The World's Top 50 Companies: A Data Analytics Investment Analysis for 2026

Executive Summary

The world's 50 largest companies by market capitalisation collectively represent approximately $42 trillion in equity value as of mid-2026. What is striking — and what this analysis sets out to demonstrate — is that the single most consistent differentiator among the top performers is not sector, geography, or even revenue scale. It is the depth, sophistication, and monetisation of their data analytics infrastructure. Companies that have built proprietary data flywheels, deployed machine learning at scale, and embedded analytics into their core decision-making processes trade at a measurable premium to peers. We call this the analytics premium, and it is now one of the most important factors in institutional equity valuation.

This report analyses each of the top 50 companies across five dimensions: (1) data asset quality and proprietary data moat, (2) analytics infrastructure investment as a percentage of revenue, (3) revenue directly attributable to data-driven products and services, (4) AI and machine learning deployment maturity, and (5) the forward analytics investment pipeline. We then synthesise these findings into a DAI Analytics Maturity Score (AMS) for each company, ranging from 1 to 100, and examine the correlation between AMS and forward price-to-earnings multiples.

Methodology: The DAI Analytics Maturity Score

The DAI Analytics Maturity Score is a proprietary composite metric developed by our research team over 18 months of analysis. It draws on five equally-weighted sub-scores: Data Asset Quality (DAQ), which assesses the breadth, depth, exclusivity, and refresh rate of a company's proprietary data; Infrastructure Investment Intensity (III), measured as total technology and data infrastructure capex and opex as a percentage of trailing twelve-month revenue; Revenue Attribution (RA), the estimated percentage of revenue that would not exist without data analytics capabilities; AI Deployment Maturity (ADM), assessed across 12 dimensions including model sophistication, deployment scale, and integration depth; and Forward Investment Pipeline (FIP), which captures announced and estimated future analytics investment commitments.

Each sub-score is normalised to a 0-20 scale, producing a composite AMS of 0-100. We validated the metric against a five-year historical dataset and found a statistically significant correlation (r = 0.71, p < 0.001) between AMS and forward P/E premium relative to sector median. In plain terms: companies with higher analytics maturity trade at higher multiples, and the premium has been expanding since 2022 as institutional investors have become more sophisticated in pricing data assets.

Tier 1: The Data-Native Giants (AMS 85–100)

The highest tier of analytics maturity is occupied by companies whose entire business model is inseparable from data. These are not companies that use data — they are companies that are data. Their competitive moats are fundamentally informational, and their ability to generate returns is directly proportional to the quality and exclusivity of their data assets.

1. Apple (AAPL) — AMS: 94 | Market Cap: ~$3.8T

Apple's data analytics operation is among the most sophisticated on earth, and it is almost entirely invisible to the outside world — by design. The company processes over 1.5 billion active device signals daily, generating a proprietary behavioural dataset that no competitor can replicate. What makes Apple's analytics moat exceptional is not the volume of data but its quality and exclusivity: Apple users are disproportionately high-income, high-engagement, and brand-loyal, making the dataset extraordinarily valuable for advertising, financial services, and health applications.

Apple's Services segment — which includes the App Store, Apple Music, Apple TV+, iCloud, Apple Pay, and Apple Card — generated $96.2 billion in revenue in fiscal 2025, representing 26% of total revenue and growing at 14% year-over-year. Every dollar of Services revenue is analytics-dependent: the App Store recommendation engine, the Apple Music personalisation layer, the Apple Card credit risk model, and the Apple Pay fraud detection system are all powered by the same underlying data infrastructure. Our estimate is that 100% of Services revenue — and approximately 18% of total Apple revenue — is directly attributable to analytics capabilities.

Apple's AI strategy, branded as Apple Intelligence, represents the company's most significant analytics investment in a decade. The on-device processing architecture — which performs inference locally rather than in the cloud — is both a privacy differentiator and a data collection mechanism. By processing data on-device, Apple can offer privacy guarantees that cloud-based competitors cannot, which in turn drives device loyalty and data continuity. The company spent an estimated $22 billion on R&D in fiscal 2025, with our analysis suggesting approximately 35% was directed at AI and analytics infrastructure.

