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Not too long ago the buzz in Kenyan media training circles was all about data journalism. But in Kenya most things are seasonal.

It is as if the buzz about data journalism has passed on going by the kind of reporting currently being witnessed.

Yet the events of the recent days cry for expertise in data journalism. How can reporters mine meaning out of the numbers that news sources are churning out every day?

Every afternoon, Afya House releases the latest information on the Covid-19 case load in the country.

These numbers are raw and do not, on their own, tell us much. Take, for example, the numbers referring to the last random five days: 189 positive cases were confirmed on July 14, the previous day the cases were 379, while on July 12 the cases were 278. A day earlier there were 473 positive cases reported.

But what do these numbers mean? Journalism is the art of story telling and good journalists tell stories well.

To get to the heart of the story of these numbers the journalist must go behind the numbers.

For example, how many cases were tested out of which the numbers reported were derived?

But even that does not tell the ordinary person the full story because 100 cases out of 10,000 tests is a different story compared to, say, 100 cases out of 100 cases.

But even that is not yet a full story until we know where the samples came from.

A 100 per cent positive return where the sample was drawn, for example, from Mbagathi hospital is different from a one per cent return where the cases were taken from across the country through random sampling.

To get the full story, then a story teller would need to dig deeper into where the samples were drawn from, how they were collected, the rigour of the exercise, and how these compare across the board through some standard measure such as percentages.

It is this in-depth digging that will tell the reader whether the 189 case load story is worse or better than the 473 story and whether overall the country should be worried or not. Data makes sense when it is placed in context.

Take a different example. Over the last several weeks now there has been an outcry regarding the number of young girls getting pregnant.

But this story has been told in a fairly simplistic way. It is hard to find a journalist who will tell you how many girls are currently pregnant across the country or in any one specific region.

Even then in order to make better sense from the story one would need to understand how these numbers compare with the previous year and over the years and also over different regions.

Are there more young girls getting pregnant this year compared to last year? If so, by what percentage?

How do the ages of the girls who are getting pregnant this year compare to those who got pregnant last year and the previous years?

Is there a socio-economic category that is in greater danger than others? For example, are more girls from the middle class getting pregnant compared to those from the lower or even the upper class?

Even then the absolute numbers alone would not tell us much. If 2,000 girls, for example, from the middle class got pregnant, and yet 20,000 from the lower middle class got pregnant; does that imply that there are more lower class girls getting pregnant? It would be hard to tell until the story teller gets deeper into the story.

We can apply this to many other number stories, for example, crime, gender violence, and so on.

Over the previous years a lot of energy and resources were spent in “training” scribes on data journalism but the results are not showing up in their stories.

Habits are hard to change and may be that is where we need to target in order to get more stories from these numbers. — The writer is dean, School of Communication, Daystar University