Thought leadership
September 15, 2023

The Business Impact Of Bad Data

Data inaccuracies are not merely technical glitches; they can lead to significant business risks, particularly when it comes to an organization's reputation.

Kyle Kirwan

Data inaccuracies are not merely technical glitches; they can lead to significant business risks, particularly when it comes to an organization's reputation. These can range from billing inaccuracies to security breaches and have the potential to cause significant harm to an organization's reputation. Eroded customer trust, negative public perception, and significant financial implications can all stem from bad data. 

This blog will explore the risks of data issues and explain why safeguarding your company's data is essential for protecting its reputation.

Customer Trust Erosion:

One of the most immediate and visible impacts of data issues is on the trust customers have in your company. Inaccurate or incomplete data can lead to incorrect billing information or flawed product recommendations. On the surface, these issues can seem like minor inconveniences, but customers can suffer significant harm. 

An incident involving Hawaiian Airlines stands as a stark example of the significant business impact that erroneous data can have. In 2019, a glitch within the airline's system resulted in a chaos of miscalculated charges, transforming miles into dollars. This led to staggering credit card charges, ranging from $17,500 to a staggering $674,000 for some unfortunate passengers. A separate mistake in customers' favor saw HawaiianMiles customers able to book zero-mile award tickets. While quickly rectified by the airline, customers who had booked these flights were upset and took to social media to express their disappointment, causing lasting brand damage. 

Public Scrutiny and Perception:

In today's interconnected world, data issues rarely go unnoticed. News of data breaches, privacy concerns, or publicized inaccuracies can quickly make headlines, leading to negative press coverage, public scrutiny, and a tarnished public image. Once the trust of the public is lost, it can take a long time to rebuild, making it essential to prevent data issues before they happen.

The incident surrounding Equifax's issuance of inaccurate credit scores in the spring of 2022, exemplifies the business impact of flawed data specifically within the financial sector. Incorrect scores were reported for over 300,000 individuals, influencing interest rates and even leading to loan denials. Equifax attributed the issue to a coding error within an older server system, affecting critical attribute values in credit reporting. The fallout was substantial, marked by a significant drop in Equifax's stock prices following the Wall Street Journal's exposé. A subsequent class-action lawsuit against the company intensified the ramifications, shedding light on the affected individuals, like one Florida resident, who suffered loan rejections due to significantly understated credit scores.

Financial Implications:

Beyond reputation, unreliable data can also have significant financial implications. Regulatory fines, legal costs, customer compensation, and the need for extensive remediation can severely impact the bottom line. 

Last year, Unity Technologies experienced a huge loss when their systems ingested bad data, significantly compromising the accuracy of the tool's predictive machine learning algorithms. This blunder triggered a chain reaction, causing a substantial dip in performance and directly impacting Unity's revenue-sharing model, culminating in an approximate $110 million loss. CEO John Riccitello outlined the financial toll, including revenue impact, costs of rectification, and delayed launches of lucrative features due to prioritizing data quality fixes. The fallout was drastic, evident in a 37% drop in Unity's shares and press coverage questioning investor confidence in the company's strategy and leadership. 

Internal Costs:

While not as publicly devastating as customer-facing data quality incidents, bad data often has immense internal costs for businesses. These repercussions manifest in various ways, often resulting in an impaired ability to leverage data for decision-making due to a lack of trust. Substantial time and resources are also diverted towards addressing and tracing data quality issues.

According to a recent report by Vanson Bourne, data teams are losing an average of 4 working hours per employee per week in efforts to address data preparation issues for analysis. Assuming a full-time equivalency cost of $200,000 per year, this translates to a loss of $120,000 annually for a six-person team. As the imperative to embrace a data-driven approach continues to grow, organizations hampered by subpar data quality will inevitably lag behind.

From eroded customer trust to public scrutiny, negative perception, and substantial financial implications, the aftermath of flawed data can crush an entire organization. 

Businesses must prioritize robust data quality measures to avoid data pitfalls, protect their reputation, and ensure sustained success in a highly competitive and scrutinizing market. The lesson is clear: safeguarding data isn't just a technical necessity; it's the only way to strategically protect an organization's reputation and bottom line.

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Business analyst
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Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights

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