Industry Primer — Business Services
Research and data analytics companies provide market research, credit reporting, business intelligence, financial data, and decision analytics. The sector includes credit bureaus (Equifax, TransUnion, Experian), financial data providers (S&P Global, MSCI, FactSet), and market research firms (Gartner, Forrester). The combined market exceeds $100 billion. These businesses benefit from high switching costs, regulatory-mandated data requirements, and increasing demand for data-driven decision-making.
Demand is robust as every industry increases data utilization. Financial data and analytics benefit from growing asset management complexity. Credit data demand is supported by lending volumes and fraud prevention needs. ESG data and ratings are a fast-growing segment despite methodology debates. Market research spending is shifting from traditional surveys to AI-powered analytics using alternative data sources.
Over five years, AI will enhance every data analytics offering. Real-time predictive analytics will supplement historical reporting. Alternative data sources (satellite imagery, web scraping, social media, IoT sensors) will be integrated into mainstream analytics products. ESG data and analytics will mature with standardized frameworks. The data-as-a-service model will expand as companies seek to monetize proprietary datasets. Expect continued consolidation as data companies acquire complementary datasets and capabilities.
Long-term, data and analytics companies are among the most structurally advantaged businesses. Data moats deepen over time as historical depth and breadth increase. AI models trained on proprietary data create compounding advantages. Regulatory requirements for data (credit reporting, financial compliance, ESG) create non-discretionary demand. The companies that combine unique data assets with AI-powered insights will command premium valuations indefinitely.
Data uniqueness and proprietary sources create competitive moats. Regulatory requirements for data products ensure non-discretionary demand. Customer retention rates (typically 90%+) provide revenue visibility. AI and machine learning capabilities determine product differentiation. Data privacy regulations (GDPR, CCPA) create compliance requirements. Cross-selling multiple data products increases ARPU.
AI transforms data analytics from descriptive to predictive and prescriptive. Machine learning models identify patterns across massive datasets that human analysts miss. NLP enables analysis of unstructured data (earnings calls, news, social media). AI-powered credit scoring incorporates alternative data for thin-file consumers. Generative AI creates automated research reports and market summaries. Real-time anomaly detection identifies fraud, market dislocations, and emerging trends.
Research and data analytics firms can deploy AI-powered data collection and cleaning automation to reduce the labor cost of data processing — often the largest operating expense. NLP and machine learning models enhance analytical capabilities and enable new product offerings. Automated report generation reduces analyst time on routine deliverables. Self-service analytics platforms allow clients to explore data independently, scaling usage without proportional headcount. Predictive models and real-time data feeds command premium pricing.
Verisk Analytics (VRSK) provides data analytics for insurance. S&P Global (SPGI) offers financial data, ratings, and analytics. MSCI (MSCI) provides indexes, ESG ratings, and portfolio analytics. Equifax (EFX) and TransUnion (TRU) are leading credit bureaus. FactSet (FDS) delivers financial data to investment professionals. Moody's (MCO) provides credit ratings and research. Gartner (IT) offers technology research and advisory.