How do you deal with data quality issues or gaps in your community consultation data?

Clear definition of data needs and standards is crucial before commencing data collection for a community consultation project. Determine project objectives, scope, key questions, and indicators for measurement and analysis. Identify sources and methods of data collection, setting criteria and benchmarks for data quality. Aligning data collection with project goals ensures accuracy and mitigates potential issues or gaps in data quality.

You will need to have a data quality process to ensure that you are actually collecting data, collecting quality data, and process for analyzing the day. The simplest place to start is by asking the question: WHY DO I NEED THIS DATA? What is the purpose? Then ask HOW WILL I USE THIS DATA? Next you want to consider how will I MEASURE THIS DATA. The type of date needed to be collected and the means for measurement should be determined by the goals and outcomes of the organization. I like to approach data inquiries and analysis from a qualitative perspective as I am looking for gaps in what has been shared in comparison to similar demographics/psychographics previously collected from previous consultations.

Start by clearly defining your data needs and standards. Know exactly what you need to collect and why. Establish criteria for data quality from the get-go. This will help you avoid collecting irrelevant or low-quality data and ensure that your data collection aligns with your project goals.

Select tools and techniques that align with your target population and data needs. Use surveys for broad reach, interviews for detailed insights, and focus groups for diverse perspectives. Pilot these tools to identify and correct issues. In a recent project, I combined online surveys and face-to-face interviews, piloting the survey to refine questions, ensuring we collected high-quality and relevant data.

Implementing quality control measures and checks is vital throughout the data collection and analysis process. Utilize tools such as data entry forms, quality software, audit trails, cross-checks, and triangulation to detect and correct errors and inconsistencies. Document and report any issues encountered, along with corrective actions taken, ensuring transparency and accountability. This fosters trust in the reliability and accuracy of your data.

I recommend using data quality software that is separate from the main CRM. One of my favorites to use is Oracle. Depending on the organizations needs a different software may be needed.

When looking for data quality issues and or looking for gaps, I like using data visualization tools. Tools that I recommend for this is Tableau or Power BI. In addition, the utilization of heatmaps can be used to identify inconsistent or missing data.

Use imputation techniques like mean substitution or regression to estimate missing data and weighting methods to correct biases. Always disclose limitations. In a recent project, we used multiple imputation for missing survey data and weighting to ensure sample representation, minimizing the impact of missing data.

Gaining user feedback and feedback from stakeholders is important to get a perspective on information that may support growth for the organization and or assist with providing support for the customer. A simple way to accomplish this is through feedback forms. We would want to them to provide perspective on what went well and what went wrong. Also, this gives them an opportunity to recommend suggestions.

Embrace data quality issues as learning opportunities to enhance community consultation practices. Review, evaluate, and identify causes, consequences, and solutions for gaps. Document and share lessons learned and best practices, applying them to future projects. Continuous improvement based on insights gained from data quality challenges strengthens both data quality and effectiveness of community consultation efforts.