What Is Bad Data? How to Identify, Quantify, and Address Its Risks Effectively

Bad data weakens the foundation of modern business. According to Monte Carlo’s 2025 State of Data Quality Survey, it drains an average of 31% of company revenue. These losses surface in concrete ways: corrupted machine learning models that misfire, financial data that skews reporting, and supply chain records that mask critical gaps.

The challenge grows as organizations expand their reliance on analytics and AI. Gartner emphasizes that AI-ready data requires constant alignment and governance—not just greater data volumes. When flawed inputs shape advanced models, outputs fail to deliver accurate or actionable insights, no matter how sophisticated the technology.

For data platform owners, the priority is urgent. Stopping bad data at its source and preventing it from spreading across systems is essential to protecting revenue, maintaining compliance, and meeting SLAs. As you continue, you’ll learn how to recognize bad data, trace it to root causes, measure its impact, and establish safeguards that keep information accurate and reliable at scale.

Key takeaways

• Bad data takes many forms, including inaccurate records, missing values, duplicate entries, and irrelevant information. These flaws weaken trust in analytics.

• The consequences of bad data are revenue loss, compliance risks, broken AI models, and poor customer experiences.

• Detecting root causes is critical. Anomaly detection, catalog validation, lineage tracing, and collaborative tracking help teams uncover and address these issues early on.

• Measuring the impact of bad data on daily operations and strategy provides clarity through metrics like accuracy, completeness, and resolution times.

• You can prevent problems at scale by enforcing governance policies, automating validation, and cleansing data to keep it AI-ready.

What is bad data?

Bad data is incomplete, inaccurate, or inconsistent information that teams can’t trust for decision-making. 

Bad data often appears as missing values in transactional systems or duplicate CRM records that distort reporting. These issues typically stem from root causes such as manual entry errors or poorly integrated applications. For data platform owners, the consequences are daily realities: delayed SLAs, rising reprocessing costs, and weakened trust in business-critical systems.

To prevent these harmful impacts, organizations must ensure that their data remains clean and trustworthy. By maintaining high data quality standards, they can rely on their information to drive informed decisions and achieve their objectives.

What are the common causes of bad data, and what impact do they have?

Several operational and organizational factors contribute to bad data. The following challenges not only affect data integrity but also disrupt business processes and decision-making:

Manual entry errors

Human error during data entry creates inconsistencies that quickly spread across systems. In 2025, Citigroup staff nearly transferred $81 trillion instead of $280 because they failed to delete pre-populated zeros in a backup system. The mistake was flagged and reversed within 90 minutes, but this near miss shows how even routine input errors can spiral into massive financial risks.

These failures don’t just threaten banks. In healthcare, manual entry often leads to inconsistent terminology for patient allergies across systems—for example, one record might say “penicillin allergy” while another says “allergic to penicillin.” This lack of standardization causes critical information to be lost during transfers, which can result in life-threatening drug interactions.

Poor system integration and machine learning failures

Unity Software, best known for its real-time development platform used in gaming and beyond, relies on machine learning to power its ad-targeting tools. In 2022, the company reported a $110 million loss and a steep stock drop after two major data failures.

First, Unity’s Audience Pinpointer tool—built to assign value to ad requests in real time—began producing inaccurate predictions, reducing ad revenue. At the same time, bad data from a large customer corrupted parts of Unity’s models. Executives later described this as a "self-inflicted wound."

These errors exposed gaps in data validation and monitoring. Without strong checks for model drift or bad inputs, flawed data can quickly degrade performance, hurt advertiser trust, and trigger costly financial and reputational damage.

Siloed data management

When departments operate their own independent data stores, they create fragmented views of customers and inconsistent metrics. For instance, marketing teams might measure campaign success using different models than sales teams, which makes it impossible to accurately assess ROI or optimize budgets across channels. This lack of coordination between teams can severely limit data-driven decision-making.

Lack of data governance

Poor data governance creates costly mistakes that disrupt decision-making and operations. In 2024, regulators fined Starling Bank £29 million for failing to maintain effective financial crime controls. The bank expanded quickly between 2021 and 2023, opening more than 54,000 accounts for high-risk customers. Because its systems didn’t screen those accounts against sanctions lists, gaps in oversight exposed Starling to compliance breaches, reputational damage, and a huge regulatory fine.

This case shows how weak governance and siloed ownership allow errors to scale into systemic failures. When each department or team owns its data without shared standards or oversight, inconsistencies slip through unchecked. Finance may follow one set of validation rules, while compliance applies another, leaving gaps that expose the entire organization. For data platform owners, the lesson is clear: Without accountability and defined standards, data quality issues multiply, threatening both financial stability and customer trust.

