In my previous article, I argued that with the right technology, CFOs could leverage the data they already have in the general ledger to provide hugely useful insights.
Now I'd like to look at two other data sets which also offer huge opportunities for generating important insights, far more valuable than the static reports that take such an inordinate time to create and tie up valuable human resources.
The first of these areas is sales. Sales teams are the beating heart of the business − they are where the strategies play out. The company's success or failure is ultimately the product of the sales team's effectiveness. It's obviously vital that the sales team − and the rest of the business − thoroughly understands what is driving sales and why.
Typically, sales reports are based on information from the ERP system. But these reports are superficial and backward looking, and do not provide any analysis of what is actually driving sales. In today's competitive global markets, what's really needed is a granular understanding of who the company's customers are, what products they are buying, where they are buying them and when, and at what price.
It's also extremely useful to understand what products are purchased at the same time; the so-called basket analysis.
To gather all of the information into one conventional sales report would ordinarily mean trawling through various systems, such as inventory management, HR, warehousing, point of sale and so on.
All of this would then have to be transferred manually onto one document, almost invariably a spreadsheet. It's all unbelievably time-consuming and also inaccurate, as re-inputting data manually is the front door for human error. It is also static, and difficult to explore, query and analyse.
As an aside, it's worth reminding oneself just how dangerous spreadsheets can be when used as analytical tools, something they are not designed to do. Elaborate formulas must be manually updated − something that can easily be missed, and spreadsheets that draw from other spreadsheets are particularly susceptible to errors that are hard to track down.
No business really has time to wait while somebody pulls all this information together and then inputs it into a spreadsheet.
The huge danger here, of course, is that wrong conclusions are drawn, with potentially unfortunate consequences.
What's needed is a way to draw on these multiple sources of data automatically, and then for the data to be transformed into use-friendly, interactive views that will assist managers in gaining insights, in order to make decisions.
Another prime consideration is speed. No business really has time to wait while somebody pulls all this information together and then inputs it into a spreadsheet − speed and ease are essential, and the information needs to be usefully presented, which really means interactively.
This means moving away from reports which only answer one question, to interactive dashboards that allow the user to ask questions suggested by the data.
With this kind of analysis, one can start to understand the nuances of customer behaviour and thus start to form opinions on how to increase demand. This kind of analysis can also show up inefficiencies.
The power of the connected view
What I've essentially been arguing is that while pure sales data can provide a useful historical view, when it's combined with data from other sources in an integrated way, it becomes much more useful.
The same principle holds good when looking at the debtors book. If you think about it, the debtors book is the barometer of the business's cash flow, and so of its sustainability. Keeping track of who has paid and who has not is obviously important, but it's really just the first step.
It would be highly desirable to be able to see how the debtors book has changed over time, as well as the payment history of each customer.
If a good customer starts to pay later, wouldn't it be better to see that trend and address it early on? Sorting debtors into "ageing buckets" manually is possible, but it is as inaccurate and time-consuming as other manual processes.
And, for planning purposes, any CFO would love to be able to predict, with a fair amount of accuracy, which customers will be in which bucket in the next month in order to improve cashflow planning.
As in the case of sales, it is possible to use technology to pull in information from other sources, combine it with the debtors book, and present it in customised analytic views and dashboards that truly support smart decision-making.
A long-time customer in the manufacturing space uses this holistic view of their data to manage their sales. Every day, they examine the historical sales data, up to the day before they are viewing it, at the lowest invoice-line level. This tells them which customer bought which product, when, where, how many and for how much.
When combined with their debtors book, the same application tells them not only who is buying their products, but how good those customers are at paying for the products they purchase. The sales managers can also see what products are being bought together in the same invoice (basket analysis), and very importantly, what products are not being bought.
The same approach allowed a client to identify that its cost of sales had risen sharply, and to identify the reason: the factory was inadvertently using a more expensive raw material in manufacturing the product. The ability to bring sales and production information into the same app or dashboard showed up a significant anomaly quickly, and in this case, it was easy to rectify.
Information − specifically 'connected information' − really is power.
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