Taking account of...

Glass, Lewis & Co. (commonly referred to as Glass Lewis) is a leading provider of independent proxy advisory services and corporate governance solutions.¹

Alongside its primary competitor, Institutional Shareholder Services (ISS), Glass Lewis controls over 90% of the proxy advisory market. They play a critical role in the financial ecosystem by advising institutional investors (like pension funds and mutual funds) on how to vote their shares at company annual meetings.²

Core Services

Glass Lewis provides research and technology to help investors manage "proxy voting"—the process where shareholders vote on company matters like board elections and executive pay.³

Proxy Paper Research: Their flagship product, which provides in-depth analysis and voting recommendations on over 30,000 shareholder meetings annually across 100+ global markets.⁴
Viewpoint Technology: A SaaS platform used by institutional investors to manage their voting workflow, implement custom policies, and handle reporting.⁵
ESG & Compensation Data: Detailed tools for analyzing Environmental, Social, and Governance (ESG) risks and modeling "Pay-for-Performance" to see if executive compensation aligns with company results.⁶
Report Feedback Statement: A tool that allows public companies to submit a response to Glass Lewis’s research, which is then delivered directly to the investors before they vote.⁷

Ownership and Structure

Headquarters: San Francisco, California (with major offices in New York, London, Sydney, and Tokyo).⁸
Ownership: As of March 2021, Glass Lewis is owned by Peloton Capital Management (a private equity firm) and Stephen Smith (a Canadian financial entrepreneur).⁹ They acquired the company from the Ontario Teachers' Pension Plan and AIMCo.¹⁰
Market Reach: Their clients collectively manage over $40 trillion in assets.¹¹

Current Trends and 2026 Outlook

As of late 2025, Glass Lewis is undergoing several significant strategic and regulatory shifts:¹²

Initiative	Description
End of "Benchmark" Policy	Glass Lewis has announced it will stop publishing a single "Benchmark" voting recommendation by 2027. Instead, they are moving toward AI-driven, client-specific frameworks tailored to each investor's unique philosophy.
New 2026 Guidelines	For the 2026 proxy season, they have updated their Pay-for-Performance model, replacing letter grades (A-F) with a numeric scorecard (0-100) based on six quantitative tests.
Regulatory Pressure	In December 2025, the U.S. executive branch issued an order calling for increased oversight of proxy advisors, specifically targeting their influence on ESG and DEI matters.

Why They Matter

When Glass Lewis recommends a "Vote No" on a company's director or a compensation plan (Say-on-Pay), it can trigger a significant loss of support from major shareholders. Because many large funds do not have the internal resources to analyze thousands of companies, they rely heavily on Glass Lewis to provide the "independent" view on whether a company's governance is sound.

ICE Data - Revenue and Profitability

2025-12-20T12:43:00.005+00:00

ICE Data (officially categorized under the Fixed Income and Data Services segment) is an exceptionally profitable business and serves as the "steady engine" for its parent company, Intercontinental Exchange (ICE).¹

As of late 2025, this segment is a high-margin, recurring revenue powerhouse that protects the company from the volatility of the stock and energy markets.²

1. The Numbers: Revenue and Profitability (2024–2025)

Based on the most recent financial reports, the segment generates billions in high-margin revenue with a significant portion being "sticky" subscriptions.³

Annual Revenue: In 2024, the Fixed Income and Data Services segment generated $2.3 billion in net revenue.⁴ By Q3 2025, quarterly revenue grew to $618 million, putting the segment on track for a record year.⁵
Operating Margins: This is a highly efficient business.
- GAAP Operating Margin: Roughly 37–39%.⁶
- Adjusted Operating Margin: Approximately 45%.
Recurring Revenue: About 80% of the segment's revenue is recurring (subscription-based). This means that even if trading volumes on the NYSE drop, ICE Data continues to collect fees for pricing feeds, indices, and analytics.

