8, 2026 Time: 9:00 a.m. PT / 12:00 p.m. ET As organizations move beyond generative AI toward agentic systems capable of autonomous decision-making, the demands on enterprise data are increasing significantly. Agentic AI requires data that is not only accessible, but trusted, connected, and rich in context.

TDWI Top Trends | Data Mesh and Cross-Cloud Collaboration: From Architecture Concept to Operating Model The scale and complexity of modern enterprise data have outgrown centralized data warehouses and lakes. This is the reality data mesh was conceived to address. By shifting data ownership from a central platform team to the business domains that produce and understand it, data mesh promises to untangle the bottlenecks that slow analytics and erode trust.