Increased Costs and Resource Drain

Poor data quality results in increased operational costs, as teams spend significant time cleaning, validating, and reconciling data. This inefficiency diverts resources that could otherwise be invested in furthering transformation or refining AI models. Additionally, inconsistent data often leads to costly rework in AI projects, as the models must be retrained or tuned to handle unexpected discrepancies in data.

Unreliable Decision-Making

Transformation and AI are very dependent on accurate data for models, predictions, and insights that drive business decisions. Bad data creates a weak foundation for teaching those AI models, so they must then be trained multiple times – which is very expensive and wasteful.

When data is inconsistent, outdated, or incomplete, the results are unreliable, causing executives and managers to distrust AI-driven insights, halting adoption. For example, inaccurate customer data may lead to flawed personalization, hurting customer experiences and reducing ROI.

Time and Labor Costs for Data Cleaning and Reconciliation

Poor data quality forces teams to spend excessive time identifying, cleaning, and reconciling data errors. Research from data management firms like Informatica shows that data scientists and analysts can spend up to 80% of their time on data preparation, leaving only a fraction for actual analysis and model development. This inefficiency is especially damaging in transformation projects and AI initiatives where speed and agility are essential. For example, in financial reporting, discrepancies in data can often delay the month-end Close process, resulting in added overtime costs, staff turnover, and hampering strategic analysis that drives shareholder value.

Reduced Efficiency and Delayed Project Timelines

Bad data disrupts workflows across the organization. For AI initiatives, models trained on inaccurate or inconsistent data often need extensive retraining, revalidation, or tuning, leading to significant delays and massive cost overruns.

Each delay in an AI project or digital transformation timeline directly impacts the business’s competitive advantage. Additionally, bad data can lead to misguided actions, which might require manual intervention or corrective actions, further slowing down the process.

Technology Costs Due to Extra Data Storage and Processing

Handling and storing poor-quality data come with hidden technology costs. As data storage becomes increasingly complex, retaining vast amounts of duplicate or erroneous data leads to storage inefficiencies, increasing costs for cloud storage and data processing. Companies also incur additional costs when they need to scale up their infrastructure to handle poorly organized or bloated datasets, with some estimates suggesting that businesses waste 15-25% of their data storage resources on inaccurate data alone.

Potential Reputational Costs

When transformation and AI initiatives go wrong due to bad data, the costs extend beyond finances to reputation. For instance, if an AI-driven customer support bot pulls inaccurate data about a customer’s purchase history or issues a wrong recommendation, it can quickly lead to a loss of trust. This erosion of customer confidence can ultimately impact customer retention rates, brand reputation, and customer loyalty, further escalating the cost of bad data across the business.

Model and Process Degradation

AI models trained on bad data become biased or inaccurate, leading to suboptimal performance, degraded outputs, and lower trust in AI systems. Transformation projects that depend on automated processes also suffer, as bad data may trigger incorrect actions or workflows. Over time, these inaccuracies erode user confidence and make it difficult to scale AI and automation effectively.

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Sources:

McKinsey on AI and Data Quality

Harvard Business Review on Data Quality

Gartner on Data Quality’s Financial Impact.