S&P Global Marketplace is where S&P Global presents its data and solutions, together with those of curated third-party providers, for a buyer to explore and evaluate. This guide sets out what S&P Global's own pages say about it, what a buyer gets from a tile, a Blueprint and the Query Library, how S&P Global maps entities to its identifiers, and how to write the same kind of query profile for a company dataset you load yourself. Every statement about S&P Global was checked against the pages linked here on 4 October 2026.
Fokals is delivered direct, by REST API and as bulk files in JSON, JSON Lines or CSV, which you load into your own warehouse with its own loader and join to S&P Global content on the identifiers you hold.
What S&P Global says the Marketplace is
The page for the Marketplace in S&P Global's investor fact book calls it a discovery platform that uses AI to show data and solutions from across S&P Global alongside those of curated third-party providers. It says users can explore offerings from all five divisions of S&P Global, together with Kensho and select third-party partners, and that each offering has a tile holding visualisations, sample data, research and technical documentation. The page reports more than 330 tiles, 80 of them added in 2024.
S&P Global's release of 6 February 2024 describes the Marketplace in the same terms and announces AI-enabled search on it, developed by Market Intelligence and Kensho.
The pages linked here describe the Marketplace as a place to discover and evaluate. They name the third-party providers as curated or select but do not list them, and they say little about how a purchase is contracted. For each tile you want, ask S&P Global which delivery route carries it, which licence covers it, what it costs and which identifiers it uses.
What a tile, a Blueprint and a query give you
The fact book names the tools a buyer uses to evaluate an offering.
| Tool | What S&P Global's pages say | What to do with it |
|---|---|---|
| Tile | A page for each offering, with visualisations, sample data, research and technical documentation | Open the sample and the technical documentation first, and check the grain, the keys and the dates |
| Generative AI Search | Answers questions about offerings in natural language and recommends other relevant datasets and services, built with Kensho on the platform's information, metadata and documentation | Use it to find tiles, and confirm each answer on the tile itself |
| Blueprint | Shows how datasets and solutions combine for a use case such as private markets or energy transition, with the datasets required, the workflow, a code notebook and a technical video | Read the notebook to see which identifiers join the datasets |
| Query Library | Over 1,300 pre-built queries, each with a profile page that gives a description, key metadata and the type of query | Reuse a query as a pattern and write your own in the same form |
Queries are sorted by type, the fact book says, with MSSQL, PostgreSQL and Snowflake as examples, and can run on premises or in the cloud. A profile page also records where the data comes from, which datasets the query uses and which data packages it needs. That structure is worth copying, because a query that states what it reads and what it needs can be reused by someone who did not write it.
How S&P Global maps entities to its identifiers
A join to S&P Global content starts with S&P Global's own identifiers. Its release of 2 October 2023 describes Kensho Link as a machine learning service that matches the entities in a customer's own records to identifiers in S&P Global's company database. It says Link can return Capital IQ identifiers and the identifiers of a cross-reference dataset, the Business Entity Cross Reference Service, in one step. That dataset cross-references public and private entities, with corporate hierarchy relationships and identifiers such as LEIs.
Three things follow for a buyer. S&P Global's company database has its own entity identifiers, so a join to another vendor's data runs through a crosswalk unless that data already carries them. The LEI is a candidate shared key, because the release names it among the identifiers the cross-reference holds and a listed company carries its LEI in Fokals tables. And the other identifiers Fokals carries, ticker with MIC, ISIN and share-class FIGI, are the ones to ask whether S&P Global's crosswalk holds. The entity resolution entry explains why the match is measured and not assumed.
Writing a query profile for your own data
The same parts make a profile for any dataset you load. The example is a query on Technology Changes, part of the marketing stack dataset: the companies that added a named technology in the last 30 days.
| Part of a profile | The example |
|---|---|
| Description | Companies that added a named technology in the last 30 days |
| Data source | Fokals marketing stack dataset |
| Datasets used | Technology Changes |
| Type | Standard SQL with one parameter, :technology_id |
select
company_id,
company,
ticker,
isin,
observed_at
from company_tech_events
where category = 'technology'
and key = :technology_id
and change = 'added'
and observed_at >= current_date - 30
order by observed_at desc;Four details belong on the profile, because each changes what a row means.
- The
keycolumn holds the technology id when the category istechnology, so the query takes it as a parameter. - A first observation is a baseline and writes no change, so a tool already in place when a website is first observed does not appear as an adopter.
