3 actions top executives and board leaders can take to help safeguard data credibility

The hidden risks behind the data sets

Traditional anchors of corporate judgment, such as government statistics, audited financials, and regulatory disclosures, remain essential, but they can be incomplete, overly opaque, or released too slowly to effectively support and match the rapid pace of decision-making in today’s marketplace. Recent government shutdown disruptions and the suspension of certain federal data collection programs also highlight a growing structural risk. Critical public data sets that underpin economic forecasting and market benchmarks may be delayed, impaired, or temporarily unavailable, potentially introducing blind spots into models that assume continuity and reliability of official data sources.⁴

Even when available, these indicators can shift significantly after their initial release, as subsequent revisions incorporate more complete information. This underscores the challenge decision-makers face when relying on preliminary economic data that may later present a different picture of underlying conditions.⁵ Leveraging preliminary data involves a fundamental trade-off: gaining timeliness at the cost of reduced vetting and a greater potential for revision. Balancing this trade-off can be particularly challenging given the increasing speed at which executives are expected to make decisions today.

In part to address this challenge and drive competitive advantage, leaders often rely on alternative data sources. These include commercial data feeds, sentiment indicators, geolocation data, and algorithmically assembled data sets that promise real-time economic insight.⁶

At the same time, trust in information itself is eroding. Institutional credibility has weakened, and expertise is more frequently contested.⁷ The historical hierarchy of “official” versus “unofficial” data is blurring, often leaving leaders to decipher credibility in real time and potentially under material financial and reputational pressure. Deloitte Chief Global Economist Ira Kalish says: “We are living through an era of compounding shocks. Energy disruptions, labor market instability, and financial market volatility are hitting simultaneously, and each one can affect the data signals leaders rely on. When conditions change this fast, the question is not just whether you have the right models, but whether the data feeding those models still reflects the world as it actually is.”

AI has brought data credibility to the fore, as it is increasingly used in decision-making. In Deloitte’s 2026 Global Human Capital Trends survey, 60% of executives surveyed said they now regularly use AI to support their decisions.⁸ Generative AI systems can now produce outputs that appear authentic but may be synthetic, biased, hallucinated, or otherwise ungrounded.⁹ If the data upon which organizations base their AI models is flawed from the start, these risks only multiply.

This challenge is not only technological but also organizational. Nearly two-thirds (61%) of respondents to the 2026 Global Human Capital Trends survey recognize the growing importance of addressing the decline in the quality and trustworthiness of work and workforce data, but only 5% report taking meaningful steps to address it.¹⁰

Compounding this, organizations face a growing risk of overreliance on AI-generated analysis itself, where the speed, confidence, and seeming plausibility of AI outputs may discourage scrutiny. As decision-making becomes increasingly data-driven and AI-driven, many organizations may be accelerating their analytical capabilities faster than they are strengthening the credibility of the data that informs them and the judgment to critically examine what AI produces from it. For finance leaders, for example, who are charged with capital allocation, liquidity management, forecasting integrity, and enterprise risk oversight, this is not merely a technical concern. Data credibility can become a finance risk.

Steps leaders can take

Leaders can consider the following steps to drive confidence in the credibility of their data as they advance scenario planning, agile decision-making, and strategic enterprise initiatives.

1. Prioritize data provenance and transparency

In an environment of information abundance, not all data deserves equal weight. Traditional data sets such as audited financials, government statistics, and standardized surveys have established methodologies and longitudinal integrity. However, alternative data sources, while often timelier, may not be as reliable or adhere to the same standards.¹¹

For leaders, the question is not so much about whether to adopt alternative data. It is about the risks associated with adopting it without understanding how it is created, filtered, and maintained. Recent cases across multiple industries show how demand forecasts, credit assessments, and pricing models can be distorted by manipulated social signals, automated bot activity, or flawed data collection methods.¹²

Leaders should require full transparency across the data life cycle. They need to have clear visibility into how data is sourced, transformed, and used, while holding third-party providers accountable for demonstrable lineage, auditability, and reproducibility.¹³ In other words: Where has this data been? What was done to it? Can we rerun the same data process and get a similar output? This last question reflects a foundational principle of the scientific method: A finding is not considered trustworthy until it can be independently replicated. Organizational leaders can establish formal governance controls, such as making it mandatory that data providers include all available metadata, such as timestamps, sampling methods, and geographic coverage.

2. Cross-validate signals in forecasting

No single data set captures reality perfectly. Data reflects collection biases, coverage gaps, and methodological constraints. Further, in some cases, such as GPS-derived or other location data, accuracy can be affected by the reliability of measurement tools. Signal drift, temporary interruptions, atmospheric conditions, or other environmental factors can disrupt measurement.

For these reasons, organizations and data experts often triangulate the data they have access to, cross-validating among independent sources to protect forecast or analytical integrity. Having multiple independent indicators point to the same trend can improve confidence that the signals collected reflect reality rather than noise. To strengthen that signal base, leaders can also:

• Continuously identify new data sets from a mix of public, private, and sector-specific sources, prioritizing those that offer distinct coverage, timeliness, or explanatory value
• Institutionalize a portfolio-based approach to data by systematically evaluating and weighting sources based on historical performance, independence, reliability, and methodological transparency
• Stress-test new data sets against historical benchmarks and established indicators before integrating them into planning models¹⁴
• Invest in building proprietary data assets such as internal transaction records, operational data, and customer interaction data that the organization controls directly

3. Formalize data stewardship models and infuse data governance into risk management oversight

C-suite leaders can drive formal data stewardship models by establishing clear governance structures, including defined roles, ownership, escalation paths, and accountability for data quality and risk across the enterprise. They can also use scenario planning to explicitly define when data is “fit for purpose” versus when uncertainty exceeds acceptable risk appetite thresholds.¹⁵

Meanwhile, as boards deepen their oversight of strategy, technology, and risk management, they may already be discussing data management and privacy through the lens of AI governance.¹⁶ But since poor data quality can cascade into mispriced or misplaced investments, liquidity misjudgments, regulatory exposure, and reputational harm, upholding data credibility likely warrants its own discrete line item on boards’ risk management agendas. What makes this particularly acute is that data quality failures may not be visible at the point of consequence. By the time flawed data has propagated through planning models, financial forecasts, and automated decision systems, the downstream effects are often already in motion and difficult or impossible to unwind. Boards can also ensure they’re providing robust oversight by upskilling, ensuring they’re data-fluent enough to probe assumptions and methodologies and set a data-aligned risk appetite.

Moving from data abundance to credible insight

In a world where information is everywhere, judgment may be the most valuable resource. Deloitte’s research confirms that leaders are stepping into this reality, especially using scenario planning and agile governance to lead through uncertainty. The next frontier will likely be ensuring those capabilities are built on data that deserves trust.

We believe the organizations most likely to succeed in leveraging data to make smarter decisions won’t be those with the most data or the most advanced models. They will be the ones whose leaders and teams rigorously evaluate, validate, and govern information consistently over time—from source to scenario to strategic outcome.