Impact of Poor Data Quality

“Poor data quality is like a virus that spreads through the organization, infecting processes, decisions & ultimately, the bottom line.” – David Loshin

Data is the backbone of modern businesses. It’s what guides decisions, strategies & growth. 

Business leaders are often told to rely on data for making decisions, yet many struggle with the quality of the data they have. 

Many businesses don’t realize how much they’re impacted by data errors until it’s too late. They might overlook duplicate records, outdated information, or incorrect details, thinking these issues are minor. But these “minor” issues add up, creating a massive problem.

Organizations often overlook the significance of data quality, leading to habitual acceptance of errors. Statements like, ‘the data for that report isn’t great,’ become common, highlighting a deeper cultural issue.

In practice, conducting a comprehensive review of data quality issues across all business areas can be time-consuming & impractical. Many areas may not prioritize data quality, and stakeholders might resist exposing their data issues. Therefore, focusing on ‘open doors’—areas and stakeholders already concerned with data quality—can be a more effective approach.

In this article, we’ll break down what poor data quality is, why it happens & how it impacts your business. 

More importantly, we’ll explore how you can fix these problems and build a solid foundation for your business success.

Let’s understand the real impact of poor data quality.

What Exactly is Poor Data Quality?

Poor Data Quality

Poor data quality is data that fails to support business objectives due to issues like incompleteness, inaccuracy, or untimeliness. It prevents business processes from being completed on time, within budget, or with appropriate outcomes.

It happens when your data is messy, unreliable & full of errors. In businesses, poor data quality often means dealing with duplicate entries, outdated information, or simply wrong details.

It’s more like trying to read a book with missing pages & smudged ink. But poor data quality is more than just these obvious mistakes. It’s about the hidden flaws that people often miss. These flaws come through manual data entry errors, system migrations, or inconsistent data formats. 

When data is unreliable, every decision based on it is shaky. Poor data quality leads to wasted time, as teams scramble to verify & correct information. It means marketing campaigns miss their targets because of faulty contact lists. Sales teams might call the wrong numbers or send emails to outdated addresses. Operations might face inefficiencies because inventory data is incorrect.

Beyond the surface, poor data quality affects trust. When managers can’t trust the data, they can’t make confident decisions. This mistrust spreads, impacting overall business efficiency and morale. 

Identifying stakeholders who are already concerned about data quality can help prioritize and address these issues more effectively. These stakeholders often have firsthand experience with the negative impacts of poor data quality and can be crucial allies in driving improvement initiatives.

But

Ensuring data quality requires the involvement of various roles such as data quality developers, network engineers, and database administrators, each playing a critical part in maintaining data integrity. 

For instance, a typical annual salary of a data quality developer might be around US $120,000, highlighting the investment needed in human resources.

But why does poor data quality happen in the first place?

Common Causes of Poor Data Quality

Common Causes of Poor Data Quality

Poor data quality doesn’t happen by accident. It’s often the result of several overlooked issues within a business.

Regulated industries, such as financial services and pharmaceuticals, face unique challenges due to stringent data requirements. Financial regulators, post the global financial crisis, mandate high-quality data to ensure prudent lending practices, while pharmaceutical regulators like the FDA and MHRA conduct unannounced inspections to ensure data integrity and patient safety.

  • Manual Data Entry Errors: When employees manually enter data, mistakes are bound to happen. Typos, misplaced digits & incorrect entries can easily slip in. Imagine typing hundreds of customer details; even the most careful person can make errors.
  • Lack of Training: Many employees aren’t trained on the importance of data quality. They might not understand how their input affects the entire system. Without proper training, staff may not follow best practices for entering and managing data.
  • Inconsistent Data Formats: Data comes from various sources, each with its own format. When these formats clash, it creates inconsistencies. For instance, one system might record dates as MM/DD/YYYY, while another uses DD/MM/YYYY. This can lead to confusion and errors.
  • Poor Data Governance: Many organizations lack clear policies and processes for managing data. Without good governance, there’s no standard way to handle data entry, updates, or deletions. This leads to a chaotic data environment where mistakes are common.

