M Science: Initiation of Coverage

GAII Intelligence defines the Alternative Data & Data Infrastructure market as companies that supply investment firms with non-traditional evidence on company and economic performance — transaction, receipt, location, web and business-spend data — and the tools that collect, clean and deliver it. Buyers are heads of data strategy and data sourcing, fundamental and quantitative analysts, and the compliance teams who must approve each new source.

Capabilities we treat as mandatory

• Point-in-time integrity: history that can be tested without look-ahead, with restatements, panel changes and survivorship handled and disclosed.
• Provenance and screening: a documented chain from source to delivery, with consent, licensing, MNPI and personal-data screening a compliance team can review.
• Investable mapping: data resolved to tickers and entities, so that a signal can be tied to a security without manual work.

Capabilities we treat as differentiating

• Predictive-value evidence: published back tests or, better, independent studies showing that the data anticipates reported results.
• AI-ready delivery: data shares into Snowflake and Databricks, and MCP connections that let a client's AI assistant query the data under entitlement.
• Transparent AI answers: conversational tools that show the query, panel size and method behind each answer and decline unsupported questions.
• AI in the pipeline: models that find sources, map entities and repair collection, cutting the time from idea to usable dataset.

M Science competes with YipitData, Consumer Edge and Bloomberg Second Measure in transaction-based KPI data, with Visible Alpha and consensus providers in KPI estimates, and with Guidepoint and expert networks for sector research.

The following are the capabilities and characteristics that, in our assessment, most distinguish M Science within the Alternative Data & Data Infrastructure market. Each is drawn from publicly documented product capability, company releases and documentation, or independent reporting.

• Point-in-time model: Change-data capture in the Unified Data Model supports point-in-time analysis, historical comparison and back-testing.
• Quant delivery: Snowflake Share, Databricks Delta Sharing, S3, API and more than 600 programmatic endpoints.
• AI-assistant delivery: An MCP server that carries analyst context into ChatGPT, Claude and in-house systems.
• KPI depth: More than 1,440 KPIs on over 1,400 companies across about 40 subsectors (company).
• Analyst research: Written company research and direct analyst engagement alongside the data.
• Continuity: Wholly owned by Jefferies, with research history back to 2002.

The following are factors that prospective clients should weigh, and areas where our public review did not find sufficient evidence to reach a settled view. Several are common across vendors at this stage of the market and are not specific to M Science. We encourage prospective clients to raise each of them directly with the company; we have done the same, and will update this note as answers arrive.

• Assurance: We did not locate SOC 2, ISO 27001, SSO or audit-log documentation.
• AI terms: No AI-use, model-provider or no-training statement was located; buyers should request them before using the MCP server.
• Provenance: Source consent, MNPI and personal-data screening are not publicly documented.
• Panel disclosure: Panel sizes are not disclosed, and coverage figures differ between sources (250+ public companies in February 2026; 1,400+ companies today).
• Evidence: No published predictive-value study or quantified client case was located.
• Pricing: Not published; no trial located.
• Litigation history: A trade-secrets dispute with YipitData over hired staff was settled privately in 2025.

M Science traces to Majestic Research, founded in 2002, which ITG acquired in 2010 and Leucadia National (now Jefferies Financial Group) acquired in 2016, renaming it M Science. It is a wholly owned Jefferies subsidiary with offices in New York and Hong Kong, and has a data subsidiary, Sundial.

M Science does not report revenue or headcount separately. In January 2025 it appointed Spenser Marshall CIO with a focus on AI, and hired Cindy Floyd from GLG as Global Head of Sales and Robert Mohan from Wellington Management as Head of Account Services.

Company-reported figures are shown as such and are not independently verified. Verification is carried into the vendor questionnaire.

M Science sells data feeds and research by sector, now unified under one data model with AI access on top. Figure 2 sets out our assessment of how it maps onto the alternative data workflow.

Unified Data Model

Star-schema, point-in-time model for analyst-curated data (June 2026).

