In my previous two posts (here and here), I talked about how the use of data enhances our ability to understand culture. In this post, I’d like to expand on that a bit further and provide some real world context.
gothamCulture is passionate about safety; in fact, we recently worked with several clients to address safety concerns within their organizations. There is evidence to suggest that workplace safety is not only essential to maintaining the health and wellbeing of employees but also can improve a business’s bottom line. For organizations with safety concerns, addressing these challenges often necessitates a change to the underlying culture.
In our work with clients, data often takes the form of text-based inputs from interviews, focus groups, and site observations. While text-based data provides a wealth of information, it can be challenging to extract the most important pieces. One widely used method is text mining, which can be used to identify major themes among the interviews. In the example text cloud above we used text mining to look at overall morale. A couple key words jump out such as “antagonistic”, “complacent”, “change”, and “unsafe”. This is supported by key ngrams such as “staff extremely difficult”, “tough change culture”, and “question unsafe bad”. These data points seem to suggest that while change is needed to improve overall safety there are underlying tensions within the organization that make it difficult to discuss and implement improved safety measures.
This data is useful in understanding broad issues and challenges in organizations; however, it does not show connections and correlations which are helpful in determining strategies best suited to address the issue. Correlations are a product of quantitative (numeric) data, to identify correlations we transform our text-based data into quantitative data. While there are a number of methods being pioneered, a simple method we have leveraged is using text clouds to identify themes and then determine which interviews, focus groups, and site observations include those themes. Interestingly, this method produces fairly reliable results.
The network diagram above shows a number of correlations that exist across the data. The size of the circle relates to the number of correlations the “theme” has, the size of the line relates to the strength of the correlation, and the color relates to different categories of themes (blue=training, green=morale/culture, yellow=safety). Here we get a better idea of the different dynamics within the organization. For instance, while there is a connection between training and safety, the elements connecting those two themes are a hierarchical culture and poor morale. In this case, it is not enough to update policies or develop new training opportunities, the organization must also address its hierarchical elements which seem to be linked to poor morale, inadequate communications, and a sense that the organization is uncaring.
Organizations are a lot more like ecosystems than they are machines. Addressing challenges (whether safety, mergers, or customer relations) requires a lot more than turning a wrench or drawing a schematic; it involves understanding relationships between the values, personalities, and perspectives that exist across the organization. Traditionally, most people have felt that data analysis is a little out of place when looking at culture, but, as we’ve shown, it is an effective tool that can save time and reveal compelling insights.
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