The Hidden Costs of Bad Data — How Inaccurate Information Hurts Your Business

The accuracy and reliability of data can make or break an organization. And yet, according to a report by IBM, bad data is pervasive. So pervasive, that it costs U.S. companies a staggering $3.1 trillion annually​.

From financial losses and operational inefficiencies to misguided decision-making and damaged customer trust, the repercussions of bad data ripple through every layer of an organization — and the consequences can be dire and far-reaching.

Understanding these impacts is the first step toward mitigating the risks and investing in data quality initiatives that can safeguard your organization's future.

Understanding the Financial and Operational Toll of Bad Data

Bad data has a profound impact on enterprise organizations. Recognizing the impact bad data can have is crucial for mitigating the risks associated with poor data quality. These are just some of the downstream effects poor data quality have:

Financial Costs

Bad data can lead to significant financial losses for organizations. Inaccurate or incomplete data can result in everything from poor budgeting decisions to misdirected marketing campaigns to inaccurate TAM calculations, to erroneous competitor analysis, to costly billing errors.  

Verizon Wireless, for example, made so many inadvertent billing mistakes in 2010 that it agreed to a voluntary payment of $25 million to the U.S. Treasury in a settlement with the Federal Communications Commission. More recently, Consolidated Edison—one of the largest energy companies in the country—charged customers a higher gas rate than it was allowed to charge under a utility tariff and suffered a class-action lawsuit.

Rectifying errors like these is expensive, straining an organization's financial resources and directing employee attention away from more strategic projects, ultimately causing missed opportunities to make up for substantial losses.

Operational Inefficiencies

When data quality is compromised, operational processes suffer — bad data can lead to inefficiencies in supply chain management, inventory control, and production planning. As a result, employees spend considerable time verifying and correcting data errors, reducing their overall productivity.

Target's entry into the Canadian market was fraught with supply chain issues due to bad data. Inventory inaccuracies and mismanaged logistics led to empty shelves and customer dissatisfaction, contributing to their eventual withdrawal from Canada​.

A Harvard Business Review deep dive into the Target Canadian expansion explains that new stores struggled with distribution challenges and shelf replenishment, leading to stock-outs. That, coupled with non-US inventory and higher prices caused Target Canada foot traffic to plummet.

Inefficiencies like these significantly disrupt business operations — sometimes to the point where they must be abandoned — slow down decision-making, and decrease the organization’s ability to respond quickly to market changes.

Poor Decision-Making

Decisions based on flawed data can steer your business in the wrong direction. Inaccurate data skews analysis and insights, leading to strategies that fail to meet business goals. This can affect various aspects of the business, including marketing strategies and resource allocation.

Bad data can also mislead product development teams, resulting in products that do not meet customer needs. Ford's development of the Edsel car is a classic example of market research data being misinterpreted.

Based on their market research data, Ford felt they’d made a good case for a medium-priced car to compete with Chrysler and General Motors. They estimated that by 1965, half of all U.S. families would be buying more cars in the medium-priced field, allowing them to sell upwards of 400,000 Edsel cars a year.

So, they catered to that audience, landing on a design that appealed to “the younger executive or professional family on its way up.” But there was one thing they didn’t account for in the data — a growing shift toward compact cars. This led to a product that failed to meet consumer preferences and resulted in a costly flop.

Reduced Customer Trust and Satisfaction

Bad data can result in incorrect customer information, leading to errors in communication, order processing, and service delivery. This can erode customer trust and satisfaction, damaging a company’s brand and reputation.

Maintaining high data quality is essential to ensure positive customer interactions and foster long-term loyalty. In fact, Segment’s State-of-Personalization Report reveals that 56% of consumers say they’d become repeat buyers after a personalized experience. Companies with messy or incorrect data will be pushed further and further behind.

Wells Fargo suffered severe reputational damage when customers discovered errors in their account information and unauthorized account openings. The ensuing scandal led to a massive loss in customer trust and forced the bank to pay $3 billion to resolve criminal and civil investigations.

Compliance and Regulatory Risks

Many industries are subject to strict regulatory requirements regarding data accuracy and privacy. Bad data can lead to non-compliance with these regulations, resulting in legal penalties, fines, and reputational damage.  

Equifax faced a massive data breach in 2017 due to poor data management practices, exposing the sensitive information of 147 million people. The company reached a global settlement with the Federal Trade Commission, the Consumer Financial Protection Bureau, and 50 U.S. states and territories and had to pay $425 million in fines and legal penalties.

Proper data management practices are essential to safeguard organizations from these potential pitfalls.

