Introduction

Investing in tools and technologies that facilitate data cleansing and validation processes is essential for maintaining high data quality standards across various datasets.

Last week, we discussed how presenting users with a predefined list of options might appear as a simple solution to data quality issues. However, working with drop-down lists poses unique challenges that make them unsuitable for many situations. In this article, we’ll explore when and how to use drop-down lists effectively to improve data quality.

When to Use Drop-Down Lists for Data Quality

To ensure drop-down lists enhance data quality, they should meet two key criteria: size and completeness.

1. Size: Keep the list relatively small to avoid performance issues. Rendering large lists can lead to impractical delays, causing users to input incorrect values out of impatience.
2. Completeness: The list must be finite and provide valid options only. Incomplete lists may lead to users selecting invalid options inadvertently.

Complexities in Setting Up Valid Choices

Designing a seemingly simple drop-down list, such as capturing a customer’s gender indicator, can be surprisingly intricate. Consider the following scenarios:

• Non-Person Entities: If the customer is not an individual but a company or trust, additional options like “Not Applicable” might be required.
• Unknown or Refused to Disclose: Differentiate between “Unknown” and “Refused to Disclose” to avoid potential gender discrimination issues.
• Diverse Designations: Should the list cater to various gender designations, including gay, lesbian, and transgender? Decisions about such inclusions should be carefully considered.

Now imagine the complexity of managing a more complex list, for example for street names, or customer types.

Governing Drop-Down Lists for Consistency

Drop-down lists are a form of reference data and require governance to ensure they serve their purpose consistently across systems.

Anyone that has set up a statistical survey will know that reducing the number of choices gives us more easily measurable results (grouping results into meaningful groups or dimensions). However, leaving valid options out will skew results by forcing users to respond with a less accurate answer.

Follow these best practices:

1. Business-Led Setup: The creation of reference lists should be driven by the business subject matter experts rather than solely relying on technical resources. This is critical to ensure that reference lists maintain integrity over time.
2. Consider Data Usage: Define your lists with a focus on how data will be used both for reporting and operational purposes. A well-crafted list leads to more accurate and easily measurable results.

How to ensure quality, integrity and consistency across diverse sources and systems: Learn strategies for ensuring quality, integrity, and consistency across diverse data sources and systems to enable effective decision-making.

The importance of data standards: Discover seven insights highlighting the significance of data standards in maintaining data integrity and consistency.

Conclusion

In conclusion, drop-down lists can be valuable tools for improving data quality, but their implementation requires careful consideration. Keep the list size manageable, ensure completeness, and manage complexities when designing valid choices. Moreover, governance and involvement of the business are vital to maintaining consistency and achieving reliable data outcomes.