The Impact of Poor Data Quality in Business (And How to Fix It)

Every business runs on data, and the quality of that data can either propel your business forward or hold it back.

In today’s digital economy, where data-driven decisions fuel innovation, growth, and competitive advantage, the importance of data quality cannot be overstated. High-quality data is essential for building trust and ensuring that every decision you make is backed by reliable information.

But how does data quality impact business performance exactly? And what are the business costs or risks of poor data quality?

In this blog, we’ll answer those questions and provide you with a comprehensive understanding of why data quality is crucial for your organization’s success.

What is Data Quality?

Data quality refers to the state of your data, which impacts how effectively it can be used to drive business decisions. A data governance strategy is at the heart of data quality, ensuring your data is properly managed, maintained, and monitored to meet organizational standards.

While these classifications differ depending on who you ask, data quality can be broken down into several key dimensions:

1. Accuracy: Data that correctly reflects reality.
2. Completeness: Data that contains all required information.
3. Consistency: Data that is uniform across systems, departments, and databases.
4. Uniqueness: Data that is free from any duplicates.
5. Timeliness: Data that is up-to-date and available when needed.
6. Validity: Data that conforms to the required formats and business rules, ensuring it’s in a usable format.

At C5 Insight, we use the acronym ROTII for bad data quality:

• Redundant
• Outdated
• Trivial
• Inaccurate
• Incomplete

By implementing a data governance strategy that ensures your organization adheres to these dimensions, you establish data integrity. However, if data isn’t properly governed, it becomes a liability rather than an asset, which you’ll see examples of next.

How Does Data Quality Impact Business Performance?

At its core, data quality is about trust. If you can’t trust your data, how can you trust the decisions you make based on it?

When data quality is compromised, the ripple effects can be felt across every aspect of your business, from operations to customer experience. Poor data quality isn’t just inconvenient. It can result in significant disruptions, financial losses, and a damaged reputation.

To best answer the question, “How does data quality impact business performance?” we’ll share real-world examples of poor data quality in business that arise when there are breakdowns in the dimensions above.

Examples of Poor Data Quality in Business

Let’s look at realistic examples of the impact of poor data quality in business:

• Inaccurate income data leads to loan defaults: A financial services company has incorrect customer income data within Microsoft Dynamics 365 CRM, resulting in loans being approved for unqualified applicants, ultimately increasing loan defaults and financial losses.
• Incomplete shipping information causes delivery delays: An e-commerce business fails to collect full shipping addresses in its Dynamics 365 Commerce system, leading to delivery errors, increased customer complaints, and costly redelivery efforts.
• Inconsistent inventory data disrupts supply chains: Inconsistent inventory tracking at a manufacturing company within Dynamics 365 Supply Chain Management can cause stockouts or overproduction, delaying shipments and wasting products, both of which harm profitability.
• Duplicate patient records lead to medical errors: A healthcare provider has multiple records for the same patient in its Microsoft Cloud for Healthcare solution, which can cause confusion and increase the risk of medical errors due to fragmented information.
• Untimely flight data impacts airline operations: An airline uses Microsoft Dynamics 365 for Customer Service but has not kept its data organized, causing scheduling conflicts and overbookings, resulting in flight delays, unhappy passengers, and increased operational costs.
• Invalid student data complicates enrollment processes: A university receives applications with incorrectly formatted student information in Dynamics 365 CRM, leading to delays in enrollment, miscommunication with students, and inefficiencies in the admissions process.

These examples of poor data quality in business highlight some of the business costs or risks of poor data quality across various industries. Whether it delays critical services, disrupts supply chains, or damages customer relationships, bad data can lead to inefficiencies and financial losses.

Next, let’s look at the hard business costs of poor data quality.

The Business Cost of Poor Data Quality

When data quality falls short, the consequences are often immediate but can become more costly to fix if ignored. Businesses with poor data quality are impacted daily, but some don’t realize it until it’s too late. Here are some glaring statistics that estimate the business costs of poor data quality:

• 15-25% of total revenue (MIT Sloan)
• $12.9 million every year (Gartner)
• 9.3 hours per week wasted (McKinsey)

These costs compound over time as poor data management increases the complexity of data ecosystems, convolutes decision-making, and wastes valuable employee hours. When businesses fail to address data quality, the impact reverberates throughout the organization.

One key area where data damage becomes apparent is in business analytics. Even the most sophisticated analytics tools are only as good as the data they analyze.

Measuring the Business Value of Data Quality

While the business costs of poor data quality are significant, measuring the business value of data quality is just as critical. But how do you measure the value of something that seems invisible?

Good data should be like a well-oiled machine: silent, efficient, and reliable.

The importance of data quality in business analytics becomes evident in areas like predictive analytics, strategic planning, and market positioning. By investing in data quality measures like a robust data governance strategy, businesses can trust their analytics to provide actionable insights.

Organizations that prioritize data quality can measure ROI through more concrete outcomes. Accurate and reliable data leads to reduced operational costs, stronger customer loyalty, and enhanced profitability, all of which are direct benefits of investing in a data governance strategy.

At this point, you should have a clear answer to the question, “How does data quality impact business performance?” Now, it’s time to do something about it.

Long-Term Business Success Depends on a Strong Data Governance Strategy

Given data quality’s essential role in business performance, having a comprehensive data governance strategy in place is non-negotiable.

An effective data governance strategy involves clear policies and procedures for managing data throughout its lifecycle. This includes data collection, storage, usage, changes, and eventual disposal. Moreover, it involves assigning roles and responsibilities to ensure accountability at every level of the organization. After all, data management is not just an IT issue—it’s a business-wide concern.

Beyond internal policies, businesses must invest in the right support and technologies to automate data management processes and ensure ongoing data quality monitoring. Regular audits, real-time analytics, and daily monitoring can catch data discrepancies early and prevent larger issues down the line.

But does your team have time for data management and governance tasks on top of their daily work demands?

At C5 Insight, we know your teams are busy. Even cleaned data gets dirty again as employees quickly move through their day.

That’s why we’ve developed a unique service to maintain your data quality (and more) called a LUCK 365 Center of Excellence. After implementing a data governance strategy, we provide continuous support across your Microsoft solutions to protect your data quality and ensure your employees adopt good governance practices.

Let’s connect about your data strategy.