The economy loses $3.1 trillion every year due to poor data monitoring and governance. Plus, poor-quality data costs organizations $15 million in the form of customer losses and missed opportunities. So, ensuring good data quality is essential for business success. Why? Because it improves lead generation, enhances customer understanding, and leads to stronger customer relationships. But that’s barely the tip of the iceberg.

Let’s explore how data quality affects business success and how you can maximize its impact.

What Is Data Quality?

Data quality refers to the degree to which data meets specific requirements and is fit for its intended purpose. High-quality data is free from inconsistencies, errors, and redundancies. It also accurately represents the real-world phenomena it’s intended to capture.

In contrast, poor-quality data can lead to inaccurate insights, flawed analysis, errors and inconsistencies, incorrect conclusions, wasted time, reputational damage, and reduced customer trust.

Characteristics of Data Quality

Data must have the following characteristics to be useful:

1. Completeness – It means you have all the necessary information on hand.
2. Consistency – It means your data is consistent across different systems or sources.
3. Accuracy – It means your data depicts the actual values or characteristics of what you’re measuring.
4. Relevance – It means your data is relevant to the purpose or context for which it is being used.
5. Timeliness – It means your data is up-to-date.
6. Validity – It means your data meets data standards and business rules.

Benefits of High-quality Data

Let’s take a look at how high-quality data is the key to business success:

1. Improved Decision-making

High-quality data provides a solid foundation for generating accurate and reliable insights. This enables decision-makers to identify patterns, trends, and correlations that inform strategic planning, allowing them to align their goals, objectives, and actions with market realities.

For instance, Xerox reduced call center turnover by 20% by analyzing which candidates were likely to remain with the company. Once they realized that creative people would stay for longer, they focused on them.

Similarly, the Bank of America analyzed thousands of worker surveys and found that allowing staff to take their breaks together led to performance improvements. This enabled the company to reduce stress levels by 19% and improve performance metrics by 32%.

2. Enhanced Operational Efficiency

By analyzing high-quality data, organizations can identify areas for improvement, such as bottlenecks or quality gaps. This helps them implement targeted process optimization strategies, measure the impact of those changes, and refine their operation to ensure efficiency.

For instance, a global quick-service restaurant chain used people analytics to identify bottlenecks limiting store success. They found that career development, staff empowerment, and shorter shifts were key to improving productivity.

By making these changes, the company experienced a 100% increase in customer satisfaction, improved speed of service by 20 seconds, reduced new hire attrition, and enhanced sales by 5%.

3. Better Customer Experiences

With accurate and comprehensive data, businesses can dive into the minds of their customers, understanding their preferences and desires. This enables them to deliver personalized experiences that hit the bullseye every time.

For instance, instead of blindly suggesting a product, Amazon uses customer purchase data and machine learning to decide which products it should recommend to customers. And according to McKinsey, this powers over 35% of Amazon’s consumer purchases.

Plus, Amazon’s on-site recommendation conversion rate can be as high as 60%, according to Forrester. This is massive compared to competitors like eBay or Aliexpress.

How to Ensure Data Quality

Now that you know how high-quality data can improve business performance, let’s look at ways you can ensure data quality:

1. Implement Data Quality Monitoring

Before jumping on the data quality management wagon, it’s essential to identify existing data quality issues your organization is facing and how they’re impacting business performance and KPIs.

Once you’ve found how business processes are influencing KPIs and data assets, you should begin building a data quality monitoring program that defines targets and the roadmap to success.

Here, you should consider using artificial intelligence (AI) to automate processes.

For instance, let’s say you run an e-commerce website and have various tracking tags, such as Google Analytics, Facebook Pixel, and Google Ads conversion tracking, running in the background.  

To ensure you get accurate data on user behavior, website conversions, and marketing campaign performance, auditing your tags is essential. It’ll also help you ensure the information being captured aligns with actual user interactions.

2. Establish a DQ Standard

When you’re developing a data quality monitoring program, you need to establish data quality (DQ) standards. These will serve as a benchmark against which data is measured to determine its accuracy, completeness, and consistency.

A DQ standard will enable employees, data and analytics leaders, and other stakeholders to understand the expectations and requirements for data quality within the organization. It’ll also provide a clear framework and set of guidelines everyone can refer to when working with data.

3. Perform Data Profiling

Data profiling means examining data from the primary source and summarizing what it says. This practice helps you identify corrective actions and generate valuable insights that drive improvement ideation.

Plus, data profiling can also enable you to identify data quality issues in your primary sources, reducing data inaccuracies. But it isn’t a one-time activity. That’s because errors don’t wait on anybody, and missing or incorrect information can lead to problems that are challenging to resolve down the line.

4. Create a DQ Dashboard

Once you’ve checked your data sources, you should create a data quality (DQ) dashboard that gives a comprehensive overview of DQ. This will help you easily identify trends influencing business operations, gain insights into roadblocks, and take corrective actions.

Moreover, it’ll enable you to track performance over time, reduce the chances of repeating similar mistakes, ensure data enrichment through appending, and prevent data inaccuracies from slipping you by.

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