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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

Continuous Auditing Transforms Corporate ERPs

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continuous auditing transforms corporate erps

As corporate accounting departments cross the threshold into late July 2026, the adoption of continuous, automated auditing systems has reached a definitive turning point. Driven by advances in artificial intelligence and deep integration with modern Enterprise Resource Planning (ERP) platforms, leading finance organizations are moving away from traditional, periodic post-hoc audits in favor of real-time, 100% transactional verification. This technological transition is redefining internal control environments, reducing compliance costs, and eliminating the structural delays inherent in legacy quarterly closing processes.

Unlike traditional auditing frameworks that rely on statistical sampling—a process that inevitably leaves operational blind spots—continuous auditing software monitors operational data feeds continuously. Every purchase order, electronic invoice, payroll disbursement, and cross-border wire transfer is automatically cross-referenced against established corporate governance parameters, regulatory tax schedules, and anti-fraud algorithms in real time. Anomalies or unauthorized ledger entries are flagged instantly, allowing internal audit teams to investigate and remediate compliance gaps immediately rather than months after the close of a financial period.

The implications for executive financial management are far-reaching. By embedding continuous verification directly into daily transaction workflows, chief financial officers gain uninterrupted visibility into the organization’s true financial standing. Real-time balance sheet auditing eliminates the severe operational bottlenecks associated with month-end and quarter-end financial reconciliations, freeing accounting professionals to focus on strategic financial modeling, tax planning, and capital allocation rather than manual data entry and spreadsheet consolidation.

However, implementing continuous auditing requires accounting leadership to invest heavily in data governance and technical upskilling. Internal audit teams must evolve from manual ledger reviewers into system architects capable of auditing complex algorithms and validating automated data pipelines. Accounting firms and corporate controllers that master continuous auditing will establish a resilient compliance framework capable of meeting stringent international regulatory standards with total transparency.

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Accounting

U.S. Imposes New 50% Tariffs on Canadian Imports Under Rare Legal Provision

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U.S. Imposes New 50% Tariffs on Canadian Imports Under Rare Legal Provision

WASHINGTON — In a major escalation of cross-border trade friction, U.S. President Donald Trump has signed executive orders imposing new 50% tariffs on a wide selection of Canadian exports, citing discriminatory practices by Ottawa targeting American auto, dairy, and beverage industries.

The new duties, announced Monday, will take effect in 30 days. They target a broad spectrum of consumer and industrial goods—ranging from wine, liquor, and milk products to commercial cement, furniture, clothing, and hockey equipment.

Untested Legal Mechanism

To enact the sweeping measures, the administration invoked Section 338 of the Tariff Act of 1930—a rarely used legal provision allowing the executive branch to levy additional tariffs of up to 50% on foreign nations deemed to discriminate against U.S. commerce.

White House officials noted that Section 338 addresses trade discrimination rather than national security or economic emergencies. The move comes months after prior global emergency tariffs faced legal challenges in domestic courts, signaling Washington’s pivot toward alternate statutory authorities to maintain import duties.

Senior administration officials briefed reporters that the measure directly responds to Canadian provincial bans on U.S. alcohol, restrictions on American vehicle exports, and import quota disparities affecting U.S. dairy and cheese producers relative to third-party trading partners.

“While the administration continues to secure reciprocal trade agreements globally, Canada retaliated against efforts to protect domestic industry,” U.S. Trade Representative Jamieson Greer stated.

USMCA Impact and Carve-Outs

Significantly, the newly ordered 50% duties will apply to designated items even if they otherwise comply with the United States-Mexico-Canada Agreement (USMCA).

However, the administration confirmed key targeted exemptions:

  • Energy products (including oil and natural gas)
  • Potash and critical minerals
  • Fish and seafood
  • Goods already governed by sector-specific duties (such as existing steel and aluminum tariffs)

Administration representatives emphasized that the tariffs do not stem from recent disputes concerning drifting Canadian wildfire smoke, noting that policy options regarding environmental spillover remain under separate review.

Canadian Response and Market Reaction

Following the White House announcement, the Canadian dollar experienced a sharp decline against the U.S. dollar, falling approximately 0.4% during evening trading.

Canadian Prime Minister Mark Carney issued a statement emphasizing that Canada’s earlier counter-duties had merely matched previous U.S. trade actions. “Canada stands ready to engage intensively to address outstanding issues with the U.S. to the mutual benefit of our citizens,” Carney stated, pointing to detailed proposals Ottawa submitted to modernize the USMCA framework.

Ontario Premier Doug Ford took a firmer stance, urging a “dollar-for-dollar” reciprocal response if the measures go into effect on August 19.

With a 30-day implementation window before the duties officially lock in, industry associations and trade groups on both sides of the border are calling for urgent bilateral negotiations to avert further supply chain disruption across North America.

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Accounting

Automated Continuous Auditing: Transforming Compliance and Real-Time Financial Oversight

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Transforming Compliance and Real-Time Financial Oversight

The traditional accounting paradigm—defined by periodic monthly closures and post-hoc annual audits—is rapidly giving way to continuous, automated financial oversight. As of July 2026, forward-thinking accounting practices and multinational corporate finance departments are leveraging continuous auditing systems powered by advanced machine learning models. These systems monitor operational transactions in real time, shifting audit methodologies from sample-based post-analysis to absolute, 100% transaction-level verification.

The operational advantages of continuous auditing are transformative. Standard auditing procedures historically relied on statistical sampling, which, despite rigorous methodology, inherently left gaps where anomalies or fraudulent transactions could go undetected for months. Modern continuous auditing platforms integrate directly with enterprise resource planning (ERP) databases, instantly cross-referencing purchase orders, invoices, bank feeds, and tax records. Any deviation from established control parameters or unusual transaction behavior triggers immediate flags for internal audit teams, dramatically reducing detection lag from quarters to seconds.

Beyond fraud prevention, continuous auditing fundamentally alters internal reporting and decision-making. Executive leadership no longer has to wait weeks after the close of a quarter to evaluate precise financial standing; real-time verified ledger data provides an uninterrupted view of operating margins, tax liabilities, and cash flow dynamics. This real-time visibility enables corporate controllers to adjust capital allocation strategies dynamically, mitigating liquidity constraints and capitalizing on emerging commercial opportunities far more efficiently than competitors bound to legacy reporting cycles.

However, implementing continuous auditing requires accounting professionals to acquire new analytical capabilities. The role of the auditor is evolving from manual data reconciliation toward system validation, algorithmic model governance, and strategic risk interpretation. Accounting firms and corporate finance departments must invest in continuous technical education, ensuring that audit staff possess the data engineering skills necessary to design, maintain, and evaluate complex automated compliance systems.

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