Make Data Talk#3 Using qualitative and quantitative data for organisational insights

As a data storyteller, I often stumble across a range of misconceptions about what I do in my work - one of the most common is that I focus on purely quantitative information. But that's actually not the case (and in fact, my doctoral research was predominantly qualitative!). The reality is that both qualitative and quantitative data contribute significantly to the rich tapestry of insights we can draw upon in our workplaces, and we should be actively building our skills (and our organisational strategy and plan) in both areas.

Quantitative data (or numbers) includes the values, percentages, and figures that we see every day in our professional and personal spheres. Quantiative data can be broken down into two categories, as either discreet and continuous data. Discreet data (generally) involves whole numbers, such as the count of family members, or the number of products you have in a warehouse, while continuous data often involves decimal places, such as percentages or profit margins.

Perhaps contrary to some common assumptions, qualitative data is an incredibly valuable source of information, which is often under-utilised and/or overlooked in our organisations. Qualitative data can be categorical, with (again) two subtypes: ordinal and nominal. Ordinal qualitative data (the clue is in the name!) is categories that have an inherent order, such as survey that ask you 'to what extent do you agree with the following statements...' ('strongly agree' is higher than 'agree'). On the other hand, nominal data doesn't have this structured hierarchy, such as categories of favourite car brands, cuisines, or travel destinations.

Qualitative data extends beyond categories to also encompass open-ended narratives found in recordings, conversations, emails, observations, meeting minutes etc. Much of this is formally recorded and documented, but much of it isn't - and that doesn't mean it's NOT data. Just because we observe it, hear it, or see it, and don't write it down, doesn't mean we have to discount it... We just don't want to rely on it completely. Qualitative information is much more subjective than quantitative data, and it's important to remember that at all times.

A major challenge with qualitative data is managing vast amounts of this often unstructured, and open-ended data. To use this data well, we need to engage in a thematic analysis - where we look for themes, group the information into categories, and look for the prevalence of these themes throughout the data set. This is useful and can be incredibly insightful to do, but it's important to weigh up the cost-benefit of doing so, as it is time consuming and requires access to financial and human resources to complete the task.

Quantitative data, however, allows us to create relatively quick and easy visualisations, enables us to track and easily see trends, and is more objective than qualitative data. However, quantitative information often lacks details and explanation. This is where qualitative data comes in, as it provides depth and insights into the "why" behind numerical trends, and helps us understand the phenomenon more fully.

Regardless of the role that we're in and the industry we work in, we must embrace a balanced approach to qualitative and quantitative data, and harness the power of both. We want to supplement the more objective, hard facts, with the understanding of why the results are occurring as they are. In fact, through considering multiple measures of data across these types, we are tapping back into the idea of triangulation – where we integrate subjective observations and experiences with the more objective numerical insights. This approach ensures a holistic perspective, allowing us to identify patterns and make informed decisions, which are more likely to hit the mark.

The combination of qualitative and quantitative data unlocks profound insights into our customers, teams, and organisations, and helps us understand organisational dynamics. As long as we recognise the strengths and limitations of each type, we can tailor our data collection strategies to utilise a range of data sources that can help us derive meaningful insights.

As you reflect on the data landscape within your organisation, consider the balance of data types that aligns with your objectives and empowers informed decision-making. And ask... Are you really using the data that is best placed to help you?