In this final chapter, we shall finish our exploration of real data with Qlik Sense by moving beyond the standard structures of the office and showing the full possibilities of the software for analysis of almost any kind of imaginable data. We'll therefore be looking at applying Qlik Sense to demographic data. As before, this example and many others are available for you to explore at http://sense-demo.qlik.com.
This chapter will cover the aspects necessary for demographic data discovery, including:
- General information about common KPIs
- Examples showing how to use the lasso selection in maps and scatter charts
- Examples of dimensions and measures
With Qlik Sense, it is possible to analyze not only business data, but rather any data. One great example is demographic data—statistics of countries and regions on anything from age and gender to income and life expectancy.
Such data can be found on a number of Internet sites and downloaded for your convenience, for example, from the following websites:
- United Nations (data.un.org)
- Federal government of the United States (www.data.gov)
- European Union (ec.europa.eu/eurostat)
- Qlik DataMarket (www.qlik.com/us/explore/products/qlik-datamarket)
Demographic data is used and analyzed as-is by a number of nongovernmental organizations that need it for their activities. The common measures required are GDP per capita, population, unemployment rate, inflation, life expectancy, happiness, trade balance, labor cost, national debt, election results, and so on.
Often, interesting questions about correlations are asked; for example, how does happiness correlate with material standards and health? How are population growth and the number of children affected by factors such as life expectancy, poverty, and average salary? How has life expectancy improved over the years? If you haven't seen Hans Rosling's presentations on the Internet on this topic, we strongly recommend them. They show that data analysis is both important and fun.
Common dimensions in demographic data are country, region, gender, age group, ethnicity, and so on. An example can be seen in the following scatter chart, where you can see life expectancy and per capita GDP for different countries. Many developing countries are found in the lower-left quadrant, whereas the richer countries usually are found in the upper-right quadrant.
Life expectancy versus per capita GDP
You can clearly see that the two numbers are highly correlated—the higher the GDP, the higher the life expectancy.
These measures can often also be linked to your business data to enable a deeper understanding. For instance, you can divide your country sales by the population of the country, thereby getting a relative sales number, which tells you how well you sell in that country. With this number, you can make relevant comparisons of countries of different sizes.
Alternatively, if you assume that the market space in the country is roughly proportional to the GDP, you can divide your sales by the GDP and use this number to compare market penetration between countries.
These numbers will answer questions such as, "How well are we selling in this country, given the potential?"