2. What Makes it So Profitable?

The profitability of ICE Data stems from its "proprietary" nature. Because ICE owns the exchanges (like the NYSE), they own the data generated by those exchanges.

Pricing & Reference Data: This is their "crown jewel." Thousands of financial institutions rely on ICE for the "End of Day" prices of bonds. Because there is no central exchange for many bonds, ICE’s evaluated pricing is the industry standard.
ICE Indices: They provide the benchmarks for hundreds of ETFs.⁷ As more money moves into passive investing, ICE collects a "basis point fee" on the Assets Under Management (AUM) tied to those indices.
Low Marginal Cost: Once the data infrastructure is built, adding a new customer costs almost nothing, meaning every new dollar of revenue is nearly 100% profit.

3. Comparison to Other Segments

Within the broader ICE portfolio, "Data" sits between the high-flying Exchanges and the recovering Mortgage arm:

Segment	Adjusted Margin	Role in Company
Exchanges	~75%	High risk, high reward; driven by market volatility.
Fixed Income & Data	~45%	The Stabilizer. Predictable, high-growth subscription income.
Mortgage Technology	~35-42%	High growth potential but sensitive to interest rates.

4. Strategic Outlook for 2025/2026

In 2025, ICE has focused on "ICE-Q," a next-generation data platform that uses AI to analyze proprietary mortgage and climate data.⁸ This is part of their strategy to move from "selling raw data" to "selling insights," which typically command even higher price points and margins.

Profits of Data Providers.

2025-12-20T12:37:00.004+00:00

In the world of market data, S&P Global and LSEG (London Stock Exchange Group) are the two "Goliaths" that compete directly with ICE and Bloomberg. While they all sell data, their business models and profitability profiles differ significantly based on their "moats."

Here is how they compare in terms of profitability and strategy as of late 2025.

1. S&P Global: The "Margin King"¹

S&P Global is widely considered the most profitable of the group because of its Ratings and Indices businesses, which have incredibly high barriers to entry.²

Profitability (2025): S&P Global maintains an adjusted operating margin of 48.5% – 49.5%.³ This is significantly higher than a traditional bank or tech company.
The "Market Intelligence" Segment: This is their direct data competitor to Bloomberg and LSEG (Workspace/Refinitiv). In 2025, this segment saw an acceleration to 7% organic growth.⁴
The Moat: They own the S&P 500. Every time an ETF provider (like BlackRock or Vanguard) launches a fund tracking the S&P 500, they pay S&P Global a fee based on the assets in that fund. It is essentially a "tax" on global investing.
Strategic Move (2025): In April 2025, S&P announced the spin-off of its Global Mobility unit (which includes Carfax) to focus entirely on high-margin financial data and AI-driven analytics.

2. LSEG: The "Integrated Titan"

Since acquiring Refinitiv in 2021, LSEG has transformed from a stock exchange into a data and technology giant.⁵ About 70% of its revenue now comes from data, not trading.

Profitability (2025): LSEG’s Adjusted EBITDA margins are approximately 48%, though its GAAP operating margins are lower due to the massive amortization costs from the $27 billion Refinitiv acquisition.
Data & Analytics Segment: This is their largest division, generating over £4 billion ($5B+) in annual revenue. In 2025, they successfully "sunsetted" the old Eikon platform, moving users to the newer Workspace platform.
The Microsoft Partnership: The biggest story for LSEG in 2025 is their 10-year partnership with Microsoft.⁶ They are currently rolling out AI-powered "Copilots" directly into Excel and Teams, allowing bankers to pull LSEG data using natural language.
The Moat: They own FTSE Russell (indices) and Tradeweb (fixed income trading). Like ICE, they own the "trading venues," which gives them proprietary data that others have to pay to access.

Direct Comparison: Data Segment Performance (2025 Est.)

