Can biased reporting ever be good?
Yes, when you want to generate curiosity and engagement!
In our business lives we are not always reporting – sometimes we are advising, sometimes instructing, and sometimes simply keeping communication channels open. When I am reporting and advising it is obviously necessary to remain unbiased, but when I am undertaking education initially my primary goal is to increase curiosity sufficiently to create engagement – the point at which my audience is prompted into finding out more.
I have written many posts about how detrimental it can be when we add our own ‘rose-coloured glasses’ for reporting and making decisions.
The context of these posts I was referring to ‘reporting’ as providing information with the view to driving decisions and actions; distributing the message this week or month’s data is relaying.
But what about when we are simply wanting to generate curiosity, to engage the audience and get them to listen?
Human minds don’t store independent pieces of data; brains connect sensory fragments to re-assemble when we remember something. So, the quickest way to create a memory and develop curiosity in most people is visually telling a story.
Data visualisation, which includes graphing, mapping, hierarchies, schematics etc and is undertaken to either:
- help make a decision, or conduct an investigation – analytical visualisation
- educate and make the listener curious – information graphics
Analytical visualisation is advisable for all the regular ongoing reports that circulate within your organisation through defined reporting channels – here there should never be any element of biased reporting.
On the other hand information graphics result from filtering sets of variables and abstracting them into some schematic form – reporting with bias. The primary goal of an information graphic is to prompt some more investigation – it doesn’t aim to fully inform, it simply aims to stimulate some curiosity, hence the bias is introduced as a way of cutting through to fuel imaginations.
This graphic on death is a good example:
When we are telling a story, loads of data does not necessarily make a better picture – not everyone needs to see every value; nor will using all the data will have a bigger impact. How often do we show all the data just because it is available? – being hijacked by a sudden need to look ‘smart’, ‘hard working’, or ‘thorough’, muddles the original purpose.
Few of us would think to list and describe the inter-relationships of 767 numbers in a piece of text, but it is easily possible as a graph. However, ask yourself “Will doing so really serves the purpose of getting this point across?”
Is your favourite colour in the infographic below? (can you find it)
Be clear in purpose
If we want to share rather than analyse, the difference is the same as between story-telling and reporting. Story-telling benefits from grouping and averaging, the final picture often tells a clearer and specific story.
I know the objection to ‘smoothing’ is always – loss of detail creates bias!
HOWEVER consider this: Should a story be objective?
(Can a story ever be objective given it is always interpreted, edited, embellished and delivered by the storyteller).
If, by smoothing or removing detail, you create biased reporting in the end will the story prompts the audience to dig deeper, find out more, get involved, take an action> If so, then surely the purpose of building engagement has been achieved.
Think of a weather report – at the end of the day I really don’t care about all the data, formulas and algorithms applied; to be honest I’m not too fussed if it is raining in Dakar (although curiously I always listen to that bit) – I really want to know if I will need my umbrella! All I look out for are the Rain Icons over Melbourne
By excluding a significant amount of data this alternative infographic shows more clearly bluey colours are favoured by adults!
Clearly some detail was lost in the second picture and therefore some would say a bias was introduced to the report, but the overall story is immediately engaging because of the reporting with bias – seeing this graphic first would make me want to look at the more detailed picture, but when we start the other way around my curiosity is less quickly piqued.
Where do you need to add some bias?