Data feeds and portal

Company, industry and KPI datasets with scheduled deliveries and discovery.

Maddie

Multi-agent copilot with benchmarking, charts, alerts and anomaly detection (February 2026).

MCP server and research

AI-assistant access to data and analyst-written research.

Scope of the product today

We did not locate a security attestation, AI-use terms, panel sizes, a source-screening programme or named client references.

Maddie retrieves from M Science's reports and data through a semantic layer tuned to investment language and KPIs, and the MCP server reuses the same infrastructure.

Model providers are not named, AI in collection and mapping is not described, and no evaluation has been published.

M Science combines transaction, digital purchase, web traffic and technographic data into more than 500 analyst-curated feeds; the Unified Data Model adds point-in-time history and back-testing.

Panel sizes and source screening are not disclosed, which limits provenance scoring under this variant.

M Science has the most complete quant delivery in the segment, with Snowflake, Databricks, S3, API and MCP, plus analyst research and KPI alerts.

Predictive-value evidence is the gap: the model supports back-testing, but no results have been published.

M Science publishes almost no enterprise evidence: no SOC 2, ISO 27001, SSO, audit-log or AI-use documentation was located. Figure 3 sets out what our public review located.

We would encourage buyers to request attestations, AI and model terms, and the source-screening process.

The offsetting factor is ownership: a Jefferies subsidiary with more than two decades of operating history.

M Science does not publish pricing or trial terms; subscriptions are sold by sector and feed.

Buyers should ask for back-test results and quantified references before committing.

M Science sits in the Emerging quadrant of the segment matrix, close to YipitData on Investment Utility but lower on Market & Innovation Strength because of Enterprise Readiness.

GAII Alternative Data & Data Infrastructure coverage, 21 October 2026, scored on the segment variant of Data & Content and Workflow Fit; not directly comparable with Research & Intelligence or Risk, Compliance & Surveillance scores.

11.1 Where M Science is strongest relative to peers

• Against panel vendors, on point-in-time delivery into client data platforms.
• Against KPI estimate providers, on analyst-curated alternative data.
• Against peers, on Workflow Fit.

11.2 Areas of narrower functional focus

M Science leads on delivery and trails on what it discloses.

• Assurance: not located.
• AI terms: not published.
• Panels: sizes undisclosed.
• Evidence: no predictive-value study.

Scores are on a 0–10 scale. Each dimension score is the mean of its sub-criteria, and the GAII Overall Score is the weighted mean of the six dimensions. The detailed sub-criteria scores that build each dimension are set out in Appendix A.

These are the areas where our public review did not find sufficient evidence to form a settled view. We have put them to the company and will update this note when they are answered. Prospective clients may find them a useful agenda for their own evaluation call.

• SOC 2 or ISO 27001 status.
• SSO/SAML and audit logging.
• Model providers behind Maddie and the MCP server.
• No-training and data-retention terms.
• Source consent, MNPI and PII screening.
• Panel sizes and representativeness.
• Current coverage count.
• Back-test or predictive-value results.
• Pricing and trial terms.
• Quantified client references.
• Jefferies information-barrier arrangements.
• Excel delivery.

This note is an assessment of a vendor against a defined framework, not a recommendation to buy or to decline. The following are the considerations we would put at the centre of an evaluation of M Science specifically.

If you run research or data sourcing

• Test UDM history point-in-time against reported KPIs.
• Pilot the MCP server with analysts.
• Use Maddie alerts for KPI monitoring.

If you are responsible for technology, compliance or vendor risk

• Open with Figure 3; request attestations and AI terms.
• Confirm Jefferies information barriers.
• Review data-share entitlements.

If you are assessing the category rather than the company

• Curated data is being rebuilt for AI agents.
• Data shares and MCP now sit side by side.
• Point-in-time integrity is becoming a selling point.

Each dimension score in Section 12 is the mean of the sub-criteria below. Data & Content and Investment Workflow Fit use the Alternative Data & Data Infrastructure segment variant.