How to manage, and prosper, from the growing service management data challenge

How much of the data that flows through your business do you actually use? And perhaps more importantly, how effectively do your competitors interpret their data? This blog looks at how you can become more competitive in today’s data-driven world.

The data opportunity has never been more apparent; we generate more data in 10 minutes today, than all of humanity has ever created through to the year 2003.

Big data undoubtedly presents an enormous opportunity to learn, but it also brings an expanding nightmare for many operations teams as they find themselves drowning in information without the tools to effectively harvest that data.

Due to the nature of the work, service organisations handle a lot of information and this further inflates the problem. Traditionally this data has included customer data, performance statistics, route planning and job costings.

Furthermore, the arrival of advanced sensors, customised software and cheaper data storage solutions has seen the levels of data in service organisations grow at an unprecedented rate, confounding the data challenge yet further.

Why is change necessary?

It is no longer enough for organisations to simply understand and try to improve current processes. Inevitably, this approach relies on a failure occurring and then learning from that failure. During this period of learning, the downtime experienced is likely to damage both reputation and finances.

Instead, innovative businesses can challenge the culture of learning from mistakes and use their data to solve issues before they have a detrimental effect on their business.

By successfully questioning and deriving true value from their data these organisations will define structured strategies, justify investments, optimise the customer experience and ultimately, create a distinct advantage over their competitors.

We are living in a big data revolution and the organisations that embrace this effectively will expand the gap between themselves and their industry counterparts.

How to harness the big data challenge

An important factor in being able to achieve big data success is having knowledgeable and competent resources that can focus and learn from the most important data. Up until now, field service management software has successfully simplified the interpretation of data, presenting it in easy to digest analytical dashboards. But these traditional systems and approaches can be slow and inflexible and cannot handle the new volume and complexity of big data.

By pure definition, analytics is the discovery and communication of meaningful patterns in data. This is why we started exploring the capabilities of machine learning within our own field service management software. Our system naturally handles vast amounts of data but we knew we could do more with this data.

The results of our initial research is a system that enables customers to make better, smarter, real-time data-driven decisions that will change the way they handle their operations and compete in the marketplace.

Let me explain why:

Oneserve is now capable of using deep learning artificial intelligence to discover and highlight trends and patterns in the data that runs through the veins of the system.

For example, take 100 service maintenance jobs that have been generated through our service management software. A proportion of those jobs will be ‘good’, while the rest will be ‘bad’ – i.e. were not as efficient as they could be.

The reason for this bad job could be due to access issues, an undiagnosed issue, a lack of parts, etc. Oneserve now studies these bad jobs, establishes the probable cause of them and learns what is required to prevent the bad job from occurring again.

Once our system has learnt a pattern, when a new job is booked that presents the same characteristics, it will alert the user and suggest a resolution before the problem has occurred.

Of course one of the greatest generators of data in today’s world is the Internet of Things and the sensors that sit alongside it. Oneserve can also use the data generated by sensors to predict a failure, whether that is a temperature sensor sitting in a boiler or a vibration sensor located in a sheet metal production line.

By monitoring each sensor, Oneserve not only raises an alert if a sensor reports issues, the system also automatically raises a job so that an engineer visits the site and resolves the issue before any downtime is incurred.

For our customers they can finally fully embrace the data available to them and use it to completely change the way they work. They no longer need to rely on the costly, reactive business model, instead introducing a far more sophisticated and cost effective predictive method of delivering their service.

Data is a precious commodity. We recognise this, which is why we are reshaping the way the field service management industry utilises big data. If you would like to lead the way in your industry and explore how AI and machine learning can help propel your organisation, get in touch.