Feature	S&P Global	LSEG (Data & Analytics)	ICE Data Services
Annual Revenue	~$14.5 Billion (Total)	~$11 Billion (Total)	~$2.4 Billion (Data only)
Operating Margin	~49%	~48% (EBITDA)	~45%
Growth Rate	6% – 8%	5% – 7%	4% – 5%
Core Product	Capital IQ / S&P 500	Workspace (Refinitiv)	Bond Pricing / NYSE Data
Best For...	Equity research & ESG	Multi-asset trading & FX	Fixed income & Mortgages

The "Battle of 2025": AI and Unbundling

Both S&P and LSEG are fighting a two-front war in 2025:

Generative AI: S&P is using its Kensho AI unit to automate the tagging of millions of documents, while LSEG is leveraging the Microsoft Azure cloud to integrate data into the "daily workflow" of users (moving away from the standalone terminal).
Fighting the "Bloomberg Tax": Both firms are positioning themselves as the "Value" alternative to Bloomberg. By offering better Excel integration and "unbundled" data feeds (where you only pay for what you use), they are successfully winning contracts from banks looking to cut their $300M Bloomberg bills.

Market Data Analyst

2025-12-20T12:20:00.002+00:00

At a company like Substantive Research, a Market Data Analyst plays a specialized role that sits at the intersection of fintech, procurement, and investment operations.

Unlike a typical "Market Analyst" who studies stock trends, an analyst here focuses on the market for data itself. Substantive Research specializes in bringing transparency to the often opaque world of how much financial firms (like asset managers and hedge funds) pay for their data feeds, indices, and research.¹

Here is a breakdown of what a Market Data Analyst does in this specific environment:

1. Data Normalization and "Apples-to-Apples" Comparison²

The biggest challenge in this industry is that every vendor (like Bloomberg, MSCI, or LSEG) has different pricing models.³ One firm might pay per user, while another pays based on Assets Under Management (AUM).⁴

What the analyst does: You take raw, messy contract data from clients and "normalize" it. You break down complex agreements into granular "pricing drivers" (e.g., number of licenses, geographic locations, and specific data modules).⁵
The Goal: To ensure that when you compare two firms' spending, you are comparing identical services.⁶

2. Benchmarking and Peer Analysis⁷

Substantive Research's core product is its proprietary benchmarking database.⁸

What the analyst does: You match a client’s spend against the wider market and specific peer groups.⁹ You identify if a client is overpaying by 20% for a specific ESG data set or if they are getting a "wholesale" discount compared to firms of a similar size.
The Goal: To provide the client with "leverage" for their next contract renewal.¹⁰

3. Vendor Trend Tracking

Vendors constantly change their products and pricing structures (e.g., moving from one-off fees to "consumption-based" models).

What the analyst does: You monitor the major providers—Index providers, Rating agencies, and Research platforms.¹¹ You track which vendors are gaining market share and which ones are aggressively increasing prices.¹²
The Goal: To alert clients to "inflationary" trends in data costs before they hit their budgets.

4. Client Advisory and Reporting

Since Substantive Research was acquired by Euronext, there is a greater emphasis on integrated technology and high-level reporting.¹³

What the analyst does: You prepare "Spend Analytics" reports that visualize where a firm's money is going.¹⁴ You might join calls with a Head of Market Data or a Chief Procurement Officer to explain why their costs are rising and which vendors offer the best "value for money."
The Goal: To turn raw numbers into a narrative that helps a firm save hundreds of thousands of dollars.

Core Skills You’ll Use

Expert Excel/Data Skills: You’ll spend a lot of time in spreadsheets or data visualization tools (like Tableau or Power BI) managing large datasets.
Knowledge of the "Financial Plumbing": Understanding what an ISIN is, how an index is constructed, and the difference between "terminal" and "data feed" pricing.
Attention to Detail: Because your analysis is used in multi-million dollar negotiations, a small error in a "per-user" calculation can ruin the firm's credibility.

Why this role is unique

At a traditional bank, you are a consumer of data. At Substantive Research, you are a market expert. You see the "bill" for the entire industry, giving you a bird's-eye view of the financial information economy that very few people have.

