Sponsored by: S&P Global | Leveraging Genie to Remove Friction From Exploratory Data Analysis (EDA)

The team pairs Genie inside CIQ Workbench, a white‑labeled Databricks environment, to shift trials from code-first to conversation-first on the Databricks Platform. Organizations evaluating similar environments can see how Genie enables conversational analytics as a foundation for self‑serve data exploration.

The aim is speed and accessibility. Non-technical evaluators can ask plain‑English questions to test coverage and fit, while technical teams still have full notebooks for validation. Shorter cycles and earlier signal discovery drive clearer commercial decisions.

Why early data evaluation stalls for clients

S&P Global delivers a vast catalog that spans hundreds of products and thousands of tables, used by personas from academics to private equity analysts. Traditional trials follow a familiar arc: gather use‑case requirements, map them to datasets, stand up infrastructure, then hand a technical user a notebook to reverse‑engineer schemas and write exploratory SQL. Workbench already reduces friction by spinning up private Databricks workspaces with data and services in place.

Persistent hurdles remained at exploration time. Entity disambiguation makes simple asks, such as "IBM financials," surprisingly brittle when names differ from stored keys. Semantics and business definitions live in PDFs and ad hoc docs, slowing discovery. Many datasets span dozens of normalized tables, so exploratory joins, filters, and coverage checks demand SQL fluency and large time investments before any insight emerges.

Packaging metadata into scalable Genie bundles

Hand‑building a Genie room for each of roughly 90 core datasets was not feasible. The team automated a pipeline that emits an AI bundle per subscribable dataset. Each bundle includes: a README that explains content scope and structure, a curated SQL library of common evaluations, a YAML semantic layer that defines tables, relationships, and descriptions, and a compact context file suitable for use with an LLM.

Bundles map directly into Genie configuration. Catalog entries, table and column descriptions, joins, metrics, and synonyms load automatically so each room starts with strong grounding. A registered function tackles entity recognition, mapping user input like "IBM" to the correct company key before any query runs. Without that step, wildcard searches produced thousands of ambiguous matches, making company‑level questions impractical. With bundles plus entity resolution, rooms become repeatable, accurate starting points that can be provisioned at scale.

What the new first mile looks like

Evaluators now land in a Genie room instead of a notebook. They can ask about dataset scope, coverage start dates, document distributions by year, or records for a specific company, then iterate conversationally. The demo surfaced a real anomaly, an unusual spike in IBM conference presentations during a narrow historical window, which would have taken far longer to uncover through manual SQL alone.

Genie does not replace notebooks. Technical users can drop into the underlying Databricks workspace at any time to validate logic, extend analysis, or productionize work. The key change is the order of operations: conversation first to find signal quickly, code when deeper validation is needed.

Quality, governance, and repeatability at scale

Schema abstraction and semantic grounding compress time to insight for business users, while accuracy is managed through benchmarks and user feedback on room configuration. The bundle approach standardizes documentation, examples, and relationships so new rooms can be provisioned consistently across dozens of datasets and hundreds of engagements, with clear transparency into what each room contains.

Putting a conversational front door on data trials

Genie moves the first touchpoint of evaluation from code to conversation, backed by curated metadata and automated entity resolution. Clients get faster, self‑serve answers to coverage and fitness questions, and teams focus on actual signal rather than schema literacy. That shift turns fragmented, one‑off trials into a repeatable process that scales.