How to Minimize and Reverse Bad Data

Addressing the challenges posed by bad data requires a proactive approach, focusing on prevention, detection, and correction. Here are four ways organizations can mitigate and correct the negative impacts of poor data quality:

1. Establish and Implement the Appreciate Data Governance Framework

Establish Clear Policies and Procedures

Define data quality standards and guidelines to ensure consistency across the organization. Create a data governance council responsible for maintaining these standards and addressing data issues promptly​

Align Roles and Responsibilities for Governing Data Assets

Designate data stewards for each department to oversee data quality and compliance. These individuals should have a deep understanding of their domain and the authority to enforce data policies.

Establish Ongoing Measurement Criteria and Process Audits

Conduct periodic data audits to identify and rectify inaccuracies. Utilize automated tools to streamline the auditing process and ensure comprehensive coverage​.

As Doug Kachelmuss, Senior Director of Data and AI, says, “Success of a data governance program is achieved when employees consistently incorporate data governance activities into their daily tasks with checks and balances in place.”

2. Employ Key Data Tools

A tool is just a tool if you don’t know how to leverage it. Establish governance processes, and then choose tools to complement these processes. For example, you might consider:

Data Cleansing Tools

Use data cleansing software to detect and correct errors, such as duplicates, incomplete records, and outdated information. Tools like Monte Carlo, Ascend.io, and BigEye can assist in proactive data quality monitoring and alerting.

Data Integration Solutions

Implement data integration tools to ensure data consistency across various systems. Solutions like Fivetran, Matillion, Airbyte, or DBT help unify data from different sources, reducing the risk of discrepancies.

Data Virtualization

Adopt data virtualization solutions to provide a unified view of data without physically moving it. This approach helps maintain data consistency and reduces the risk of errors during data migration.

Real-Time Data Monitoring

Set up real-time monitoring systems to track data quality continuously. AI and machine learning algorithms can help you identify patterns and anomalies in data, predict potential issues, and ensure higher accuracy. Tools like Collibra, Alation, InfoSphere, and Atlan alert you to potential issues before they escalate.

3. Enhance Employee Training and Awareness

Comprehensive Training Programs

Develop training programs to educate employees on the importance of data quality and best practices for maintaining it. Regular workshops and online courses can keep the workforce updated on the latest data management techniques​.

Promote a Data-Driven Culture

Foster a culture that values data accuracy and reliability. Involve stakeholders from various departments in the review process to ensure that data management practices align with organizational goals and address all relevant concerns. And encourage employees to report data issues and participate in data quality initiatives.

A RACI (Responsible, Accountable, Consulted, and Informed) model can be a huge help in getting everyone on the same page and reminding them of their role in upholding data quality day-to-day.  

Incentivize Data Quality

Implement incentive programs to reward departments and individuals who consistently maintain high data quality standards. Recognize their efforts in company meetings and newsletters to promote positive behavior​.

Doug explains, “Building internal muscle memory is crucial on governance concepts and change like this doesn't happen overnight. However, consistent reinforcement and quick wins help build momentum in driving toward this culture change in organizations.”

4. Regularly Review and Update Data Management Practices

Create a Sense of Urgency

For any organization with a data management plan, instilling a sense of urgency is crucial — otherwise, it’s easy for employees and leadership to let things slip. Making your data management plan a top priority keeps the organization focused and proactive in handling its data assets.  

According to Doug, “To be truly effective, this urgency should resonate with senior leadership and align with the company's strategic goals.”

Continuous Improvement

Start small with your efforts to improve data quality so as to not overwhelm. Build on your policies over time by regularly reviewing data management practices and update them based on new insights and technologies. You might also consider establishing a feedback loop to incorporate lessons learned from past data issues.

Benchmarking and Metrics

Document and implement Key Performance Indicators (KPIs) to track the progress of your initiatives. Use benchmarks to compare performance over time and identify areas for improvement​.

Linking KPIs to revenue or sales loss can be a powerful motivator for driving change. As you gain insights from your data, share what’s meaningful with your stakeholders and keep moving forward.

Invest in a Comprehensive Data Strategy

Understanding the hidden costs of bad data is crucial for any organization aiming to thrive in a data-driven world. And the return on investment (ROI) from these initiatives is substantial — quality data not only prevents costly errors but also unlocks new revenue opportunities and drives innovation, positioning organizations for long-term success.

Data maturity is the key to good organizational decisions, growth trajectory, and AI readiness. Is your data doing everything it can for your organization? Take the Data Maturity Self-Assessment to find any gaps — so you're ready for your next bold move.