Documenting consultation activities and outcomes is vital to effectively managing the engagement process. The overall goal of consultation data management is to:

  • Find the right data to answer basic, yet important questions such as: Who has been consulted with about a particular issue? Where and when such meeting took place? What were the results?

  • Discover important patterns in stakeholder feedback to identify risks, and inform decisions

  • Demonstrate compliance to the regulatory or lending requirements, and how the company incorporates community feedback in the project outcomes

  • Provide accurate and reliable records of consultation for dispute resolution.

Below are the steps we usually work through with clients to help them understand how to organise their stakeholder engagement data, perform and write up data analyses.

Step 1 - Preparation

Basic questions to go through at this stage are:

  • Do you have existing data?

  • What format is it in?

  • Does it need to be ‘cleaned’ for duplicates, messy records, etc?

  • What value would this existing data hold for your future processes? Sometimes old data is just too messy and would require more resources than the value of the data justifies

  • Do you have any established reporting and evaluation requirements?

Step 2 - Design

Key questions asked at this stage:

  • What are the crucial pieces of stakeholder information that you need to capture for analysis and reporting? For example, postcode for reporting by geographic area

  • Organise your stakeholders so it will be easy for you to distribute targeted information and filter data for reporting – what filters and groupings will assist this?

  • What methods and processes will you use for interacting with your stakeholders?

  • What filters needed for you to create reports that meet the requirements identified in Step 1?

  • Are there ways you can automate the data collection?

  • How are you going to process media, social media and other external data?

Step 3 - Qualitative data

Qualitative data is text or narrative from open ended questions on surveys, meeting notes, submissions, interviews, media articles, etc. Two important things to consider are:

  1. What questions do you want to answer from the rich-text data?

  2. What are the needs of those who will use the information?

Qualitative analysis provides a rich understanding of stakeholder views on the project. Qualitative analysis help answer some questions such as:

  • What are the key stakeholder issues, concerns, suggestions and aspirations ( referred to as "themes")?

  • Are there any relationships between themes (eg: A few themes might appear together consistently in your data. Exploring these connections might help explain the “why” in your data)?

  • How to drill down to specific comments on a theme, for example, what do dirrectly affected people actually say about the project impacts on their way of life?

To present your findings:

  • Develop an outline for presenting results - Look for quotes or descriptive examples and visual representations of your data to help explain the data and your findings.

  • Don't forget to address the limitations of your methodology and results – look for what is missing (eg, missing voices from the vulnerable segments of the affected community).

Process and methods for data collection:

  • Are there ways you can minimise the amount of effort you need to put in to collect the qualitative data and yet still get the information in as ‘raw’ format, in stakeholders’ own words?

 Step 4 - Process

  • Structure your database design to support the methods of engagement, the timing of your process, and the required outputs. Ensure that categories match what your process will entail so you can get rich detail in your analysis.

  • Integrate all sources in your analysis and reporting – does your stakeholder engagement solution allow for integration of data from all sources such as surveys, submissions, media, etc? If not, what measures will you put in place to ensure that your reports present an accurate and comprehensive picture of stakeholder views?

Step 5 - Protocols and Maintenance

A quality database means quality output and meaningful reports.

  • Develop data management protocols that set rules around how to use the database, what information should be captured, how to ensure consistency in the data capture.

  • Train your users to ensure consistency in data input and analysis.

  • Perform data maintenance as regularly as you can, depending on nature of your engagement process (ie, a data maintenance should be undertaken before a big reporting period or before the next consultation stage takes place).

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