In the past, holding a dominant market position was synonymous with success, and high margins and weak operational efficiency went along with it.
Today globalisation, technology and the multiplication of supply mean the difference is driven by high-performing business processes. The most common way of bringing them up to that standard is to tie them to smarter business decisions — in other words, to use analytics applied to the business.
Using analytics is not in itself a strategy, but using it to strengthen a business capability certainly is. So how do we strengthen a business capability strategically with analytics?
By selecting the business capabilities that differentiate us from our competition.
By applying extensive statistical and quantitative analysis to those business capabilities, so that decision processes are structured on real facts.
By automating the decisions tied to those business capabilities through dashboards.
With that said, let us go over a few concepts; we have written extensively about analytics on this blog. Its most basic definition is: the use of mathematics in analytical processes in order to make optimal decisions.
In theory we all know analytics; in practice few of us exploit its full potential. It is logical to think that intelligent decisions are made with information — so consider the following:
How many decisions do you make each day without current data to support them?
Are the indicators you use to manage formulated optimally?
Do the business premises you use to combine one area's variables rest on enough data?
Is all the data you handle day to day 100% reliable?
Do you use technology — beyond a spreadsheet and a laptop — to make decisions?
Surprisingly, most people have serious weaknesses in the reasoning behind these five basic questions, and yet are evaluating predictive models and artificial intelligence for some business processes. You cannot run before you can walk — but “walking”, where analytics is concerned, is simpler than it looks.
The first layer of analytics is the descriptive layer, or Business Intelligence. (See: Business Intelligence or Big Data?) In this layer business decisions are modelled and automated through dashboards, and each area's data marts (cubes) are created for analysts to exploit.
Without developing this layer properly, going straight to the predictive layer is inefficient — let alone to the prescriptive layer or AI.
We must not lose sight of the objective: using analytics as the differentiating element for our distinctive business capabilities. When we take on analytics projects, the recommendation is that they be led from the business areas and not from the technology area (technology is not an end in itself: it is an enabler).
