Trends, rising or falling, form the core narrative of many data-driven stories trends. A good example of a site that uses trend data to tell stories is the Financial Times. Here are two recent trends it has highlighted to tell stories about low global interest rates and the burden of an aging population. On interest rates, the long-term trends on bond yields are not encouraging for those that want to see higher rates. The chart tells a story about a long-term shift over 30 years and how rates continue to fall as central banks try to grow the economy. The interesting question is what happens when interest rates hit zero. In Germany, Japan, and Switzerland we now have negative interest rates. We have probably all know that the population is aging.
The FT chart below tells a story about the potential economic impact of these changes by showing the number of people over the age of 65 as a percentage of the working population. Typically trend stories focus on how something is rising or falling over time. However, even a flattening trend can be a major story. One story has been how Twitter is failing to grow its active users. This story can be told through very clearly through the chart and headline below. Once you see a trend the obvious next question is why, why is it increasing or falling, or in Twitter’s case flattening. Thus the trend is not the whole story, it prompts further areas for investigation.
A common data-driven narrative is comparisons. For example, we can take a different angle on Twitter’s failure to grow its active users by comparing how it is performing relative to Facebook. This has been a story angle taken by a number of publications. Below is an example chart used to show how Facebook is outperforming Twitter. Comparisons and trends are used in extensively in telling political stories. For example, this image from the Huffington Post shows the the story of the current US political presidential campaign using opinion poll data.
3. Rank order or league tables:
Rank order or league tables are another common narrative suited to data. Here is an example from Forbes of the world’s most valuable brands. We have provided some content marketing examples below from our own BuzzSumo data. This first table below shows the sites with the most shares of articles about content marketing in the 12 months to February 2016. We could write a story on the top content marketing sites by using the data in this table. This second table shows the authors with the highest average shares of articles on content marketing. Brian Sutter‘s articles on Forbes have helped make him the author with the highest average shares. All credit though to Lindsay Kolowich for a really consistent level of shares for her content marketing articles on Hubspot.
Exploring relationships between data is a complex area, particularly when you want to see if one factor has a particular impact on other factors or can predict another factor. However, with advances in machine learning it is an area where we will see a lot more data driven stories. A simple approach to exploring relationships is to look at the correlation of two sets of data. It is important to remember that correlation is not the same as causation but it can highlight areas for further research. For example, we did a piece of research with Moz where we looked at the relationship between social shares and links.