Star Schema

2025-12-08T14:34:00.005+00:00

Copilot demo

2025-12-08T14:19:00.002+00:00

Copilot Demo

AI tools to assist Data Governance

2025-11-23T10:56:00.003+00:00

Here are some real-world examples and categories of AI tools used to assist Data Governance (DG) in a bank, explained in simple terms:

1. 🔍 AI-Powered Data Cataloging and Discovery

The Problem: Banks have data everywhere: databases, spreadsheets, emails, document scans. Finding all the specific pieces of sensitive data (like a customer's old account number or address) is a monumental task.

The AI Tool: Intelligent Data Catalogs (e.g., solutions from vendors like Collibra, Alation, or Microsoft Purview).¹

How AI Helps:
- Automated Tagging: AI uses Natural Language Processing (NLP) to "read" data and automatically tag it.² Instead of a person manually labeling a database column as "PII" (Personally Identifiable Information), the AI recognizes patterns like names, dates of birth, or Social Security numbers and labels them instantly and consistently across the entire bank.
- Data Lineage Mapping: AI automatically maps the "family tree" of data.³ It shows exactly where a piece of data started, every system it passed through, and every report it ended up in. This is critical for audits and proving compliance ("Where did this number come from?").

2. ✅ AI for Continuous Data Quality Management

The Problem: Bad data (typos, duplicates, missing values) can cost the bank money and lead to bad decisions or failed regulatory reports. Checking billions of rows for errors is impossible for people.

The AI Tool: Data Quality & Anomaly Detection Engines (e.g., solutions from vendors like Ataccama or features within larger platforms).

How AI Helps:
- Smart Anomaly Detection: AI models learn what "normal" data looks like. If a system suddenly shows a spike in transactions over a certain limit, or if the number of new customer records drops to zero for a day, the AI flags it instantly as an anomaly (an unusual event). This catches errors, system failures, or potential fraud in real-time.⁴
- Predictive Cleansing: The AI recognizes common data quality issues (like inconsistent address abbreviations—"St." vs. "Street") and automatically suggests or applies the correction, ensuring data is standardized before it gets used.⁵

3. 🛡️ AI for Regulatory and Privacy Compliance

The Problem: Banks must follow constantly changing rules (like GDPR, Basel III). Translating these complex legal documents into technical rules for every system is time-consuming and prone to human misinterpretation.

The AI Tool: AI Regulatory Change Management Platforms (e.g., solutions like Compliance.ai or OneTrust).

How AI Helps:
- Intelligent Policy Mapping: AI is fed new regulations. It uses NLP to understand the text, extract key mandates (e.g., "customer must have the right to be forgotten"), and automatically link those mandates to the specific internal data, systems, and policies that need to be updated.
- Automated Audit Trails: For every access, modification, or deletion of sensitive data, the AI creates an unchangeable record (an audit trail) that proves the bank followed its policies and the law.⁶ This makes passing a regulatory audit much faster and easier.⁷

4. 🧠 AI for Model Governance (The "Black Box" Problem)

The Problem: Banks increasingly use sophisticated AI models (like Machine Learning) to approve loans or detect fraud.⁸ Regulators require banks to prove these models are fair, not biased, and that their decisions can be explained.

The AI Tool: AI Model Governance Platforms (e.g., IBM watsonx.governance or Fiddler AI).⁹

How AI Helps:
- Explainability: These tools help "look inside the black box" of an AI model, generating an explanation for a decision (e.g., "This loan was declined because the applicant's credit utilization score was 15% above the threshold").¹⁰ This satisfies regulatory requirements for transparency.
- Bias Detection: The AI monitors the models for bias.¹¹ For example, it can check if the loan approval model is unfairly rejecting applications from a specific demographic group, allowing the human governance team to fix the underlying data or the model itself before it causes legal trouble.

These AI tools fundamentally shift data governance from a manual, reactive checklist to a proactive, automated, and continuous system.