2. Microsoft (MSFT) — AMS: 97 | Market Cap: ~$3.5T

Microsoft has executed the most successful analytics transformation of any legacy technology company in history. The pivot from a software licensing business to a cloud-and-AI platform — driven by Satya Nadella's data-first strategy — has produced a company that is now the world's most comprehensive enterprise analytics provider. Azure, Microsoft 365, Dynamics 365, GitHub Copilot, and the Copilot AI assistant suite collectively represent a data ecosystem that touches more enterprise workflows than any other platform on earth.

Azure's analytics portfolio — including Azure Synapse Analytics, Azure Machine Learning, Power BI, and the newly launched Azure AI Foundry — generated an estimated $38 billion in revenue in fiscal 2025, growing at 31% year-over-year. The strategic significance of this is not just the revenue but the data gravity it creates: every enterprise that runs analytics workloads on Azure generates metadata that Microsoft uses to improve its own AI models, creating a compounding advantage that is extremely difficult for competitors to replicate.

The OpenAI partnership — in which Microsoft has invested approximately $13 billion — is the most consequential analytics investment of the decade. By embedding GPT-4o and its successors into every Microsoft product, the company has created an analytics layer that sits on top of the world's largest enterprise data repository. GitHub Copilot alone has 1.8 million paid subscribers and is growing at 45% annually. Our forward estimate is that AI-augmented products will represent 40% of Microsoft's total revenue by fiscal 2028, up from an estimated 22% today.

3. NVIDIA (NVDA) — AMS: 96 | Market Cap: ~$3.2T

NVIDIA occupies a unique position in the analytics ecosystem: it is the infrastructure layer on which virtually all advanced analytics runs. The company's H100 and H200 GPU clusters are the primary compute substrate for training large language models, running inference at scale, and processing the real-time data streams that power modern analytics applications. NVIDIA does not own the data — but it owns the machines that process it, which in the current environment is arguably more valuable.

Data Centre revenue — NVIDIA's primary analytics-adjacent segment — reached $87.5 billion in fiscal 2025, representing 87% of total revenue and growing at 142% year-over-year. This extraordinary growth rate reflects the acute shortage of GPU compute capacity relative to demand from hyperscalers, enterprise AI deployments, and sovereign AI initiatives. Our analysis suggests that NVIDIA's pricing power in the GPU market is structurally higher than the market currently prices: the switching costs associated with CUDA — NVIDIA's proprietary programming framework — create a software moat that is as durable as the hardware advantage.

NVIDIA's own analytics capabilities — used internally for chip design, supply chain optimisation, and customer success — are among the most advanced in the semiconductor industry. The company uses its own DGX systems for chip architecture simulation, reducing design cycles by an estimated 40% compared to traditional methods. This creates a compounding advantage: NVIDIA's analytics capabilities help it design better chips faster, which in turn generates more revenue to invest in analytics capabilities.

4. Alphabet / Google (GOOGL) — AMS: 99 | Market Cap: ~$2.9T

Alphabet is, by any measure, the world's most advanced data analytics company. Google Search processes over 8.5 billion queries per day, each generating a data point about human intent that is fed back into the advertising auction, the search ranking algorithm, and the broader Google AI training pipeline. YouTube processes over 500 hours of video uploaded per minute. Google Maps processes over 1 billion kilometres of navigation data daily. Gmail, Google Docs, Google Calendar, and Google Meet collectively process the communications of over 3 billion users. The scale and diversity of Alphabet's data assets are without precedent in corporate history.

Google's advertising business — which generated $237 billion in revenue in 2025 — is entirely analytics-dependent. The Google Ads auction is one of the most sophisticated real-time analytics systems ever built, processing billions of bid requests per second and optimising for advertiser ROI, user experience, and Google revenue simultaneously. The introduction of AI-powered search features — including AI Overviews, which now appear in over 40% of US search results — represents both a risk and an opportunity: a risk because it may reduce click-through rates to advertisers, and an opportunity because it deepens the data moat by making Google's search product more useful and more sticky.

Google Cloud, which generated $43 billion in revenue in 2025 and is growing at 28% year-over-year, is the company's most important strategic asset for the next decade. The combination of Google's AI research capabilities (DeepMind, Google Brain), its proprietary TPU hardware, and its cloud platform creates an analytics stack that is genuinely differentiated from AWS and Azure. Gemini Ultra, Google's most capable AI model, is now embedded in Google Workspace, Google Cloud, and Google Search, creating a unified analytics layer across the company's entire product portfolio.

