Wed 29 / 07 / 15
Management Information goes to Hollywood (Part 1)
Thom Naylor, Managing Director of Excellent Solutions, discusses the importance of trusting your audience.
So there we were last month, reminding ourselves that we should always keep asking the questions of our data; why, what, how and when? It got me to thinking of a hypothetical question and answer game that we might all have played at one time or another – “If Hollywood were to make a movie about my life, which actor/actress would play ME?” At this point I will pivot (rather tenuously, I admit) to a different Hollywood themed question – “If Hollywood were making a movie about Management Information, what famous one-liners could they regurgitate to bring some glamour to a, seemingly, mundane topic?” Therein lies the challenge for my next couple of blog entries.
So let’s start with ‘A Few Good Men’ when Tom Cruise and Jack Nicholson’s courtroom altercation explodes; “You want answers?” / “I want the truth!” / “You can’t handle the truth!” – this is so often the case with MI. I remember, many, many, many years ago, being asked to provide a graphical illustration of sales performance for a full year. I dutifully trotted out a graph that showed sales per month with peaks and troughs – fairly reasonable for a ‘wet behind the ears’, MI Manager. I was immediately told to take it back and change it into a cumulative graph plotted by month. The justification for this change was that the continuous upwards trend of a cumulative graph would present a positive message to the management team…….. WHAAAAAAT!!! My current self often screams at my former self for agreeing to such a request and turning a mildly informative piece of MI into a bland piece of positive PR for a sales team.
This story serves to highlight the ongoing struggle between MI that is explanatory and MI that is exploratory and my contribution to this debate is as follows.
Explanatory MI sets out with an agenda – quite often the requestor knows what story they want to tell, they just need some “facts” to support their assertions. Whilst there is probably a time and place for this kind of MI, I am not a big fan of it in the business world. My general experience is that explanatory MI is often used to “mask” bad news and just leads to further, exacerbated, sub-optimal decisions by the business.
In neo-classical economics there is a theory that goes something like this; if an optimal allocation of scarce resources is to occur then certain conditions are required, one of which is ‘perfect information’. The economists amongst you might have to forgive my blunt interpretation of that theory but my economics studies were about 20 years ago and, given that I was a university student at the time, I was possibly a little bit tipsy during that lecture. Anyway, I apply a similar kind of thinking to MI within a business;
1. With perfect information (exploratory MI) a business can make optimal decisions.
2. With no information a business can make sub-optimal decisions.
3. With imperfect information (explanatory MI) a business can end up making catastrophic decisions as they are based upon a biased perception of the data landscape around them.
So, my thought for the day is ‘trust your audience’. Trust that they can ‘handle the truth’ and set forth that truth in an open, unbiased manner allowing them to make optimal business decisions based upon perfect information. The only caveat that I would add is that ‘perfect information’ in economics is also often referred to as ‘full and relevant information’ – a definition that I much prefer. So, next month I will consider (through the lens of my Hollywood movie about Management information) what is the MI view of full and relevant information.
And for those of you who are still thinking about my opening gambit…..yep, I used to think that my role would be played by Tom Cruise but nowadays I am thinking that Bob Hoskins might be a better fit.
For more information please visit the Excellent Solutions website.
You can also catch up on Thom's last blog, Children make the greatest data analysts.
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