Financial services must adhere to standards such as BCBS 239, which mandates effective risk data aggregation & reporting. Lack of clear data governance policies can lead to significant compliance issues & operational inefficiencies.

  • Errors During Data Migration: Moving data from one system to another is tricky. During migration, data can get lost, corrupted, or misaligned. These errors are hard to detect and can cause significant problems down the line.

During mergers and acquisitions, aggressive timelines often lead to data being migrated without adequate cleansing, resulting in duplicates and misaligned data that affect business operations.

  • Outdated Information: Data becomes outdated quickly. Customer addresses change, phone numbers get updated, and businesses relocate. Without regular updates, your data becomes stale and unreliable.
  • Duplicate Entries: Duplicates happen when the same data is entered more than once. This can be due to multiple data sources, manual entries, or system errors. Duplicate data skews reports and makes it hard to get a clear picture.
  • Lack of Accountability: When no one is responsible for data quality, it falls through the cracks. Clear accountability ensures someone is always monitoring and maintaining data accuracy.

These issues may seem small, but together, they create a significant problem. 

You should keep in mind that the impact of data quality issues can vary across different parts of the organization. Some stakeholders might resist exposing their data issues due to fear of judgment or a preference for their own methods of managing data quality. This resistance can hinder organization-wide data quality improvement efforts.

  Get Your Data Quality Checklist! Learn how to tackle dirty, duplicate, and disparate data in this step-by-step, interactive checklist.     

 

The Ripple Effects: How One Error Leads to Another

One small error in your data can trigger a chain reaction of problems. Imagine a simple typo in a customer’s email address. This error means the customer won’t receive important updates. The marketing team, unaware of the typo, assumes the customer is disengaged. 

They might then exclude this customer from future campaigns.

Now, imagine this happens across thousands of entries. Sales teams follow up on leads that have been incorrectly categorized. Financial reports, relying on flawed data, present an inaccurate picture of the company’s performance. This leads to misguided business decisions. 

These ripple effects of one tiny error can affect every part of the business, causing inefficiencies and missed opportunities.

Organizations need to iterate through the data quality improvement cycle continuously. Each iteration requires funding and attention to maintain high data quality. This ongoing process ensures that data quality improvements are sustained over time.

Business Risks from Bad Data

Bad data can lead to serious business risks. Financial losses are common when decisions are based on inaccurate data. Misguided strategies result in wasted resources and missed opportunities.

Legal troubles arise from non-compliance with regulations. Inaccurate customer data can lead to privacy violations & hefty fines. Reputation damage follows when customers receive poor service due to data errors. 

Trust is hard to rebuild once it’s lost.

Ensuring high data quality is critical to avoid these risks and maintain smooth, efficient operations.

Let’s say you run a retail business and rely on sales data to forecast demand. Due to errors in data, you overestimate the need for a seasonal product. This results in overstock, tying up capital & storage space, forcing you to sell at a discount later, cutting into profits. 

At the same time, accurate data on another product is missed, causing stockouts and missed sales opportunities. Customers looking for this product leave disappointed, turning to competitors. These scenarios not only hurt your bottom line but also erode customer trust and loyalty, demonstrating the critical need for high-quality data.

Neglecting the involvement of key roles such as information privacy experts and application security teams can lead to significant legal and financial risks. For instance, privacy experts are needed to assess whether any data has implications under legislation like the GDPR, and security teams set up roles used by the data quality tool to access data securely.

Develop a detailed plan for the discovery phase, documenting both costs and benefits. This strategic approach helps in managing data quality risks by providing a clear understanding of the expected outcomes and necessary investments.

Real-Life Consequences of Poor Data Quality

Poor data quality can have serious real-world impacts. Here are a few examples that show how poor data quality can lead to significant financial losses, operational disruptions & reputational damage.