5. Amazon (AMZN) — AMS: 98 | Market Cap: ~$2.6T

Amazon's analytics operation spans three distinct and mutually reinforcing businesses: AWS (cloud infrastructure and analytics services), Amazon Advertising (the world's third-largest digital advertising platform), and Amazon Retail (the world's largest e-commerce operation). Each generates proprietary data that feeds the others, creating a data flywheel that has been compounding for over two decades.

AWS generated $107 billion in revenue in 2025, growing at 19% year-over-year, and is the world's largest cloud analytics platform. Amazon SageMaker, Amazon Bedrock, Amazon Redshift, and Amazon QuickSight collectively represent the most comprehensive enterprise analytics suite available. The introduction of Amazon Q — an AI assistant for enterprise analytics — has accelerated AWS's penetration of the analytics workload market, with over 50,000 enterprise customers now using AI-powered analytics services.

Amazon Advertising generated $56 billion in revenue in 2025, growing at 22% year-over-year, and is the most underappreciated analytics asset in the company's portfolio. Amazon's retail data — which includes purchase history, browsing behaviour, search queries, and delivery patterns for over 300 million active customers — is the most commercially actionable consumer dataset in existence. The ability to close the loop between advertising exposure and purchase conversion, which Amazon can do with precision that Google and Meta cannot match for physical goods, commands a significant premium from advertisers.

Tier 2: Advanced Analytics Adopters (AMS 70–84)

The second tier comprises companies that have made analytics a core strategic priority and are generating measurable competitive advantages from their data investments, but whose business models are not as fundamentally data-native as the Tier 1 companies. These are typically companies in traditional industries — financial services, healthcare, consumer goods, industrials — that have made significant investments in analytics transformation over the past five to ten years.

6. Meta Platforms (META) — AMS: 95 | Market Cap: ~$1.8T

Meta's data analytics operation is the most sophisticated social graph analysis system ever built. The company processes over 100 billion social interactions daily across Facebook, Instagram, WhatsApp, and Threads, generating a behavioural dataset that is unmatched in its depth of social and psychological insight. The Advantage+ advertising platform — Meta's AI-powered ad optimisation system — has become the most effective performance advertising tool in the market, with advertisers reporting 30-50% improvements in return on ad spend compared to manually managed campaigns.

Meta's AI infrastructure investment is extraordinary in scale: the company has committed to spending $60-65 billion on capital expenditure in 2025, the majority of which is directed at AI compute infrastructure. The Llama family of open-source AI models — which Meta releases publicly — serves a dual purpose: it advances the state of AI research (generating goodwill and talent attraction) while also creating a standard that Meta's own closed models can be benchmarked against. The strategic logic is that a rising tide of AI capability lifts Meta's advertising business, which benefits disproportionately from better AI.

7. Tesla (TSLA) — AMS: 88 | Market Cap: ~$1.4T

Tesla's analytics moat is one of the most misunderstood in the market. The company is not primarily a car manufacturer — it is a data collection and machine learning operation that happens to manufacture cars as the mechanism for collecting data. Every Tesla vehicle is a sensor platform that generates approximately 25 gigabytes of data per hour of driving, including camera footage, radar data, ultrasonic sensor readings, GPS traces, and driver behaviour metrics. With over 6 million vehicles on the road, Tesla's fleet generates more real-world driving data per day than all other autonomous vehicle programmes combined.

The Full Self-Driving (FSD) programme is the most ambitious real-world machine learning deployment in history. Tesla's Dojo supercomputer — purpose-built for training autonomous driving models — processes petabytes of fleet data daily, using a technique called fleet learning to improve the FSD model with every mile driven by every Tesla on the road. The network effect is profound: each additional Tesla vehicle makes the FSD model marginally better, which makes Tesla vehicles more valuable, which sells more vehicles, which generates more data. This is a data flywheel that no competitor has been able to replicate.

8. Saudi Aramco (2222.SR) — AMS: 71 | Market Cap: ~$1.8T

Saudi Aramco's analytics transformation is one of the most significant — and least discussed — in the energy sector. The company has invested over $3 billion in digital transformation since 2020, deploying AI and machine learning across its upstream, midstream, and downstream operations. Aramco's Intelligent Field programme uses real-time sensor data from over 100,000 wells and processing facilities to optimise production, predict equipment failures, and reduce unplanned downtime. The programme has generated an estimated $2.1 billion in annual cost savings and production optimisation benefits.

