At the beginning of every year, subject-matter experts typically gaze into their crystal balls to predict the year ahead before blogging about their prophecies for us all to enjoy. We learn what may happen, what may not happen, and sadly, what eventually doesn’t happen despite high confidence that it will. But that’s forecasting for you: it’s never completely accurate.
As the first quarter of 2013 came to a close another set of predictions for the Field Service Industry in 2013 appeared, this time from Trimble’s Mark Forrest who has given his thoughts regarding the year ahead. But there’s nothing new from the headlines: “Good customer service drives profitability”; “Safety is a key priority”; “Technology will streamline fuel costs”; “Technicians will take on a lead role”; and “The importance of the Cloud”. All of these have been clearly emerging trends over the past year-or-more.
This isn’t saying that Mark is wrong – in fact much of what he has predicted is agreed upon – it’s just business-as-usual. That is until the reader of his article appreciates the final prediction: “The key is in the information, not data” which, when you digest the accompanying details, is actually a statement of facts rather than a prediction of the future field service business. And upon first reading this, there is just one natural reaction and comment: with raised eyebrows we question “Seriously?” This so-called prediction has been mooted for many years and it is still failing to materialize so maybe 2013 is the year, right?
Unfortunately, it’s unlikely. Service businesses receive an overload of data, much of which cannot be readily processed into useful information. Even business intelligence tools – such as ClickSoftware’s ClickAnalyze – help with understanding the data by presenting pretty graphs and alerts but there is still something missing: intelligence. Presenting data as information using an eye-catching user interface is not intelligent and it can easily miss important points: if the user-interface isn’t reporting on something then how does the business know about a potential issue? The answer is simple: this remains a manual and time-consuming task for the Business Analyst.
This prediction is only likely to come true when the underlying software starts utilizing artificial intelligence thus turning the data and reporting into something meaningful. In this scenario, the company’s Business Analysts no longer have to interpret the data because the software is providing direction and meaning. It becomes a decision-support tool rather than a huge – and ever-growing – data repository. There’s no need to process the information because the software is telling what’s wrong and what to do. Artificial intelligence – powered by algorithms – is used frequently within optimized scheduling and routing, service forecasting and capacity planning, and within the supply chain where inventory levels are planned so why not start applying this logic to analytics? Don’t give me numbers: tell me what to do!
In the days of modern, intelligent, optimization software, surely genuinely intelligent business intelligence is imminent?