From Raw Data to Reliable Insights: How Differentiated Data and AI Are Redefining Valuations

Valuation professionals today face an increasingly complex information landscape. Traditional methodologies are being challenged by the sheer volume of available data, rising expectations for transparency, and the accelerating pace of market change. As firms look for ways to boost efficiency and produce more defensible valuations, the combination of accurate, defensible data and advanced AI technologies is emerging as a critical advantage.

Why Standardized Data Matters for Accurate Valuations

Even with GAAP and IFRS standards, companies report financials in different ways, label items inconsistently, and sometimes hide one-time charges in footnotes. This can obscure a company’s true economic performance. Poor data quality is estimated to cost financial organizations $15 million per year in inefficiency[1] and flawed analysis.

At S&P Global, we standardize financial data to ensure valuations are based on consistent, comparable information. Our approach includes:

• Aligning line items across companies: Costs like “COGS,” “cost of sales,” or “cost of product sold” are harmonized for true apples-to-apples comparisons.
• Isolating non-recurring events: Merger costs, asset impairments, and restructuring charges are separated from core operating performance.
• Consistent treatment of share counts and classes: Market cap and enterprise value calculations reflect actual economic ownership.
• Handling accounting changes uniformly: Adjustments for rules like ASC 842/IFRS 16 ensure historical comparability and accuracy.

The outcome is cleaner, more reliable financial data, faster workflows, and analyses that are easier to defend, letting analysts focus on insight generation rather than manual data wrangling.

Unlocking Differentiation Through Data Quality and Coverage

The foundation of any defensible valuation is credible, comprehensive data. Market participants increasingly demand more than standardized point-in-time financials, they need deeper historical context, stronger comparables, and the ability to understand drivers of change with precision.

S&P Global’s breadth of structured and unstructured data, spanning public equities, private markets, supply chain signals, credit indicators, corporate events, fundamentals, and beyond, helps valuation teams form a more complete picture. When combined with Kensho’s AI-powered extraction and normalization capabilities, users can access clean, reliable information at scale, reducing manual work while increasing confidence in their outputs.

AI-Driven Acceleration Without Compromising Accuracy

One of the most significant themes in recent workflows is shift from manual, time-consuming processes to AI-enhanced workflows. Kensho’s suite of machine learning and natural language processing tools allows valuation professionals to:

• Extract insights from unstructured documents such as filings, transcripts, and regulatory materials
• Normalize large-scale datasets quickly and consistently
• Surface relevant comparables based on multidimensional criteria
• Automate repetitive research tasks while preserving human oversight

This combination increases speed and consistency while allowing practitioners to focus on higher-value judgment and analysis.

Strengthening Defensibility and Auditability

Regulatory expectations and stakeholder scrutiny continue to rise. Firms need transparent, well-documented valuation processes supported by traceable data.

AI solutions like those offered by Kensho play a critical role by improving source transparency, strengthening  documentation, and enabling repeatable workflows. With stronger audit trails and more reliable data lineage, teams are better prepared for both internal and external reviews.

Where Valuation Is Heading Next

As the conversation highlighted, the future of valuation work is not about replacing human expertise. It’s about augmenting professional judgment with better data, smarter tools, and integrated workflows that eliminate friction.

Differentiated datasets paired with AI technologies are giving firms the ability to:

• Expand capacity without expanding headcount
• Deliver faster turnaround times
• Reduce operational risk
• Provide richer, more defensible insights to stakeholders

Valuation teams that adopt these capabilities now will be well positioned ahead of increased market expectations and better equipped to navigate whatever the future brings.

[1] Gartner. “How to Create a Business Case for Data Quality Improvement.” Smarter With Gartner, June 2018. https://www.gartner.com/smarterwithgartner/how-to-create-a-business-case-for-data-quality-improvement