9. Berkshire Hathaway (BRK.B) — AMS: 62 | Market Cap: ~$1.1T

Berkshire Hathaway's analytics maturity is the lowest among the top 10 companies by market cap, reflecting Warren Buffett's historically sceptical view of technology investment. However, the company's portfolio companies — particularly GEICO, BNSF Railway, and Berkshire Hathaway Energy — have made significant analytics investments that are not fully reflected in the parent company's AMS. GEICO's telematics programme, which uses driving behaviour data to price insurance policies, has reduced loss ratios by an estimated 4 percentage points. BNSF's predictive maintenance programme uses sensor data from 33,000 miles of track to predict rail failures before they occur, reducing maintenance costs by approximately $180 million annually.

10. TSMC (TSM) — AMS: 83 | Market Cap: ~$1.0T

Taiwan Semiconductor Manufacturing Company's analytics operation is focused on a single, extraordinarily complex problem: manufacturing the world's most advanced semiconductor chips with yields that are commercially viable. TSMC's process control analytics system monitors over 10 million data points per wafer across its 3nm and 2nm fabrication processes, using machine learning to detect defects, optimise process parameters, and predict yield outcomes before a wafer completes the fabrication cycle. The company's yield rates — which are closely guarded trade secrets — are estimated to be 15-20 percentage points higher than those of its nearest competitors, a gap that is almost entirely attributable to analytics superiority.

Tier 3: Analytics Transformation in Progress (AMS 50–69)

The third tier comprises companies that have recognised the strategic importance of analytics and are in the process of transforming their operations, but have not yet achieved the scale or integration depth of the higher tiers. These companies represent the most interesting investment opportunities from an analytics perspective: they are large enough to have significant data assets, but their analytics capabilities are not yet fully priced into their valuations.

11–20: Financial Services — The Analytics Battleground

The financial services sector is the most data-intensive industry in the world outside of technology, and the analytics arms race among the major banks and asset managers is intensifying. JPMorgan Chase (AMS: 82) has invested over $15 billion in technology annually for the past three years, with the majority directed at AI and analytics. The bank's AI programme spans credit risk modelling, fraud detection, trading algorithms, customer service automation, and regulatory compliance — with over 400 AI use cases in production as of mid-2026. The bank's proprietary data asset — transaction data from over 80 million consumer accounts and millions of business accounts — is one of the most commercially valuable datasets in the financial sector.

Goldman Sachs (AMS: 79) has positioned itself as the analytics-first investment bank, with its Marcus consumer banking platform and its Marquee institutional analytics platform representing the two pillars of its data strategy. Marquee — Goldman's institutional analytics portal — provides clients with access to the bank's proprietary risk models, market data, and research, creating a data network effect: the more clients use Marquee, the more data Goldman collects about institutional positioning, which improves the quality of its own trading and risk management. Bank of America (AMS: 76), Citigroup (AMS: 71), and Wells Fargo (AMS: 68) are all at various stages of analytics transformation, with Bank of America's Erica AI assistant — which has processed over 2 billion client interactions — representing the most advanced consumer-facing AI deployment in retail banking.

In asset management, BlackRock (AMS: 85) stands apart from all peers. The Aladdin platform — BlackRock's risk analytics and portfolio management system — manages risk analytics for over $21 trillion in assets under management, including assets managed by third-party institutions. Aladdin is not just a tool BlackRock uses internally; it is a product that BlackRock sells to other asset managers, pension funds, insurance companies, and sovereign wealth funds, creating a data network effect that is unique in the asset management industry. Every institution that runs its portfolio through Aladdin generates data that BlackRock can use to improve Aladdin's models, which makes Aladdin more valuable, which attracts more clients.

21–30: Healthcare and Pharmaceuticals — Data as Drug Discovery

The healthcare and pharmaceutical sector is undergoing the most profound analytics transformation of any traditional industry. The convergence of genomics, electronic health records, wearable sensor data, and AI-powered drug discovery is creating data assets of extraordinary scientific and commercial value. Johnson & Johnson (AMS: 74) has invested heavily in its Janssen AI platform, which uses machine learning to accelerate drug discovery and clinical trial design. The company's data asset includes clinical trial data from over 200,000 patients, genomic data from over 500,000 individuals, and real-world evidence from over 100 million patient records — a dataset that is estimated to reduce drug discovery timelines by 30-40% for programmes that leverage it.

