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How to value data: the fuel powering the AI revolution

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Data has become one of the most valuable assets a company can own. Much like oil powered the industrial era, data now drives innovation, competitiveness and profitability, especially in the world of artificial intelligence.

Machine learning models depend on vast quantities of high-quality data to identify patterns and make accurate predictions. Without data, AI systems would lack the foundation needed to learn, adapt and generate meaningful insights.

For businesses monetizing and leveraging their proprietary data, what methodologies can be employed to value these assets?

Examples of proprietary data monetization

In addition to using data internally to gain a competitive advantage or boosting efficiency and improving decision making, we are seeing increasing examples of companies with high-quality, specialized datasets capitalizing by selling or licensing these assets to third parties.

Health care

One area where data is transforming the industry’s landscape is in diagnostic imaging, particularly in the interpretation and utilization of medical images such as X-rays, MRIs and CT scans.  A key benefit of AI in this field is its ability to accelerate the diagnostic process. Traditional methods of image interpretation can be time-consuming and subject to human error. AI, however, can process and analyze images quickly, significantly reducing the time and costs for diagnosis. AI capabilities can also enhance the accuracy of diagnoses. By learning from large datasets of medical images, AI algorithms can identify patterns that might be overlooked by human readings.

The cost to access imaging data can be substantial. Stanford University’s Center for Artificial Intelligence in Medical Imaging curated an imaging data repository featuring a total of 223,462 unique pairs of radiology reports and chest X-rays across 187,711 studies from 64,725 patients. The school licenses the data for an annual fee of $70,000 per dataset. 

Automotive

Connected vehicles collect a wide range of data, including location, driving habits, vehicle health, car-usage, and even personal information from the driver’s connected devices, such as a smart phone through Apple CarPlay. This data can be used for various purposes, including advertising, insurance rates and even employment verification.  As more connected vehicles hit the road, the volume of data being generated is rapidly growing. Research by S&P Global found that connected vehicles can generate nearly 25 GB of data per hour from over 100 different data points.  

BMW’s CarData platform provides access to the telematics data of BMW and Mini vehicles. The bundled vehicle information types are called “keys” and are arranged in categories such as usage data, event data, vehicle, basic data and metadata.  BMW charges €0.09 per individual event key and €0.29 per individual data key with a maximum monthly cap of €5.00 per car.

Media

Reddit started charging for access to its data and content via its application programming interfaces in 2023, asking developers to pay $12,000 per every 50 million requests. API is the method third-parties access data from a platform to connect to the third-party’s apps, conduct research, or data analysis.  Reddit produces a massive amount of user-generated content from a diverse “community of communities,” with a total of 5.3 billion pieces of content created by their users in the first half of 2024. Both Google and OpenAI use Reddit data to train their large language models, which underpin Google’s Gemini and OpenAI’s ChatGPT.  

How to value proprietary data

As data increasingly becomes a critical asset for AI-driven companies, accurately determining its monetary value becomes essential. There are several primary valuation methodologies, each suited for different scenarios. 

Market approach

Market-based valuation involves assessing data value based on comparable market transactions or licensing agreements, adjusting prices for differences such as data quality, exclusivity, or volume. While straightforward and market-driven, it can sometimes be challenging to find truly comparable datasets, limiting its accuracy for highly specialized or proprietary data.

Cost approach

Cost-based valuation calculates data’s value based on expenses incurred in obtaining, preparing, maintaining and storing it, including both direct costs (acquisition, infrastructure) and indirect costs (labor, compliance). This method is quantifiable and practical for newly created datasets but may undervalue datasets where the strategic or market worth significantly exceeds production costs.

Income approach

In this approach, data is quantified based on expected future economic benefits derived from its usage, typically through revenue growth, cost reduction or operational improvements. It closely ties the data’s valuation to tangible business outcomes, although forecasting future benefits can introduce uncertainty and sensitivity to assumptions.

Advanced data-based valuations

Advanced statistical or data science-based valuation combines analytics and machine learning to create nuanced valuation models tailored specifically to data characteristics. This approach uses techniques like feature extraction, sensitivity analysis and predictive modeling to identify the relative importance of data attributes, such as freshness, frequency of access and delivery mode (real-time vs. batch). 

While sophisticated and highly tailored, it requires advanced expertise and robust historical data to ensure accuracy. In practice, organizations frequently use hybrid methods, combining elements from multiple methodologies to capture data’s full strategic, economic and operational value.