  • 2017 Equifax Data Breach: Equifax, one of the largest credit reporting agencies, experienced a data breach that exposed sensitive information of 147 million people. The breach was partly due to poor data management practices, including outdated systems and unpatched software, which allowed hackers to exploit vulnerabilities.
  • NASA Mars Climate Orbiter: In 1999, NASA lost its $125 million Mars Climate Orbiter because of a data error. The spacecraft crashed due to a simple mistake: one engineering team used metric units while another used imperial units. This mismatch in data led to the spacecraft’s incorrect trajectory and its eventual destruction.
  • JPMorgan Chase Trading Loss: In 2012, JPMorgan Chase suffered a trading loss of $6.2 billion due to poor data quality. The loss, known as the “London Whale” incident, was exacerbated by errors in the bank’s risk models, which relied on flawed and incomplete data. This led to significant financial loss and damaged the bank’s reputation.
  • Sainsbury’s Nectar Card: In 2014, UK supermarket chain Sainsbury’s faced a customer backlash due to poor data management of their Nectar loyalty card scheme. Many customers received incorrect points balances, leading to dissatisfaction and mistrust in the brand. The error was due to data inaccuracies and migration issues when updating the system.

These examples highlight the importance of maintaining accurate & reliable data in all aspects of business operations.

A top-down benefits calculation approach can be used, where metrics within the organization are benchmarked against similar organizations to detect underperformance. The gap between current performance and the benchmark can then be analyzed to estimate the benefit of resolving data quality issues.

Reporting and Analytics Impacts

Reports are designed to summarize data for decision-making, but data quality issues at the source lead to misleading insights. Senior stakeholders often miss data gaps due to high-level summaries. 

For example, missing data in a road traffic collision report can lead to incorrect conclusions, impacting policy decisions by the Department of Transport. Summarized data in that case will hide significant issues, causing flawed decisions & strategies.

Simple Steps to Improve Your Data Quality

Improving data quality doesn’t have to be complex. Here are some straightforward steps to get you started:

✔ Conduct Regular Data Audits

Regularly review your data to identify and correct errors. Schedule periodic checks to spot inconsistencies, missing values, and duplicate entries. These audits help maintain data integrity and ensure that any issues are addressed promptly.

As part of your regular data audits, it is essential to quantify the benefits of data quality improvements. For example, calculate the time saved from reduced error corrections or the increase in successful marketing campaigns due to accurate contact information.

✔ Standardize Data Entry Processes

Establish clear guidelines for data entry across your organization. Ensure that all employees follow the same procedures for entering and updating data. Standardized formats reduce errors and make it easier to integrate data from different sources.

✔ Train Your Team

Educate your staff about the importance of data quality. Provide training on best practices for data entry and management. When employees understand the impact of their actions, they are more likely to be diligent and accurate.

✔ Use Data Quality Tools

Implement tools specifically designed to improve data quality. Tools that offer powerful solutions for data cleansing, matching, and integration. These tools can automatically identify and correct errors, saving time and improving accuracy.

✔ Automate Data Cleaning Tasks

Automation can significantly reduce the time spent on manual data cleaning. Use software to schedule regular cleaning tasks. This ensures that data is continuously monitored and maintained without requiring constant manual intervention.

✔ Create a Data Governance Framework

Develop a robust data governance framework that outlines policies and procedures for managing data. This framework should define roles and responsibilities, ensuring accountability for data quality across the organization.

✔ Perform Data Integration Efficiently

Integrate data from various sources seamlessly. Go for the integration capabilities that allow you to connect to multiple databases, CRMs, and file formats effortlessly. This ensures a unified view of your data without the need for third-party connectors or plugins.

✔ Go For Advanced Data Matching Techniques

Use advanced data matching tools to resolve duplicates and create master records. WinPure’s fuzzy matching technology can identify and merge non-exact matches, ensuring a comprehensive and accurate dataset.

✔ Maintain Continuous Monitoring

Set up continuous monitoring systems to track data quality in real-time. These systems can alert you to potential issues before they escalate, allowing for immediate corrective action.