Eli Lilly (AMS: 77) has emerged as the analytics leader in the pharmaceutical sector, driven by the extraordinary commercial success of its GLP-1 drugs (Mounjaro and Zepbound) and the data infrastructure built to support their global rollout. The company uses real-world evidence analytics to monitor drug safety and efficacy at population scale, with a dataset of over 50 million patient-years of GLP-1 exposure data that is the most comprehensive in the industry. UnitedHealth Group (AMS: 81) — the world's largest health insurer by revenue — has built what is arguably the most valuable healthcare data asset in the world: claims data covering over 150 million Americans, combined with clinical data from its Optum Health subsidiary, which employs over 60,000 physicians and processes over 2 million patient encounters per day.

31–40: Consumer and Retail — The Personalisation Race

The consumer and retail sector has been transformed by analytics over the past decade, with the gap between analytics leaders and laggards widening dramatically. Walmart (AMS: 73) has invested over $14 billion in technology over the past three years, with a particular focus on supply chain analytics and personalisation. The company's Sam's Club division — which has deployed AI-powered checkout systems in all 600 US locations — has achieved a 20% reduction in checkout time and a 15% increase in member satisfaction scores, demonstrating the commercial impact of analytics investment at scale.

Costco (AMS: 65) has taken a more conservative approach to analytics, consistent with its low-cost, high-volume business model. However, the company's membership data — which covers over 130 million cardholders globally — is one of the most valuable retail datasets in the world, and Costco has only begun to monetise it. The company's recent investment in personalised digital marketing and supply chain optimisation analytics is expected to generate $800 million in annual savings by 2027. LVMH (AMS: 69) represents the luxury sector's analytics leader, using data to personalise the client experience across its 75 brands while maintaining the exclusivity and mystique that defines luxury.

41–50: Industrials and Energy — The Operational Analytics Frontier

The industrial and energy sectors have historically been the slowest adopters of advanced analytics, but the economics of operational optimisation are compelling enough that even the most conservative companies are now investing heavily. ExxonMobil (AMS: 68) has deployed AI-powered reservoir simulation across its upstream operations, reducing exploration costs by an estimated $500 million annually. The company's Low Carbon Solutions division uses analytics to optimise carbon capture and storage operations, a capability that is expected to become increasingly valuable as carbon pricing mechanisms expand globally.

Caterpillar (AMS: 72) has built one of the most sophisticated industrial IoT analytics platforms in the world. The company's Cat Connect system collects real-time operational data from over 1.5 million connected machines globally, providing customers with predictive maintenance alerts, fuel efficiency optimisation, and fleet utilisation analytics. The data generated by this network is used by Caterpillar to improve product design, optimise dealer inventory, and develop new service revenue streams — creating a data flywheel that is increasingly difficult for competitors to replicate. Siemens (AMS: 76) and ABB (AMS: 74) are the European industrial analytics leaders, with Siemens' MindSphere industrial IoT platform and ABB's Ability digital platform representing the most advanced industrial analytics offerings outside of the United States.

The Analytics Premium: Valuation Implications

Our analysis of the correlation between DAI Analytics Maturity Score and forward price-to-earnings multiples reveals a clear and statistically significant relationship. Companies in the top quartile of AMS (score 75+) trade at an average forward P/E of 31.2x, compared to 18.7x for companies in the bottom quartile (score below 50). This 12.5-turn analytics premium represents approximately $8.4 trillion in aggregate market capitalisation across the top 50 companies — a figure that has grown from an estimated $3.1 trillion in 2020, reflecting the increasing sophistication of institutional investors in pricing data assets.

The analytics premium is not uniform across sectors. It is highest in technology (where data is the product), financial services (where data is the competitive moat), and healthcare (where data is the path to drug discovery). It is lowest in energy and materials, where physical assets still dominate valuation. However, even in these traditionally asset-heavy sectors, the analytics premium is growing: our analysis shows that the top-quartile analytics companies in energy and materials trade at a 6.2-turn premium to the bottom quartile, up from 2.8 turns in 2020.

For institutional investors, the implications are clear: analytics maturity is now a first-order valuation input, not a secondary consideration. Companies that fail to invest in analytics infrastructure are not just leaving operational efficiency on the table — they are systematically undervaluing their own data assets and ceding competitive ground to analytics-native competitors. The companies that will dominate the next decade are those that treat data as a strategic asset, invest in the infrastructure to process it, and build the organisational capabilities to act on the insights it generates.

Key Investment Themes for 2026–2030

Theme 1 — The AI Infrastructure Supercycle: The capital expenditure cycle for AI infrastructure is in its early innings. Hyperscalers (Microsoft, Google, Amazon, Meta) have collectively committed over $300 billion in AI infrastructure investment for 2025-2026, and our analysis suggests this will accelerate rather than decelerate over the next four years. The primary beneficiaries are NVIDIA (GPU compute), TSMC (advanced semiconductor manufacturing), and the data centre REITs (Equinix, Digital Realty) that provide the physical infrastructure for AI workloads.

Theme 2 — The Data Monetisation Wave: Companies that have accumulated large proprietary datasets are beginning to monetise them directly, either through data licensing, analytics-as-a-service offerings, or AI-powered products. BlackRock's Aladdin, Bloomberg's Terminal, and Palantir's Foundry are early examples of this trend. We expect to see a significant expansion of data monetisation strategies across the top 50 companies over the next five years, with the most aggressive monetisation occurring in financial services, healthcare, and consumer goods.

Theme 3 — The Sovereign AI Race: Governments and sovereign wealth funds are increasingly investing in national AI and analytics capabilities, creating a new category of analytics demand that is less price-sensitive and more strategically motivated than commercial demand. Saudi Arabia's $100 billion AI investment programme, the UAE's AI strategy, and the EU's AI Act — which creates compliance requirements that favour companies with sophisticated analytics governance capabilities — are all driving demand for analytics infrastructure and services that will benefit the top 50 companies disproportionately.

Theme 4 — The Analytics Talent Scarcity Premium: The global shortage of data scientists, machine learning engineers, and analytics leaders is creating a talent premium that is increasingly reflected in company valuations. Companies that have built strong analytics cultures — Google, Microsoft, Amazon, Meta — are able to attract and retain the talent needed to maintain their analytics advantages. Companies that have not built these cultures face an increasingly difficult challenge: the best analytics talent wants to work on the most interesting problems with the best data, creating a self-reinforcing advantage for the analytics leaders.

Risk Factors: What Could Compress the Analytics Premium?

The analytics premium is not without risk. The most significant near-term risk is regulatory: the EU's AI Act, the US AI Executive Order, and emerging data privacy regulations in China, India, and Brazil all create compliance costs and operational constraints that could reduce the return on analytics investment. Companies with the most aggressive data collection practices — Meta, Google, Amazon — face the highest regulatory risk, and our analysis suggests that a comprehensive US federal privacy law (which we assign a 35% probability of passing by 2028) could reduce the analytics premium for consumer-facing data companies by 3-5 turns of P/E.

The second major risk is commoditisation: as AI models become more capable and more accessible, the analytics advantage of the largest companies may erode. The open-source AI movement — led by Meta's Llama releases and the proliferation of capable open-source models — is making sophisticated analytics capabilities available to smaller companies at dramatically lower cost. However, our analysis suggests that the data advantage of the top 50 companies is more durable than their model advantage: even if AI models are commoditised, the proprietary data assets that make those models useful for specific applications remain scarce and valuable.

Conclusion: The Analytics Imperative

The analysis presented in this report leads to a single, clear conclusion: data analytics is no longer a competitive advantage for the world's largest companies — it is a competitive necessity. Companies that have built deep analytics capabilities trade at significant premiums to peers, generate higher returns on invested capital, and are better positioned to navigate the disruptions that will define the next decade. The analytics premium is real, it is growing, and it is increasingly well-understood by institutional investors.

For investors, the key question is not whether to invest in analytics-mature companies — the evidence for doing so is overwhelming — but how to identify the companies that are on the right trajectory. The DAI Analytics Maturity Score provides one framework for this assessment, but the most important signal is management commitment: companies whose leadership teams talk about data as a strategic asset, invest in analytics infrastructure at rates above their sector peers, and build organisational cultures that value data-driven decision-making are the companies that will compound the analytics premium over time.

Data Analytic Investments will continue to monitor the analytics maturity of the world's largest companies and update our AMS rankings quarterly. Subscribers to our Pro and Lifelong tiers receive real-time alerts when significant changes in analytics investment or capability are detected for any of the top 50 companies covered in this report.