AI usage in Data Governance

2025-11-23T10:41:11.566+00:00

🤖 Your AI Sidekick for Data Governance in the Bank

The banking world runs on data. Every deposit, loan application, and transaction creates a massive amount of information. To keep all this sensitive data safe, accurate, and compliant with strict government rules (like GDPR or Basel), banks rely on Data Governance (DG). Think of DG as the set of rules, roles, and processes that ensures data is handled properly.

But here’s the problem: manually managing this tidal wave of data is practically impossible. That's where Artificial Intelligence (AI) steps in, acting as a powerful, tireless assistant to the human data governance team.

The AI Advantage: Automation and Precision

Instead of replacing the human experts, AI takes on the most time-consuming, repetitive, and error-prone tasks. This frees up the human team to focus on the big-picture strategy.

1. The Super Sleuth for Data Quality

A bank has millions of records. If a customer's address is misspelled in one system and their name is slightly different in another, it creates a data quality issue.

AI's Role: AI tools, specifically Machine Learning (ML), can scan all these records instantly. They don't just follow simple rules ("Is the address blank?"). They learn what good data looks like, spotting subtle errors, inconsistencies, or duplicate entries that a human reviewer would miss. They can automatically suggest corrections or flag the record for review, ensuring the bank's data is clean, accurate, and reliable for everything from credit scores to financial reports.

2. The Automatic Data Classifier

Banks have different types of data: highly sensitive personal data (like Social Security numbers), less sensitive marketing data, and public financial data. Regulatory rules are different for each type.

AI's Role: AI can automatically read and classify data the moment it enters the system. For example, it can instantly recognize a cell phone number and label it as Personally Identifiable Information (PII). This allows the bank's security system to apply the correct, strict access controls immediately, minimizing the risk of unauthorized viewing. This saves countless hours of manual tagging and ensures the bank is compliant with data privacy laws right from the start.

Real-Time Compliance and Risk Mitigation

In the financial world, speed is essential, especially for compliance. Regulators require banks to prove they are following the rules at all times.

3. Continuous Compliance Monitoring

Traditional DG involves periodic audits—checking the data system a few times a year. This leaves long gaps where problems can develop unnoticed.

AI's Role: AI systems provide real-time monitoring. They constantly watch how data is being used and accessed. If an employee suddenly tries to download an unusually large number of customer files, the AI flags it as an anomaly, potentially stopping a security breach or regulatory violation before it happens. This predictive and proactive approach drastically reduces risk and the chance of massive fines.

4. Automated Policy Enforcement

When a new privacy rule comes out, thousands of internal data policies might need updating.

AI's Role: AI can translate those new regulatory mandates into technical rules that are automatically enforced across all systems. If a policy states that customer data must be deleted after seven years, the AI automatically identifies those records and initiates the deletion process, removing the chance of human error and ensuring the bank doesn't illegally retain old information.

By using AI as a DG assistant, banks can transform their governance from a slow, expensive, and reactive effort into a fast, efficient, and proactive part of their daily operations. This doesn't just reduce risk; it builds greater trust with customers and allows the bank to use its data to innovate faster and smarter.

Data Governance in Banking.

2025-11-23T10:34:00.001+00:00

🏦 The Imperative of Data Governance in Banking: Trust, Compliance, and Opportunity

In the modern financial landscape, data is perhaps a bank's most valuable, yet most vulnerable, asset. The sheer volume, velocity, and variety of information—from customer transactions and personal details to risk assessments and financial reports—make robust Data Governance (DG) not just a best practice, but a foundational imperative. Without a solid DG framework, a bank risks everything: crippling regulatory fines, irreversible damage to customer trust, and a hampered ability to leverage data for innovation.

The Pillars of Effective Data Governance

Data governance is the established continuous process of creating and improving policies and standards around data management. Its core principles in banking revolve around three critical pillars: Accountability, Quality, and Security/Compliance.

