There are two general types of data. Qualitative and Quantitative data. In order to understand the uses and importance of Qualitative Visualization Tools we have to understand the differences between these two types of data and the similarities if any.
Quantitative data is information about quantities; that is, information that can be measured and written down with numbers. Some examples of quantitative data are your height, your shoe size, and the length of your fingernails, your age, how many cars you have, how many kids you have, how many streets are in a city etc.
Qualitative data is information about qualities; information that cannot really actually be measured. Some examples of qualitative data are the color of your eyes, how happy you are, how happy the people in a city are or the general mindset of a population of people in an organization.
Quantitative vs. Qualitative Analysis
So how do you analyze these two different types of data? One of the most common and useful tools for analyzing quantitative data is Excel. While Excel is built for quantitative analysis, it’s not a substitute for other more complex software like Tableau when it comes to visualizing qualitative data. Therefore, it’s not the ideal application for analyzing clinical data. However, Excel needs to be considered for some of the advantages it offers over these other tools.
For example, a healthcare system, working with Health Catalyst, can load all of its data from various source systems into our enterprise data warehouse. An application created with these tools will answer specific questions that the healthcare organization asks over and over again; information that it needs every day or every month so it can see how things change over time. But when someone in the organization starts asking other exploratory questions that require ad-hoc analysis, this requires a different environment, usually an Excel pivot table. It’s easy to overtax some tools and ask them to perform analyses for which they aren’t designed. Some questions are better left up to Excel, which can be like a sandbox in terms of functionality for flexible, ad-hoc analysis. On its own, Excel has grown more powerful over the years in terms of what it can deliver for visualizing analytics. PowerPivot was a nice addition to Microsoft Excel 2010, which added a data modeling experience that is separate from the visualization experience. PowerPivot also leverages an in-memory tabular data model that can handle larger datasets. Microsoft Excel 2013 introduced PowerView, which is an entirely separate user window built into Excel for a crisper dashboard-like user experience. In 2014, Microsoft released Power BI which is a stand-alone visualization product completely outside of Excel with its own pricing, but which introduced several features that broaden the ability to share data views that are managed from a centralized location. Power BI also has a growing user base with a successful community input process that is driving feature enhancements.
It goes beyond data too. Qualitative and Quantitative also come at odds when it comes to research.
Qualitative Research is primarily exploratory research. It is used to gain an understanding of underlying reasons, opinions, and motivations. It provides insights into the problem or helps to develop ideas or hypotheses for potential quantitative research. Qualitative Research is also used to uncover trends in thought and opinions, and dive deeper into the problem. Qualitative data collection methods vary using unstructured or semi-structured techniques. Some common methods include focus groups (group discussions), individual interviews, and participation/observations. The sample size is typically small, and respondents are selected to fulfill a given quota.
Quantitative Research is used to quantify the problem by way of generating numerical data or data that can be transformed into usable statistics. It is used to quantify attitudes, opinions, behaviors, and other defined variables – and generalize results from a larger sample population. Quantitative Research uses measurable data to formulate facts and uncover patterns in research. Quantitative data collection methods are much more structured than Qualitative data collection methods. Quantitative data collection methods include various forms of surveys – online surveys, paper surveys, mobile surveys and kiosk surveys, face-to-face interviews, telephone interviews, longitudinal studies, website interceptors, online polls, and systematic observations.