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The Technologies Needed to Turn Big Data into Information You Can Use
brought to you by WBR Insights
Data is not the same thing as knowledge. Today, most field service organisations are able to generate and access vast troves of data. However, data alone doesn't help an organisation succeed unless it has the tools and the skills to interpret the data, recognise patterns, and convert it into actionable insights that can be used to improve operations, customer satisfaction, and business outcomes.
Excellent field service management has become an essential requirement for organisations that want to stay on the road to service delivery excellence. Indeed, flawless service delivery is no longer a unique selling point for companies, but a basic expectation of today's business customers. And that means field service managers need to be data-savvy themselves. They need to be able to create value from data, be au fait with at least the basics of data analytics, have the tools that enable the quick understanding of patterns in datasets, and understand how these patterns can be used to optimise operations and services.
What we're talking about here is having the ability to use big data to deliver field service excellence through predictive maintenance technologies and analytics. With the right tools, systems, and skills, field service organisations can forecast disruptions in the machines and equipment of the customers they serve and intervene before they cause delays and threats to productivity.
A number of technologies are required for organisations to begin their big data journey towards predictive field service. Let's consider what they are.
There's no point in gathering data unless the tools are there to track it. Data and analytics are both key to predictive field service. To begin with, organisations must be able to tap into their existing data surrounding scheduling, dispatch, fleet performance, logistics, parts fulfilment, success rates, customer satisfaction, and technician experience. It's no longer good enough for this information to exist offline - it needs to be on a computer, and ideally in the cloud. Only when the organisation is using technology to track these important analytics will it be in a position to start making use of big data for forward planning - such as scheduling for greater flexibility in anticipation of extreme weather or putting the right technician with the most relevant experience on the right job at the right time.
Internet of Things (IoT)
When it comes to predictive maintenance, however, even more data is needed - and this is where the Internet of Things (IoT) comes in. One of the biggest driving factors behind all current developments in the field service industry, IoT technology is where real-time, on-site data gathering begins. Sensors and chips embedded within on-site machinery and equipment connect to the internet, harvest real-time data about current conditions, and feed it back to a centralised system where it can be analysed for patterns, and alert management and technicians of pending faults or failures before they occur. The Internet of Things is instrumental in collecting vast troves of data and keeping all devices within the service organisation connected.
Artificial Intelligence (AI) and Machine Learning (ML)
With the organisation now embracing data gathering and analytics in all forms, it can now start to leverage the power of AI - more specifically machine learning - to create predictive maintenance models and optimise every aspect of field service delivery. By applying machine learning algorithms across the service chain, hidden patterns in data are unearthed that can be turned into action.
As Tom Craven, VP of Product Strategy at RRAMAC Connected Systems, recently told Design News, "When you gather the regular maintenance data, you can build a history of the data. Then you use algorithms to detect anomalous behaviour in the historical data. In time, you learn that when you see this anomaly, you know - based on the history - that this component is likely to fail in the next 10 to 17 days. The analysis of the data can predict the very specific failures in a specific timeframe."
Combined, analytics, the Internet of Things, and machine learning technology have the power to not only gather data but transform it into real knowledge that enables field service organisations to prevent failures, improve efficiencies, and deliver exceptional customer experiences. What's more, with these advanced technologies in place, big data can be utilised not only for predictive maintenance, but predictive job duration, predictive first-time fix, predictive parts management, predictive scheduling, and even predictive customer cancellation as well. The leading field service organisations of tomorrow will embrace analytics, IoT, machine learning, and many more technologies to continuously improve the customer experience and ultimately win in the broader business arena.
Big data is set to be a hot topic at Field Service Europe 2018, taking place this November at the NH Collection Amsterdam Grand Hotel Krasnapolsky, Amsterdam, Netherlands.
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