Clear Accountability and Stewardship: Who owns the data? A successful DG framework starts by defining Data Ownership and assigning Data Stewards—individuals or departments held explicitly accountable for the quality, security, and compliance of specific data assets. This eliminates ambiguity and ensures that data integrity is prioritized across all business units.
Data Quality Management: Inaccurate or inconsistent data undermines every decision, from credit risk modeling to regulatory reporting. DG enforces standards and validation processes to ensure data is accurate, consistent, and reliable. This involves processes like data cleansing, data validation at the point of entry, and continuous monitoring of quality metrics. High-quality data is mission-critical for informed decision-making and accurate reporting.
Regulatory Compliance and Security: The banking sector is among the most heavily regulated industries. Data governance is the mechanism for ensuring adherence to complex frameworks like GDPR, CCPA, and Basel Accords. This involves implementing comprehensive policies for data classification, role-based access controls, encryption, and data retention. By enforcing security measures and maintaining detailed audit trails, banks can confidently demonstrate compliance and protect sensitive customer information from unauthorized access or breaches.

Driving Value Beyond Risk Mitigation

While risk management is a primary driver, effective data governance unlocks significant competitive advantages:

Enhanced Decision-Making: High-quality, well-managed data provides the reliable foundation needed for advanced analytics and AI/ML initiatives. This allows banks to gain deeper market insights, evaluate performance effectively, and make strategic choices based on factual evidence.
Operational Efficiency: Standardized data processes and clear data catalogs reduce the time employees spend searching for, verifying, and reconciling information. This improves cross-departmental collaboration and overall operational agility.
Customer Trust and Loyalty: Protecting customer data through robust privacy and security controls is fundamental to maintaining public trust. When customers feel their data is safe and handled responsibly, loyalty is strengthened, minimizing reputational risk.

In a world where data is constantly being generated and regulatory scrutiny is increasing, banks must move past treating DG as a reactive necessity and embrace it as a strategic enabler of growth and innovation.

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Taking account of...

AI Prompts for Data Analysis: Spreadsheets, Charts and Insights

AML Intelligence

AI

AI 2026

Tableau

MDM

ESG Data

MIT DL + Harvard Python

API EIA

Independent proxy advisory services

Core Services

Ownership and Structure

Current Trends and 2026 Outlook

Why They Matter

ICE Data - Revenue and Profitability

1. The Numbers: Revenue and Profitability (2024–2025)

2. What Makes it So Profitable?

3. Comparison to Other Segments

4. Strategic Outlook for 2025/2026

Profits of Data Providers.

1. S&P Global: The "Margin King"1

2. LSEG: The "Integrated Titan"

Direct Comparison: Data Segment Performance (2025 Est.)

The "Battle of 2025": AI and Unbundling

Market Data Analyst

1. Data Normalization and "Apples-to-Apples" Comparison2

2. Benchmarking and Peer Analysis7

3. Vendor Trend Tracking

4. Client Advisory and Reporting

Core Skills You’ll Use

Why this role is unique

Star Schema

Copilot demo

AI tools to assist Data Governance

1. 🔍 AI-Powered Data Cataloging and Discovery

2. ✅ AI for Continuous Data Quality Management

3. 🛡️ AI for Regulatory and Privacy Compliance

4. 🧠 AI for Model Governance (The "Black Box" Problem)

AI usage in Data Governance

🤖 Your AI Sidekick for Data Governance in the Bank

The AI Advantage: Automation and Precision

1. The Super Sleuth for Data Quality

2. The Automatic Data Classifier

Real-Time Compliance and Risk Mitigation

3. Continuous Compliance Monitoring

4. Automated Policy Enforcement

Data Governance in Banking.

🏦 The Imperative of Data Governance in Banking: Trust, Compliance, and Opportunity

The Pillars of Effective Data Governance

Driving Value Beyond Risk Mitigation

Copilot

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Nasdaq Futures (NQ)

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Modern Data + SQL

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1. S&P Global: The "Margin King"¹

1. Data Normalization and "Apples-to-Apples" Comparison²

2. Benchmarking and Peer Analysis⁷