Data shows that data-driven organizations perform better. But what does it take to get there?

Nov 12, 2019 • Features • Data • management • Digital Transformation

Data visibility and automation are key to transforming a service organisation. Increasingly, Field Service operations are looking to better data, in taking advantage of technology transformation and servitisation. In the third article of this series, Paul Smedley, in his latest article in a series for Field Service News, looks at many practical examples of successful change...

Data-driven organizations perform better. An Economist survey in 2012 demonstrates how organisations that rely on data outperform competitors financially. 31% of those whose use of data was substantially ahead of peers were also substantially ahead on financial performance. Put another way, 63% of those ahead financially were ahead on data usage.

Why does this matter for Field Service?

Two trends make this critically important. Digital transformation provides data in ways that was impossible, even unforeseen, not many years ago. Machines can send us information about usage patterns or issues that need resolving (the ‘internet of things’).
Field technicians can track everything they do and access all kinds of information, from smart devices, mobile data and digital media. Secondly, this plays a far more significant role, when we provide solutions as services not just products (servitisation). Data is key to service delivery – and also to developing markets for the new revenue streams.

Joined-up data means we can literally see issues and change business decision-making processes or business priorities. For instance, we can understand the costs of providing a certain type or level of service. We can also see what happens if we don’t have the right skill or equipment because our data is out of date - or if we don’t identify issues that could cause future problems. More than that, good use of technology can now increasingly automate routine, high-volume tasks – including joining up legacy systems or tracking workflow.

While data-driven decisions are no all new, they are now quite different in scale and scope. Collecting data and performing analytics used to be prohibitively expensive, reserved for high-end businesses or programmes. Furthermore, this was ‘after the fact’, looking back to come up with explanations and solutions, often for internal management purposes.

Today, (as the Gartner graphic shows), we don’t just look at what happened and why it happened. We analyse what will happen (predictive) and take automated action (‘prescriptive’). Furthermore, this goes beyond internal management usage, with direct impact on customer experience and business development.

Start Simple

Award-wining work at ADT Fire & Security shows demonstrates how making data easy to access gave instant visibility. This was one of the first steps taken, providing immediate financial benefits. For instance, the busy summer period was revealed to be caused by supply issues (many people off at the same time) rather than demand.

Visibility on engineers’ utilisation started to build a culture in which everyone rose to the productivity challenge. Key to this was data-driven performance management, a significant change in role for front line managers. Data-driven decisions are all about people! “We try and think what people want, what’s next and how we can help”, explains Marie Eustace, Business Planning Manager.
“There were people who resisted the change, but you just need to work harder bring them along,” adds Deborah Morrison, Service Manager.

In fact, the amount of data or analysis required to stimulate major changes is quite low. It’s simple from a data perspective and has powerful impact. So why aren’t more people doing it?

By linking this data into capacity planning and better resource allocation, the operation saved £1.9m in overtime, with 60% fewer ‘no access’ visiting and 35% fewer service complaints. Significantly, data alone doesn’t drive this activity. It needs to be linked into dashboards that managers can easily use and integrated into a demand-led resourcing model that starts from your service strategy and budget – not just with a team allocating jobs.

Capacity & scalability

Typically, data-driven decision making uncovers areas where data is missing, incomplete or inaccurate. At BT/OpenReach in 2017, simplified workflow and scheduling depended on reducing 1,000 tasks to 30 service priorities.
At the start, only 10% of jobs raised were accurately described. Engineers were interrupted, sent out on ‘priority’ jobs which turned out to be anything but! Collaboration across the business led to improved diagnostics and soon 90% were accurate first time. “This enables engineers to use their skills and knowledge to deliver better customer service”, concluded Mark Williamson, then Director of Exchange Engineering Services at BT.

At Anglian Water’s sewerage plants in 2018, data was key to creating a sustainable maintenance solution. Two months of quality checking found 4,500 data inconsistencies out of 27,000 plans: incorrect expectancy times, wrong frequency of checks, and duplication of work. “We needed to get boots on the ground to embed their knowledge in the systems”, observes Kostas Verykokidis, Project Lead. “Include people who do the work”.

A central database and an end-to-end understanding of workflow made it possible to group similar tasks in the system and put the right sequence of jobs together. There were 42% fewer repeat visits and backlogs dropped 78% in six months.
While transformation can start simple, you need to plan for scalability – and invest in your insight team. At ADT, the success of their work in 2017 was recognised and resulted in new work across the whole JCI group, across Europe. To scale up the use of insight requires a different kind of data structure and capacity – and they are now using Microsoft’s PowerBi to both automate data production and share it in engaging dashboards. Data governance and collaboration are key success factors.

A global survey from Bi-Survey.com shows that businesses with a collaborative style of decision-making treat information much more as an asset than companies with other decision-making approaches.

They are also more likely to use information to identify new opportunities. What’s more, although less than half respondents agreed that information is highly valued for decision-making or treated as an asset in their organisation today, two-thirds believe it will be in the future.

Data driven decision making

While you can start simple, data science also enables AI or Machine Learning for predictive and prescriptive approaches (see Gartner diagram). Work at Anglian Water in 2019 demonstrates a collaborative approach towards this. Firstly, a group of experienced analysts, under Richard Thorpe, created a federation of insight. Rather than attempt to centralise everything or leave isolated pockets of analysis (‘Shadow BI’) the federation offers a shared set of 14 guiding principles and learning activity that develops them as insight professionals.

Alongside this, a central Data Science team have developed Machine Learning approaches, generating millions of pounds in benefit within their first year. For example, analysis has helped them auto-regulate the energy-intensive aeration in sewage treatment, optimise the placement of expensive sensors and build a weather impact system, which was tried and tested during the
“Beast from the East”.

This gave a good opportunity to raise the profile at senior levels in the business. As well as this, analysis is successfully identifying potentially dissatisfied customers (up to 1:3 from 1:10) to trigger preventative contact and auto-sorting emails to improve response times by 3 days.

The approach to change is important. Build early successes, with demonstrable savings, and use continuous improvement to develop further with agile methods – such as ‘sprints’ and ‘scrums’ – rather than traditional programme management.

Automation is another key benefit that can flow from this. You can use machine learning to identify trends (rather than rely on human analysis and time) and then add automatic triggering of certain actions under pre-defined circumstances.

Also use data preparation and visualisation tools to automate the production of dashboards and exception reports. Microsoft’s PowerBi is gaining traction, in particular, as part of the movement towards information optimisation.

Paul Smedley leads a best practice network for professionals in service operations across Europe – the Professional Planning Forum, established in March 2000. The member case studies referred to can be found in our resources portal at www.theforum.social. Contact Paul on +44 7959 438 712 or paul@theforum.social.