Inadequate data lineage tracking

Without tracking data from source to consumption, organizations can’t pinpoint where errors arise or how changes impact downstream systems. This lack of visibility lets data quality issues slip through until they trigger serious business disruptions. This makes it difficult to fix the root cause.

These challenges highlight why the next step is not just spotting the consequences but also investigating where the problems originate.

How to detect bad data and identify root causes

Identifying bad data requires a systematic approach to both detection and root cause analysis. Below are effective strategies that will help you detect bad data and uncover its origins:

Use automated anomaly and pattern recognition

Automated anomaly detection tools such as Monte Carlo and Bigeye help teams spot data inconsistencies that manual processes often miss. By analyzing large datasets in real time or in batches, these tools can flag schema drift, null values, duplicate records, and other anomalies. This proactive monitoring helps teams identify issues before they cascade into downstream systems or decision-making.

Implement drill-down lineage tracing

Understanding how data flows across systems is crucial for finding the root causes of bad data, and drill-down lineage tracing makes that possible. It enables users to track data from its source to its final destination, highlighting any transformations, errors, or mismatches along the way. By visualizing data lineage, organizations can pinpoint where issues arise. These issues may stem from data entry, system integrations, or data transformations. With that clarity, teams can take corrective actions at the right point in the data pipeline.

Encourage collaborative issue tracking and annotations

Data quality management isn’t just data teams’ responsibility. Collaborative tools for issue tracking and annotations bring those stakeholders together, allowing teams to document problems, investigate their causes, and work jointly on solutions. These capabilities foster a proactive data stewardship culture where team members across departments can address data inconsistencies and contribute to maintaining high-quality data standards.

Leverage catalog-based contextual validation

Data catalogs provide context-driven insights that ensure data is not only accurate but also aligned with its intended use. This method helps organizations validate data within the broader scope of its business relevance. Here are the key benefits:

• Ensuring that data meets predefined quality standards

• Providing visibility into data lineage and flow

• Identifying inconsistencies early in the process

• Supporting continuous data quality improvements

• Enforcing schemas with alerts to flag deviations

• Offering role-based visibility into lineage for accountability

By leveraging contextual validation through a data catalog, organizations enhance data integrity and build trust in their data across the enterprise. Leading solutions like the Alation Data Intelligence Platform make this possible by combining metadata, lineage, and quality checks into one system.

The image below illustrates how Alation’s data catalog organizes metadata to support data quality and governance:

How to quantify the impact of bad data

Once you’ve identified the causes of bad data, it’s important to measure its impact on decision-making, operations, and strategy. Tracking the right metrics will help you understand the extent of this problem and guide improvements. 

Below are key metrics and KPIs to monitor and how to report them effectively for different roles:

• Data accuracy rate measures the percentage of data entries that are correct. This metric ensures you make decisions using reliable information.

• Data completeness tracks whether users fill every required field. Incomplete data leads to missed insights and creates inefficiencies.

• Data consistency measures how uniform data is across different systems and platforms. Inconsistent data can lead to errors and confusion.

• Time to detection tracks how long it takes to identify data quality issues. Early detection reduces the downstream impact of bad data.

• Time to resolution measures how long it takes for teams to fix data quality issues. Efficient resolution is crucial for minimizing bad data’s negative impact on business operations.

These metrics provide a clear view of both data quality and the time it takes to remediate it, helping leaders address issues effectively.

Role-based reporting views

To make these metrics actionable, organizations should customize their reporting views based on different teams’ roles. An effective data quality dashboard should highlight the following for each role:

• Data engineers: For data engineers, metrics like time to detection and time to resolution are most important. These metrics help them evaluate the effectiveness of data quality processes and highlight areas that need attention.

• Data analysts: Analysts typically focus on data accuracy, completeness, and consistency to ensure that the data they use for analysis is reliable. As a result, they need detailed views that enable them to track these metrics across different datasets.

• Business leaders: Senior leaders need high-level KPIs, such as overall data accuracy and data downtime, to assess the strategic impact of data quality on business performance. They can then allocate resources effectively to improve data governance and quality.

• Compliance officers: Compliance teams focus on data validity, completeness, auditability, and compliance, so that data meets regulatory standards. They need role-specific reports that track these aspects to prevent legal or compliance risks. They may also leverage PII software to ensure private data is managed compliantly.

By aligning data quality metrics with role-based reporting views, organizations can ensure the right stakeholders have the necessary information to take action.

