Most organizations recognize that data plays a critical role in decision-making, transformation, and technology investment. What is less well understood is the cost of operating without data that is truly business-ready.
It's not to say that leaders believe poor data is harmless. Leaders generally agree that better data leads to better outcomes. Rather, it is that the financial impact of poor data rarely appears as a clean, isolated line item on a balance sheet. There’s no invoice labeled “inconsistent master data,” no dashboard called “misaligned definitions” or “fragmented governance”. Instead, the cost manifests indirectly—distributed across departments, embedded in rework and slower decisions, absorbed into operational friction.
Because these costs are diffuse rather than concentrated, they are consistently underestimated.
In many organizations, data is considered acceptable as long as systems function and reports can be produced. If teams can reconcile discrepancies manually or apply judgment before acting, the data is deemed usable. It becomes second nature to not even bat an eye when we notice a reporting inconsistency here, a reconciliation delay there, or a manual process that is “just how we’ve always done it.”
Each issue feels small in isolation. But collectively, they compound.
This assumption holds only in environments where humans remain the primary interpreters of information. It begins to break down as organizations scale, automate, and rely more heavily on analytics and AI.
What begins as minor friction becomes structural drag:
Data that is technically accurate but lacking business context, governance, and consistency is not business-ready. It may support reporting, but it cannot reliably support execution. Over time, organizations adapt to this gap. Manual controls emerge. Workarounds become routine. The absence of business-ready data becomes normalized rather than addressed.
The limitations of this operationally unreliable data often remain hidden until scale exposes them. And artificial intelligence is the stress test.
AI doesn’t politely compensate for inconsistent definitions or incomplete records. Unlike humans, AI does not infer intent, question anomalies, or apply institutional knowledge unless it is explicitly encoded. It operates on the definitions, relationships, and rules embedded in the data it receives.
When organizations observe that AI outputs fail to align with business reality, they are often encountering the consequences of operating without business-ready data. The issue is not the sophistication of the model, but the readiness of the data foundation.
Poor data that was once tolerable in manual workflows becomes expensive and downright risky in automated, global environments, in way of:
The organization does not simply lose efficiency—it loses momentum.
The economic impact of operating without business-ready data is rarely labeled as such. Instead, it surfaces in predictable but often disconnected ways:
Teams repeatedly intervene to correct, validate, or reinterpret data because rules and ownership were never formalized.
ERP and S/4HANA programs that go-live (often, to meet a deadline) but require prolonged remediation because business context was not fully embedded in the data.
Leaders delay action while waiting for reconciled, trusted information.
Incomplete lineage, inconsistent definitions, and unclear controls increase risk during audits and regulatory reviews.
AI initiatives that fail to scale because the data cannot reliably support autonomous or high-confidence execution.
None of these line items appear under “data quality.” And while individually these costs may appear manageable, over time they compound into material financial impact.
The core issue that we see on repeat is that many organizations still treat data readiness as a technical concern rather than a business capability. As long as systems run and reports generate, the underlying integrity of the data rarely receives board-level attention.
In an environment shaped by AI, digital transformation, regulatory scrutiny, and ongoing change, this level of readiness is no longer optional. Data functions as enterprise infrastructure. When that infrastructure is incomplete, the organization absorbs the risk and cost at scale.
Organizations that are reducing the cost of data-related friction are not doing so by fixing issues as they arise. They’re building readiness intentionally.
The organizations that avoid escalating cost tend to share common traits:
They aren't waiting for failures to force investment. Instead, they ensure their data is ready for reuse, automation, and scale. The result is not just improved data quality. It is faster execution, more resilient operations, and a foundation that allows AI and transformation initiatives to deliver sustained value.
The question is no longer: “Do we have data quality issues?” It’s clear that every enterprise does. What we should be asking ourselves is: “What is the economic impact of the issues we’ve normalized?”
Because once organizations quantify the cost—across rework, delays, risk, and lost AI value—the conversation changes. Data quality and data readiness stops being an IT improvement initiative and becomes a financial and strategic priority.
That shift in perspective is where real transformation begins. Long before go-live. Long before system adoption. Long before AI agents and assistants. Transformation starts with disciplined data foundations that reflect how the business truly operates. It’s a cultural shift — one where “Data First” isn’t a catchphrase, but a shared standard. A measurable commitment woven into how the organization defines ownership, strengthens governance, and equips leadership with the clarity and confidence to act on trusted data.
When that shift takes hold, the conversation moves beyond, “How much is poor data costing us?” and becomes, “How much value can business-ready data create?”
Done right, the answer isn’t incremental. It’s exponential.