In which is stage investigating and analying the dataset is involved in order to collect insights and understanding the data
Answers
Data by itself, consisting of bits and bytes stored in a file on a computer hard drive, is invisible. In order to be able to see and make any sense of data, we need to visualize it. In this chapter I’m going to use a broader understanding of the term visualizing, that includes even pure textual representations of data. For instance, just loading a dataset into a spreadsheet software can be considered as data visualization. The invisible data suddenly turns into a visible ‘picture’ on our screen. Thus, the questions should not be whether journalists need to visualize data or not, but which kind of visualization may be the most useful in which situation.
In other words: when does it makes sense to go beyond the table visualization? The short answer is: almost always. Tables alone are definitely not sufficient to give us an overview of a dataset. And tables alone don’t allow us to immediately identify patterns within the data. The most common example here are geographical patterns which can only be observed after visualizing data on a map. But there are also other kinds of patterns which we will see later in this chapter.
Using visualization to Discover Insights
It is unrealistic to expect that data visualization tools and techniques will unleash a barrage of ready-made stories from datasets. There are no rules, no ‘protocol’ that will guarantee us a story. Instead, I think it makes more sense to look for ‘insights’, which can be artfully woven into stories in the hands of a good journalist.
Every new visualization is likely to give us some insights into our data. Some of those insights might be already known (but perhaps not yet proven) while other insights might be completely new or even surprising to us. Some new insights might mean the beginning of a story, while others could just be the result of errors in the data, which are most likely to be found by visualizing the data.
Tables are very powerful when you are dealing with a relatively small number of data points. They show labels and amounts in the most structured and organized fashion and reveal their full potential when combined with the ability to sort and filter the data. Additionally, Edward Tufte suggested including small chart pieces within table columns, for instance one bar per row or a small line chart (since then also known as a sparkline). But still, as mentioned in the introduction, tables clearly have their limitations. They are great to show you one-dimensional outliers like the top 10, but they are poor when it comes to comparing multiple dimensions at the same time (for instance population per country over time).
Answer:
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Explanation: