Why ‘Good Data’ Matters - and How to Identify It

Imagine this: your team has just made a major decision based on a dashboard full of data… only to find out later that several key fields were missing, inconsistent, or outdated. Suddenly, what looked like insight was actually misleading and the cost of poor data hits both your budget and your credibility.

The truth is, every organisation has data, but not all data is good data. And in an era where decisions are increasingly data-based, quality is everything.

Butterfly Data’s new whitepaper, “What Does Good Data Look Like?”, is a practical guide for organisations who want to move from “lots of data” to trusted, actionable data that drives real results.

Why Data Quality Matters

The whitepaper breaks down the critical points where data quality truly matters:

• Impact on decision-making: Poor data leads to poor decisions. Even small errors in critical fields can create cascading effects across operations.
  
  
• Responsibility is everyone’s: Data isn’t just IT’s job. Everyone who touches data must help maintain its quality.
  
  
• Practical strategies for improvement: Learn how to implement standards, monitor datasets, and fix errors systematically.

How to Assess Your Data 

At the heart of the whitepaper is DAMA UK’s six dimensions of data quality, which provide a clear, actionable framework:

1. Completeness - Are all critical fields filled? Missing essential information can compromise outcomes.
   
   
2. Validity - Do values conform to expected rules and formats? From UK postcodes to date formats, valid data enables automation and analysis.
   
   
3. Consistency - Are the values logical across datasets? A “UK-born? = Yes” paired with “Country of Birth = Germany” is a red flag.
   
   
4. Accuracy - Does the data reflect reality? Accurate data ensures reliable decisions and prevents costly mistakes.
   
   
5. Timeliness - Is the data current and available when needed? Outdated data can mislead forecasts or operational planning.
   
   
6. Uniqueness - Are records duplicated? Unique identifiers maintain clarity and prevent errors.

The paper walks through each dimension with realistic examples that make it easy to see how these principles apply in any organisation.

Making Data Quality Real

Good data doesn’t happen by accident. The whitepaper offers practical guidance, including (but not limited to):

• Using dropdowns and calendar pop-ups to standardise data entry
  
  
• Applying null-value standards to handle missing data consistently
  
  
• Implementing both technical and process-based solutions for monitoring quality
  
  
• Learning how to safely enrich your data with third-party sources, while ensuring it’s trustworthy and fit for purpose

By combining these approaches, you can reduce errors, streamline decision-making, and save both time and money.

Transform Your Data into an Advantage

Whether you are a data analyst, a manager, or a decision-maker, this whitepaper is a guide to:

• Identifying the datasets that matter most
  
  
• Implementing organisation-wide standards for reliability
  
  
• Building confidence in your reports, dashboards, and insights
  
  
• Using enriched data to drive deeper, more meaningful understanding

Good data is complete, valid, consistent, accurate, timely, and unique. When you have it, you can make decisions faster, with confidence, and with measurable impact.

→ Download the “What Does Good Data Look Like?” whitepaper

You can also find out more about our data quality services here to see how Butterfly Data can help you implement best practices and maintain high-quality datasets.