Seth Rao
CEO at FirstEigen
The Real Impact of Poor Data Quality and Governance on Your Business
Bad data can cost you money. It can also damage your reputation, drive good customers away, and negatively affect your entire workforce. Bad data, more often than not, results in bad decisions – and bad decisions can destroy a business.
The true costs of bad data are so overwhelming that they’re scary. If you don’t take data quality seriously, you’re at risk of being blindsided by the enormous impact of bad data.
Quick Takeaways
- Bad data is often inaccurate, incomplete, conflicting, duplicate, and invalid
- Bad data can result in mistargeted marketing campaigns, unrealized revenues, damaged reputations, and unwanted (and expensive) downtime
- Other consequences of bad data include lost productivity, costly noncompliance, and lack of data trust
- Ultimately, bad data results in bad business decisions and missed opportunities
What is Bad Data?
Bad data is any data that is not wholly accurate or doesn’t conform to necessary standards. That can include inaccurate data, incomplete data, conflicting data, duplicate data, invalid data, and unsynchronized data.
The following table describes some of the most common types of bad data.
| Type of bad data | Description | Examples |
| Inaccurate data | Data with incorrect values | Contact info with the wrong email address |
| Incomplete data | Data with empty fields | Customer data missing key information, such as social security numbers |
| Conflicting data | Similar records with different attributes | Two records for the same customer but with different addresses |
| Duplicate data | Identical data from two or more different sources | Two records for the same customer from two different databases |
| Invalid data | Data with nonstandard attributes | A ZIP code with only four digits (instead of five) |
| Unsynchronized data | Data not appropriately shared or updated between two different systems | Customer address changes in one system but isn’t updated in another |
Any of these qualities can result in data that is misleading at best and unusable at worst. If you rely on data to manage your business, bad data can cause you to make misinformed and ultimately ill-conceived decisions.
What’s Behind Bad Data? Top Causes of Poor Data Quality
Understanding the root causes of poor data quality is essential to mitigating its effects and reducing the cost of poor data quality. Here are the primary factors contributing to bad data:
1. Data Integration Issues
- When data is collected from multiple, unintegrated databases, conversion errors often occur.
- Migrating data from older legacy systems to modern platforms like NoSQL databases can amplify inconsistencies and result in missing or incorrect values.
2. Data Decay
- Over time, data naturally deteriorates, especially in marketing and sales departments.
- Also known as data degradation, this issue leads to outdated or irrelevant information that impacts operational efficiency.
3. Poor Data Migration
- During data migration from legacy systems to new environments, risks like missing data, corrupted records, or incomplete transfers are common.
- These issues are often worsened by the lack of proper data governance frameworks.
4. Data Duplication
- Duplicate entries in databases distort statistical models, leading to incorrect reporting and decision-making.
- Duplicates also inflate storage costs and waste resources during processing and analysis.
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What is the True Cost of Bad Data?
According to Gartner, bad data costs organizations $12.9 million a year. Advertisers waste 21% of their media budgets because of bad data. And bad data causes B2B marketers to target the wrong decision-makers almost 86% of the time.
These financial costs are just the obvious consequences of bad data. Bad data can result in disengaged and disillusioned customers, damaged reputations, lost productivity, and missed opportunities.
Taking all these factors into account, what is the true cost of bad data? There are many ways that bad data can negatively impact your business.
The True Business Impact of Poor Data Quality
Mistargeted Customers
Without accurate data, marketers can easily target the wrong customers with the wrong message in the wrong media. Higher-potential customers get overlooked while the company forces its message onto otherwise disinterested consumers. Whether marketers are annoying mistargeted consumers or ignoring customers who might be interested in buying what they’re selling, bad data results in wasted marketing spend and lower-than-expected revenues.
Lost Revenues
When bad data causes a company to target the wrong customers, lost revenues result. Lost revenues can also result from unexpected system downtime, another potential consequence of working with bad data. When bad data results in bad decision-making, sales aren’t as effective as they could otherwise be.
