As 1 April 2018 rushes towards us, most Kiwi businesses are well advanced in their planning. How can predictive analytics increase your confidence that your forecasts will be right?
Business forecasting is fraught with difficulty. Even the smartest business people get it spectacularly wrong. According to an article in Forbes Magazine, former Microsoft CEO Steve Balmer was quoted in 2007 as saying, “Theirs is no chance that iPhone is going to get any significant market share.” Less well known is Steve Chen, CTO and co-founder of YouTube who reportedly voiced concerns about his company’s future in 2005, saying “There’s just not that many videos I want to watch.”
Surely there is less excuse for getting planning wrong, whether it’s sales, operations or finances, given the huge volume of data available to us today?
Google’s Chief Economist Hal Varian neatly characterises this avalanche of data: "Between the dawn of civilization and 2003, we only created five exabytes; now we're creating that amount every two days. By 2020, that figure is predicted to sit at 53 zettabytes (53 trillion gigabytes) -- an increase of 50 times."
We are literally being buried in all sorts of information. But the great benefit to most business is not in the quantity of data being generated from all sorts of sources, but what meaning can be extracted from these mountains of information. Meaning that can be used to improve forecasting.
Many organisations are trying to harness the power of this information, moving manual, spreadsheet-based forecasting to more real-time, integrated approaches. Cloud-based tools that connect directly with data sources and then apply analytics can efficiently produce all kinds of insights and data points.
The danger for businesses is that the ease of collecting and collating information can obscure the need for building a solid foundation of statistical analysis. Without a solid, agreed foundation, confidence in forecasts can erode.
Clever dashboards and cool looking reports can’t make up for the underlying numbers not being solid, or solidly understood. As our partners at Vanguard Software, experts in this area, put it, ‘’The foundation of forecast accuracy, sales and financial included, is that your baseline assumptions come from statistical analysis of the past (plus anything you know to be different about the future). Anything short of this first layer of statistical analysis will produce less accurate results.”
While many modern ERPs or business intelligence and reporting tools have forecasting tools built into them, enabling beautiful models to be created, the fundamental flaw can be that the base data is not statistically valid.
Applying the right predictive analytic tools to historical data to derive valid patterns and relationships puts you on a more solid footing. And ultimately provides more confidence in those planning forecasts you are about to make.
For a deeper discussion of this issue, read our white paper: Are you sure about that: how to increase confidence in the accuracy of your sales and financial forecasting.