Implications for the accounting profession

As data becomes a recognized intangible asset, CPAs are uniquely positioned to lead in its financial interpretation, governance and assurance. In financial reporting, proprietary datasets must be accurately valued for purchase price allocations under ASC 805. During M&A due diligence, accounting professionals assess whether data-driven business models are sustainable, particularly where data is a core revenue generator. Cross-border data use also raises complex transfer pricing questions, requiring accountants to evaluate intercompany pricing models in line with OECD and IRS guidance. 

For auditors, the rise of data-centric business practices adds new dimensions to SOX compliance and internal controls testing, where the integrity and monetization of data must be considered. CPAs in advisory roles are increasingly engaged to quantify the financial impact of enterprise data, such as evaluating the return on investment in CRM platforms or data licensing agreements. 

As data powers AI and digital transformation, the accounting profession will continue to play a critical role in ensuring these assets are reliably valued and properly disclosed.

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Accounting

SEC’s Semiannual Reporting Proposal Faces Investor Pushback: What CFOs Need to Know

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U.S. Securities and Exchange Commission (SEC)

A proposal from the U.S. Securities and Exchange Commission to potentially shift some public companies away from quarterly financial reporting toward a semiannual model is drawing significant pushback from investors, even as it continues moving through the regulatory process. The debate has direct implications for corporate finance teams, auditors, and the broader transparency of U.S. capital markets.

What the SEC Proposed

According to a summary published by accounting advisory firm Cohen & Co., the SEC issued a proposed rule on May 19, 2026, aimed at simplifying financial reporting requirements for many U.S. public companies. The proposal would potentially reduce the frequency of certain mandatory disclosures from quarterly to semiannual, a structural change that has not been made to core U.S. reporting requirements in decades.

The proposal follows an extended debate within U.S. policy circles, with proponents arguing that reduced reporting frequency could lower compliance costs and free up management time for longer-term strategic planning rather than quarter-to-quarter results management.

Why Investors Are Pushing Back

Comment letters submitted in response to the proposal have been extensive, and according to Cohen & Co.’s review of the public record, investors “appear to be largely opposed” to the shift, viewing frequent interim reporting as a core benefit of U.S. capital markets relative to other jurisdictions.

Accounting and law firms have taken a more measured position, generally urging any changes to remain aligned with the Financial Accounting Standards Board (FASB), whose existing disclosure requirements and guidance are built around a quarterly reporting cadence. A shift to semiannual reporting without corresponding changes to FASB guidance could create friction between SEC filing requirements and GAAP-based disclosure expectations.

Lessons From the U.K. Experience

The debate is not without precedent. The United Kingdom moved away from mandatory quarterly reporting for listed companies in 2014, returning to a semiannual disclosure requirement. According to Cohen & Co.’s analysis, that experience offers a cautionary data point: there was no measurable increase in capital expenditure or R&D investment following the change, while analyst coverage of affected companies declined as reliable interim information became less available — a particular risk for smaller and newly public companies that rely on analyst coverage to maintain investor visibility.

Practical Implications for Finance Teams

Beyond the debate over disclosure philosophy, the proposal carries practical complications. Many companies have debt covenants and credit agreements structured around quarterly financial delivery; a shift to semiannual reporting could require renegotiating those terms. Reduced reporting frequency would also extend the “window of market silence” between disclosures, a factor that governance and investor-relations teams would need to manage carefully to avoid information asymmetry.

Separately, and unrelated to the reporting-frequency debate, the SEC and FASB have continued finalizing more routine updates this year. New Accounting Standards Updates are taking effect for December 31, 2026, fiscal year-ends covering income tax disclosures, credit loss measurement, induced debt conversions, and stock compensation, according to Eide Bailly’s review of 2026 ASU activity. Additional guidance on paid-in-kind dividends and environmental credits is also on the near-term horizon.

What to Watch Next

The semiannual reporting proposal remains in the comment and review phase, and no final rule has been adopted as of this writing. Finance leaders should monitor the SEC’s regulatory agenda for further movement, while treating the current quarterly reporting requirement as the operative standard until any final rule is issued and an effective date is set.

Given the extent of investor opposition documented in the comment file, a full shift to mandatory semiannual reporting appears more likely to result in either a scaled-back compromise or continued study rather than swift adoption — though the SEC’s ultimate direction remains uncertain.

 

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

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