Damaged Reputations
Bad data can cause a company to act in ways that affect its reputation. Maybe it’s a marketing slip-up that puts the wrong product in front of the wrong consumers, or perhaps it’s a more strategic error based on unreliable data analysis. Whatever the actual circumstances, you don’t want to be cast as a company that makes decisions based on bad data.
Unwanted Downtime
Another potential consequence of bad data is system and equipment downtime. Good data can help a company better predict when system or equipment maintenance is necessary and keep those machines and systems up and running. Bad operating data can cause a company to forego necessary maintenance, resulting in unexpected repairs and unwanted downtime.
Lost Productivity
Not only does bad data affect physical operations, but it also affects the people who manage a company’s systems and machinery. A company’s employees can quickly become burned out and discouraged when continually forced to deal with errors caused by bad data. In addition, fixing unnecessary mistakes takes time away from more productive tasks. Bad data affects employee engagement and productivity.
Noncompliance
A company needs accurate and up-to-date data to ensure compliance with the many governmental regulations that impact numerous industries today. Bad data can cause a company to become noncompliant or to lack the audit trail necessary to prove compliance. Since noncompliance can be costly, this is a big issue for many companies, especially those in the healthcare and financial fields.
Lack of Data Trust
When a company can’t trust its data, it can’t trust the decisions it makes. Unreliable data results in uninformed decision-making and overly cautious management. In a world that operates at light speed, a lack of data trust can cause a company to be outpaced by competitors relying on more trustworthy data.
Bad Decisions
Even worse than being cautious about untrustworthy data is blindly believing data that looks good on the surface but ultimately turns out to be inaccurate. Bad data results in bad decisions, which can cripple a company’s ability to both manage daily operations and create long-term strategy. When it comes to making business decisions, relying on bad data is worse than having no data at all.
Missed Opportunities
Bad decisions can cause a company to waste money blindly following the wrong prospects. Bad or misinterpreted data can cause a company to ignore high-potential opportunities and cede ground to better-informed competition.
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Minimize the Cost of Bad Data with DataBuck
When you want to minimize the cost of bad data, turn to FirstEigen’s DataBuck, a data quality monitoring solution that ensures your company is working with the highest-quality data possible. DataBuck uses machine learning and artificial intelligence to generate data quality rules in real time, identify and clean bad data, and provide the quality data you need to make the best possible business decisions.
Automatically turn bad data into good data with DataBuck. Contact FirstEigen today to learn more.
Check out these articles on Data Trustability, Observability & Data Quality Management-
FAQs
Poor data quality can lead to significant costs for businesses, including revenue loss, increased operational expenses, and damaged customer trust. Incorrect data can cause errors in reporting, leading to misguided business decisions that negatively impact growth and profitability.
Bad data leads to inaccurate insights, causing poor business decisions. It can result in ineffective strategies, missed opportunities, and wasted resources. This ultimately affects the bottom line, as decisions based on faulty data can cause financial and reputational damage.
Poor data governance results in weak data management practices, lack of accountability, and inconsistent data handling. This creates data quality issues, such as inaccuracies, inconsistencies, and incomplete data, which can impact business performance and compliance.
Businesses can improve data governance by implementing clear data policies, assigning responsibility for data accuracy, and using tools for monitoring data. Regular audits and employee training on data handling are essential to maintain high data quality standards.
Common sources of bad data include human error during data entry, outdated data, incomplete records, and inconsistencies across systems. Data silos and lack of real-time updates also contribute to poor data quality.
Signs of poor data quality include frequent errors in reports, inconsistent data across systems, high levels of manual data corrections, and customer complaints related to incorrect information. These signs indicate the need for better data quality management.
DataBuck helps manage data quality by automating data validation, detecting anomalies, and providing real-time monitoring. It ensures that data is consistent, accurate, and reliable, reducing the risk of costly errors and improving business outcomes.
Best practices include defining clear data ownership, setting up a governance framework, and using data quality tools for monitoring. Regularly reviewing and updating governance policies helps to prevent data management issues and improve overall data quality.
DataBuck helps by providing automated anomaly detection and real-time data quality checks. It identifies and resolves data errors before they affect business operations, ensuring that your data is always reliable and accurate.
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