CDAO Fall USA 2026

Attendees, exhibitors & solutions

Who this event is for

Industry trends

Key trends shaping this market and its wider industry.

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Frequently asked

Glossary

1

CDAO (Chief Data & Analytics Officer)

Executive who owns enterprise data strategy, analytics, and increasingly AI delivery. The CDAO Fall programme is built around this role: balancing data value creation with governance, platform cost, and regulatory exposure.

2

Data mesh

Decentralised operating model in which domain teams own and publish data as a product, supported by a central platform and federated governance. Frequently discussed at CDAO Fall as an alternative to a single central data team.

3

Data fabric

Metadata-driven architecture that stitches together distributed sources through virtualisation, cataloguing, and automated integration. Vendors in the exhibition typically position fabric alongside lakehouse and catalog tooling.

4

Data contract

A machine-readable agreement between a data producer and its consumers covering schema, semantics, quality thresholds, and change notification. It is the practical enforcement layer behind data-product thinking.

5

Lakehouse

Architecture that combines cheap object storage with warehouse-style transactions, catalog, and SQL performance, usually via open table formats such as Delta, Iceberg, or Hudi. A common platform choice debated in analytics-leader sessions.

6

Semantic layer / metrics layer

A governed definition of business metrics, dimensions, and joins that sits between raw tables and BI or AI consumers, so dashboards and Copilots return the same number for the same question.

7

Feature store

A system that computes, stores, and serves machine-learning features consistently for training and real-time inference, reducing train-serve skew in production AI.

8

MLOps and LLMOps

Engineering practices for versioning, testing, deploying, monitoring, and retraining models. LLMOps extends them to prompt versions, retrieval indexes, evaluation sets, guardrails, and token-cost control for generative AI.

9

RAG (retrieval-augmented generation)

Pattern that retrieves relevant enterprise content at query time and injects it into an LLM prompt, so answers are grounded in governed data rather than model memory. A dominant GenAI delivery pattern in 2025-2026 data programmes.

10

Vector database and embeddings

Embeddings convert text, images, or records into numeric vectors; a vector store indexes them for similarity search. The retrieval backbone for RAG, semantic search, and recommendation use cases.

11

Data observability

Continuous monitoring of freshness, volume, schema, distribution, and lineage so pipelines fail loudly before downstream dashboards or models do. Usually evaluated alongside data quality rule engines.

12

Master data management (MDM)

Discipline and tooling for creating trusted golden records for customers, products, suppliers, or reference data, then syndicating them to operational and analytical systems.

13

Data catalog and metadata management

Inventory of datasets, owners, lineage, classifications, and usage that underpins discovery, access control, and audit. Increasingly the control plane for AI-ready data.

14

AI governance and model risk management

Policies, controls, documentation, and testing that make AI systems accountable: model inventory, bias and fairness checks, explainability, human oversight, and mapping to frameworks such as the NIST AI RMF or the EU AI Act.

15

Data clean room

Controlled environment where two parties join and analyse datasets without exposing row-level records, used for advertising measurement, partner analytics, and privacy-sensitive collaboration.

16

Privacy-enhancing technologies (PETs)

Techniques such as differential privacy, tokenisation, secure multi-party computation, and synthetic data that reduce re-identification risk while keeping analytical utility.

17

DataOps

CI/CD-style automation, testing, and observability applied to data pipelines, with the aim of shortening release cycles and reducing manual firefighting for analytics engineering teams.

18

Data literacy and enablement

Programmes that teach business teams to read, question, and use data with governed self-service tooling. A recurring CDAO people-and-culture topic alongside platform investment.