- Every change carries its observation time, the moment it was observed. That makes the feed point-in-time: a query as of any past day returns what was known that day.
- A removal is confirmed before it is written, so the brief absences caused by consent banners and tests stay out of any churn query built on
removed.
Every row carries the company ID and, where the company or its parent is listed, the ticker, ISIN, LEI and share-class FIGI. That is what lets the result join to S&P Global content or to any other table you hold. The data dictionary names each column used here.
A Blueprint outline for private markets
S&P Global's Blueprints show how datasets combine for a use case, and the fact book names private markets as one. The same shape works for the Fokals datasets, and the outline below gives a Blueprint in that form: what it is for, the datasets it needs, the workflow and the code.
| Part | The outline |
|---|---|
| Use case | Private companies that raised money and then opened roles |
| Datasets required | Company Funding in the intent dataset and Hiring Activity in the hiring dataset |
| Workflow | Take the private capital raises reported in regulatory filings, then add the postings the company opened in the 90 days after the filing |
| Code | The query below |
-- Write the date arithmetic in your warehouse's syntax.
select
f.company_id,
f.company,
f.filed_at,
f.amount_sold,
sum(h.new_postings) as new_postings_90d
from company_funding f
join company_hiring_daily h
on h.company_id = f.company_id
and h.day > f.filed_at
and h.day <= f.filed_at + 90
where f.form = 'D'
and f.company_id is not null
group by f.company_id, f.company, f.filed_at, f.amount_sold;Use filed_at, the date the filing was made, and not first_sale, because the sale can pre-date what anyone could read. The query keeps the raises that are tied to a company, so every row of the result carries a company ID and the identifiers to join on. The use case on following private funding from regulatory filings treats the raises in depth.
What each is built for
S&P Global describes credit ratings, benchmarks and the company database that Kensho Link maps to among its offerings. Fokals is built for company-level evidence: the technologies a website runs, the roles a company advertises, what it announces and files, and weekly intent scores in which every score carries the dated signals behind it. Every observation is dated and written once, so a join to S&P Global content supports monitoring and point-in-time tests.
Sample data for the companies you track is sent on request, with the data dictionary and methodology.
Frequently asked questions
What is the S&P Global Marketplace?
S&P Global's investor fact book calls it a discovery platform that uses AI to show data and solutions from across S&P Global alongside those of curated third-party providers. Each offering has a tile holding visualisations, sample data, research and technical documentation. The fact book reports more than 330 tiles, of which 80 were added in 2024.
Does the S&P Global Marketplace include alternative data?
S&P Global says it includes offerings from curated third-party providers beside its own. The pages linked here do not name those providers or say which of their offerings are alternative data, so check the tiles for what is listed now. A tile's sample data and technical documentation show what a dataset holds before you ask about a licence.
What is the Query Library on the S&P Global Marketplace?
It is a library of pre-built queries, over 1,300 according to the fact book. A query's profile page says what it does, where its data comes from, which datasets and data packages it needs and what type of query it is, for example MSSQL, PostgreSQL or Snowflake. Queries can be run on premises or in the cloud and serve as building blocks for longer workflows.
How do I evaluate a dataset on the S&P Global Marketplace before I license it?
Open the tile's sample data and technical documentation, check the keys, the dates and the identifiers against the companies you track, and run a pre-built query where one exists. Then ask S&P Global which delivery route and licence apply. The same steps work on any vendor's sample, including a Fokals sample for your own list of companies.
How is Fokals data delivered for a join to S&P Global content?
Fokals is delivered direct, by REST API and as bulk files, and licensed by written agreement. You load the data into your own warehouse with its own loader and join it to S&P Global content on a shared identifier, such as an LEI, which a listed company carries in Fokals tables.
How can I combine company signals with S&P Global data?
Join on an identifier both sides carry, and measure the match before you build on it. Fokals tables carry ticker, MIC, ISIN, LEI and share-class FIGI for listed companies, and brands and subsidiaries carry their parent's. S&P Global's 2023 release describes Kensho Link as a way to map entities to its identifiers, and the guide to mapping alternative data to a security master works through the join on the Fokals side.
The queries and code on this page are examples to adapt. Test them in your own environment before you rely on them.
What this page says about the products it names was checked against their public documentation on 4 October 2026. Product and company names are trademarks of their owners. Fokals is not affiliated with them or endorsed by them.