Pulling it all together

In some instances, a single indicator will provide the right level of insight for a company to be in a position to evaluate whether a core principle has been achieved. In other instances, several data points will need to be reviewed simultaneously in order to provide an overall picture of how the company is tracking against a core principle.

For example:

  • Reviewing the type of engagement technique/tools used, the number of stakeholders who have engaged with each technique/tool and the stakeholder attitude toward each techniques/tools, provides an understanding as to whether the techniques/tools used are appropriate for the stakeholder audience (including disadvantaged or vulnerable groups). Attendance rate coupled with attitude will generally give an indication that the technique/tools are appropriate for the local context.
  • Several KPIs are informed by stakeholder attitude or trust. While a positive trend in stakeholder attitude or trust may correlate to a more positive experience for a stakeholder, it may not necessarily mean that core principles have been met, and meaningful engagement achieved. Furthermore, a stakeholder may hold a positive attitude toward the company but could still be in opposition to a project/activity and therefore the attitude of a stakeholder should not be considered in isolation. Accordingly, other KPIs will need to be used in tandem to provide an accurate reflection of reality.
LightbulbTip: There are a range of digital tools that can be used to assist practitioners and companies to undertake data analysis. When selecting the right data analytics tool, it is important to consider the nature of the KPIs selected, the data modelling capabilities of the tool, and the resources available (i.e. affordability and licensing arrangements).

Data analysis

When analysing data obtained via data collection methods it is important that an appropriate analysis is conducted to inform the KPIs selected by a company. This analysis may use the following techniques:

  • Frequency analysis: used for quantitative data and intended to identify the number of times answers are provided. The data analysed in this manner is usually presented in graphical formats to aid in readability. The data will present common issues, concerns, and perceptions amongst respondents, and can be used to inform changes in community perceptions overtime.
  • Content analysis: used to determine the presence and frequency of certain words, themes and concepts in qualitative datasets (e.g. open-ended questions in a community survey). Utilising content analysis will enable the qualitative data received to be viewed through the lens of quantitative analysis. This often involves the use of ‘coding units’, which allow for meaningful identification of trends for stakeholders and/or stakeholder groups.

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