How to prevent bad data and improve data quality at scale

After quantifying the impact of bad data, the next step is prevention. To move from understanding the problem to addressing it, you need a strategic approach that combines standardized practices and continuous monitoring to ensure high-quality data at scale. Here’s how you can achieve this:

Standardize data entry and governance policies

First, it’s crucial to set clear standards for data entry and governance. This means establishing consistent data formats, validation rules, and clear data definitions across all systems. 

By using a centralized data catalog like Alation, you can manage metadata efficiently and ensure all your data assets are well-documented and accessible. This approach enhances transparency and accountability and helps teams follow established data quality standards while minimizing errors.

Automate data validation and deduplication

Automating data validation and deduplication is also crucial for ensuring data quality because manual checks are time-consuming and prone to errors. By using automated tools like Alation’s Data Quality Agent, you can enforce data standards in real time and catch issues early. This capability helps you prevent bad data from spreading, eliminates duplicate data, and saves valuable time in the process.

Implement automated cleansing scripts and routines

Finally, you should regularly clean data to ensure it stays accurate and relevant. With automated cleansing scripts, you can set up routines that run at scheduled intervals. Such routines correct data inconsistencies, remove duplicates, filter out irrelevant data, and standardize formats. 

In turn, these automated processes ensure that data remains clean and ready for analysis, which enables your teams to make more reliable decisions. It’s equally important to maintain visibility into these processes for auditing. SOC reporting, SOX, GDPR, and other regulations often depend on clear records of data handling, with requirements varying by industry and region.

➜ To strengthen your data quality efforts further, explore effective data quality assurance strategies.

Bad metadata leads to bad AI outcomes

Gartner forecasts that by 2025, as a result of poor data quality, 30% of generative AI projects will be discontinued. At the core of this challenge is metadata—the labels, definitions, and context that describe data and govern how it can be used. Metadata allows AI models to interpret inputs correctly, qualify training data, and produce outputs that align with business goals.

When metadata is incomplete, outdated, or inconsistent, AI systems lose this context. For example, mislabeled training data can skew model performance, inconsistent time stamps can distort sequence analysis, and missing lineage records can prevent teams from tracing errors back to their source. In each case, bad metadata weakens reliability, reduces transparency, and magnifies downstream risks when scaled through AI.

This reality proves that metadata is not just a technical detail but a deciding factor in AI success. Gartner emphasizes that “AI-ready data” requires more than volume—it must meet the right quality thresholds and reflect real use cases. For instance, a healthcare AI model trained without metadata that captures demographic diversity may fail to predict outcomes across different patient groups. Grounding data in accurate metadata ensures AI systems align with the needs of each initiative, rather than producing unreliable results.

Key checks for AI-ready metadata

To confirm that training data reflects real-world conditions and supports reliable AI outcomes, organizations should apply the following checks:

• Lineage tracking shows where data comes from and how it changes.

• Bias detection uncovers unfair patterns that could skew results.

• Representativeness checks confirm that data covers expected scenarios.

• Drift monitoring flags changes over time that affect model accuracy.

By applying these checks, organizations can reduce AI risk and ensure their systems deliver reliable, actionable insights.

➜ For a deeper dive into creating a solid data foundation for AI, download Alation’s “Data Quality for AI Readiness” whitepaper. It offers detailed strategies on how to build a data quality framework that supports AI at scale.

Building AI success with Alation

Bad data creates significant risks for AI, from flawed models to unreliable business outcomes. The challenge goes beyond detecting errors. Organizations also need safeguards that stop issues from spreading across systems.

The Alation Data Intelligence Platform addresses these risks by ensuring AI runs on reliable metadata and governed data. The Data Quality Agent works directly within the data catalog to deliver the following capabilities:

• Detect anomalies such as schema drift, null values, and duplicate records before they spread

• Automate validation and deduplication in real time to strengthen accuracy

• Monitor drift and bias to keep AI models aligned with real-world conditions

• Enforce quality rules and audit trails consistently across platforms

The image below shows how the Alation Data Quality Agent surfaces real-time data health through scorecards, monitors, and trend analysis, making it easier to detect issues early and ensure trusted outcomes.

By combining rule-based checks with machine learning–powered automation, Alation delivers trusted data at scale. This capability strengthens data quality management for AI, reduces risk, and ensures models produce reliable outcomes.

When poor data threatens to derail AI initiatives, Alation provides the visibility and control needed to keep them on track. Schedule a demo today to see how to build AI success on a foundation of trusted data.