In an office somewhere in the world…
Data Scientist: Yo accountant dude! Why do you spend so long producing your budget and forecast numbers?
Accountant: It’s how we performance manage the business.
DS: But dude, your work is always wrong, it’s always out… I mean, unless you have a crystal ball, you can’t get it right, right?
AC: It is not always wrong, it is 85% right, and comparing actual numbers to this number helps me tell the business where they are going wrong.
DS: Well, I’ve got exciting news dude… I can produce a forecast for you that is as accurate as your manual guessing game, and maybe more accurate, AND produce it in hours not weeks… I reckon that will accelerate your business conversations, right! <holds up hand for a high five>
AC: <ignores high five hand> I have heard about your magic, but I don’t trust it or understand your numbers. No trust, no comprehension, no use.
DS: But dude, if you are always wrong, why spend so much time on producing a number? And by the way, do you really think anyone in the business really understands your forecast numbers, not to mention that big fat red one in the variance column.
AC: When I present my numbers, there is a wonderful respectful silence that takes hold of the room, that tells me they are hanging on my every word and will rush back to their team and take immediate corrective action.
DS: <chuckles> Umm… OK. <Pauses and goes again> Now we are talking buddy! Actionable insight! So your forecast and variance analysis shows the business where they need to change path to stay on target…
AC: <sits back in awe of his own brilliance> Yes, you got it.
DS: <Raises eyebrows, breathes deeply> That’s so helpful dude… so tell me… why do you spend so long producing your budgets and forecasts that I can create in a day???
Is your mindset getting in the way of a better future? Part two
Firstly, apologies for the language as all data scientists are Californian born skateboarders in my head today and always. I know this not true, but it keeps it interesting on a cold January Day.
Secondly, the above conversation is NOT fiction, it is based on a conversation I had with one of my extended finance team not so long ago and it highlights the challenge on both sides of the finance innovation user/enabler journey.
Budgeting and forecasting are a necessary evil, we have no alternatives, but let’s take another look at their value, what needs forecasting regularly, and the effort we put into producing a number, that is by definition wrong!
Aggregrated historic data serves as a very good input to analytics models that can cut our production time, but we will only use these when we realise budgets and forecasts are JUST a guide and not some platinum standard number that requires slaving over.
If a Data Scientist came to you with this, how would you have responded?