✔ Encourage a Culture of Data Quality

Promote the importance of data quality within your organization. Encourage employees to take ownership of the data they handle and to prioritize accuracy in their daily tasks.

By following these steps, you can significantly improve the quality of your data. This leads to better decision-making, enhanced operational efficiency & a stronger foundation for your business’s success.

Pro Tip: In the early phases of a data quality initiative, it is crucial to have a dedicated data quality manager and clear governance structures. This ensures that the initiative is well-coordinated and aligns with the overall business strategy.

Be prepared to justify the costs associated with data quality initiatives. Document responses to potential challenges and consider presenting a phased approach to implementation. This can include starting with one process area or data object initially, which can help in gaining stakeholder buy-in and demonstrating early benefits.

Building a Data-Friendly Culture at Work

Building a Data Friendly Culture at Work

Creating a data-friendly culture starts with leadership. Leaders must prioritize data quality and set clear expectations.

For example, a CEO could share how data-driven decisions led to significant cost savings, highlighting the impact of quality data.

Regular training sessions are essential. Teach employees how their work affects data quality. Use real examples, such as a typo in a product code causing supply chain disruptions, to illustrate the importance of accuracy. 

Establish clear guidelines for handling data to ensure consistency. For instance, standardizing date formats across all departments can prevent confusion and errors.

Assign specific data responsibilities to team members. When everyone knows their role, it ensures data is managed properly. 

Encourage teams to report discrepancies. For example, a marketing team could flag incorrect email addresses that lead to failed campaigns.

Foster an environment where employees feel comfortable discussing data problems. Open communication leads to better solutions. Recognize and reward employees who excel in maintaining data quality. 

Acknowledge a team member who identifies a critical data error, preventing costly mistakes.

By focusing on these areas, you can build a culture that values and maintains high-quality data. 

How WinPure Can Help You Take Control of Your Data Quality

Take Control of Your Data Quality

WinPure offers a comprehensive solution to tackle your data quality challenges head-on. With easy data integration, you can connect and unify data from all popular file formats, CRMs, and databases without needing third-party connectors. 

This plug-and-play approach saves time and ensures seamless data handling across your organization. By integrating data effortlessly, you get the big picture, eliminating the need to switch between platforms and files. 

WinPure makes it simple to add, remove, merge, and purge files, providing a comprehensive overview of your records.

The AI-powered data matching tool in WinPure combines deterministic and probabilistic matching abilities. This helps reduce false positives and improve accuracy by using advanced algorithms that understand human error nuances. 

You can set custom rules, create dictionaries, and define match conditions to achieve highly accurate results.

WinPure’s record linkage tool helps merge multiple datasets effortlessly, ensuring a holistic customer view and a single source of truth. 

By connecting various data sources, it allows businesses to unlock hidden insights, eliminate duplicates, and create reliable data for better decision-making.

With advanced address verification, WinPure ensures the accuracy of postal addresses, supporting industry-leading technologies for precise geocoding and validation. This feature helps improve delivery rates, reduce incomplete data & enhance GDPR compliance.

Overall, WinPure provides a complete, user-friendly platform to clean, match, and manage your data, making your data quality management process efficient and reliable.

Wrapping It Up

Poor data quality harms business success by causing errors, inefficiencies & financial losses. Key issues include manual entry mistakes, lack of training, inconsistent formats, poor governance, outdated information, and migration errors. Solutions include regular audits, standardized processes, staff training, and advanced data tools. Building a data-centric culture with clear accountability and using solutions like WinPure transforms unreliable data into a valuable asset, ensuring accurate decisions and sustainable growth.

Written by Faisal Khan

Faisal Khan is a human-centric Content Specialist who bridges the gap between technology companies and their audience by creating content that inspires and educates. He holds a degree in Software Engineering and has worked for companies in technology, healthcare, and E-commerce. At WinPure, he works with the tech, sales, and marketing team to create content that can help SMBs and enterprise organizations solve data quality challenges like data matching, entity resolution and master data management. Faisal is a night owl who enjoys writing tech content in the dead of the night 😉

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