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. However, preparing data cleanly and at scale has proven harder than expected.
For decades, teams have built ETL and data pipelines to copy this data into separate systems for analysis. Those pipelines multiply, grow fragile, and cost more each year to maintain; the copied data often arrives without clear lineage, leaving no reliable way to trace its origin or audit how a figure was calculated. AI raises the stakes, because a model trained on ungoverned data can be a danger to the entire business.
Today, a different approach to the data foundation is taking hold: keep core business data where it lives, with shared governance, full lineage, and access control; meanwhile make it usable alongside cloud, IoT, and third-party sources without repeated copying.
Drawing on enterprise practice and TDWI research, this webinar examines what this new approach changes for analytics and AI, where it lowers cost, and which use cases it serves best, including forecasting, demand planning, and generative and agentic AI.
Donald Farmer, TDWI research fellow, and industry-leading experts from SAP, Databricks, and NTT DATA will discuss how attendees can evaluate their current approach against the demands of AI, and how to ensure your best business data reaches the people and models who use it. Key takeaways:
- Why decades of extraction pipelines left organizations with cost and complexity, but little lineage or auditability
- How keeping core business data in place, under shared governance, changes the economics of analytics and AI
- What separates a governed, open data foundation from the self-service dashboards most teams run today
- Which AI and machine-learning use cases depend on access to trusted business data, from demand planning to generative applications
- How to judge readiness, and avoid lock-in, when modernizing the path from operational systems to analytics
Guest Speakers
Abdelhalim Dadouche
Senior Product Specialist
Databricks
Abdelhalim Dadouche is a senior product specialist at Databricks, where he leads strategic engagements focused on helping global enterprises modernize their data and AI platforms. He specializes in bridging operational systems such as SAP with modern lakehouse architectures to enable trusted, scalable analytics and AI use cases.
With deep expertise in data platforms, cloud technologies, and enterprise architecture, and beyond his work with customers, Abdelhalim contributes to the broader Databricks ecosystem by enabling partners, developing thought leadership on data and AI architectures, and participating in industry events and conferences. He brings a practical perspective on how organizations can turn their enterprise data into reusable data products that support advanced analytics, machine learning, and generative AI.
Asad Mahmood
NTT DATA
Principal Solution Architect
In his current role, Asad has the opportunity to spend time with customers to understand their business challenges and, in turn, devise and propose appropriate solutions. His specialty lies in data and analytics, which allows him to understand and gauge such challenges and help customers make sense of the myriad of terms that they may be faced with: big data, machine learning, distributed storage, self-service, embedded analytics to mention a few!
Despite the constant evolution in technology, which has been fascinating over recent years, the essence of what Asad does, and customers require, remains constant: relevant information to drive effective decisions. Another facet of his role allows him to focus on the technological developments emanating from SAP, and as part of this, he strives to maintain hands-on experience with the data and analytics portfolio. This allows him to provide practical and relevant advice to customers.
Sandeep Dhingra
Global Head, Data Architecture Advisory
SAP
Sandeep Dhingra is the global head of data architecture advisory at SAP, where he leads a team of data architects helping organizations build the trusted data foundations required to accelerate AI adoption and drive business transformation. He partners with customers to design modern data architectures that unlock the full value of enterprise data and enable intelligent, data-driven decision making.
A hands-on technology executive and data strategist with more than 20 years of experience, Sandeep works closely with C-suite executives and technology leaders to align business objectives with technology investments. Throughout his career, he has architected and delivered multiple large-scale enterprise data warehouses and analytics platforms across a wide range of technologies, helping organizations modernize their data landscapes and maximize the value of their data assets. His expertise spans data architecture, cloud platforms, enterprise data management, analytics, and AI, with a focus on delivering practical, scalable solutions that drive measurable business outcomes.
Donald Farmer