Mark Homer, vice president global customer transformation at ServiceMax from GE Digital explains how to become a service data expert in five easy steps
Service data is not just a missing piece of the jigsaw but a key piece that can add crucial intelligence around manufacturing performance and profitability. But service data intelligence is also outpacing the executive skillset. Here’s how you can harness it for your advantage.
“We need more data diversity,” Bernard Marr, analytics and big data expert, said when speaking in Berlin at a recent Big Data conference. “Companies that do well are usually more diverse and have more diverse data sets,” he added. The problem though, and Marr accepts this, certainly in recent LinkedIn posts on Big Data, AI and engineering, is that the technology development and data generation is moving too fast for us to keep up. Yes, we need more data diversity, more intelligent, connected data to inform decision making but we also need people who understand it and can turn it into something valuable.
The challenge for all organisations is understanding where that value lies and how to utilise it. There is so much data, from CRM systems, asset performance management, finance, logistics, HR and increasingly from remote devices through IoT. Throw in service data, which is becoming richer, smarter and touches just about every part of the business, and you have a recipe for success, but only if you can read recipes.
So, what are the five key things executives need to do, to become data experts for their organisation?
Beware of data hype
Everything for the past few years has been about Big Data and now Big Data and AI. The industry conferences are loaded with speakers telling businesses to “just do it”, just jump on that data wagon and roll. But what’s the point unless you align your data strategy with your business strategy? Executives should not fear Big Data but use it to help enhance business processes and decision making. What are the core goals of the business and how can using data to understand products and customers better help you achieve those goals? Surely if the data is not helping you make money and improving customer experiences or services, then it is not worth having.
Start small and focus
Data for data’s sake? Focus on an area where you know it could have the biggest impact. Field service data for example, would give a business evidential support on product performance through diagnostics, but also highlight any inefficiencies in how products are serviced. Are engineers being sent to the wrong jobs geographically? Are they turning up with the right tools and spare parts? Are the products they are repairing showing consistent problems; in which case the R&D department would be interested to know for future models. Are customers ready for an upgrade, and can sales teams be alerted? Focussing on an area such as service, a traditional sot centre will help businesses understand how data can be beneficial to multiple departments.
Play clever detective, not mad scientist
Executives need to direct their data scientists to discovering new business insights using the datasets and tools at their disposal. They need to be creative and use their experience, their gut instincts to determine where products are working well, where there are consistent faults and issues and where perhaps the business is losing money and can improve. The data will help support theories or offer alternative viewpoints. It can show inefficiencies but also provide evidence for improving processes and practices. Can cost centres become profit centres? How can the organisation monetise the data once it has been captured?
Know your data, lakes and gravity
Not all data is equal of course, so gaining an understanding of how data can be harvested, which datasets carry more weight (this will vary depending on your initial objectives and will no doubt define your ROI) and where it all ends up could have some benefit. Data lakes are repositories for raw, unstructured data, that is not defined until it is needed, which can be a problem for manufacturers. Data gravity is where data grows in weight and attracts applications and additional data towards it as it flows through an organisation. Service data, for example, is becoming richer and heavier because multiple pillars of master data are being linked together – customer data, product data and performance data. Add service warranty or service contract and entitlement, triage information as background to a service request, installed bill of materials and the maintenance bill of material and the consumption of parts, labour and diagnostic results and service data is suddenly very weighty indeed. When combined with planning, sales, accounts and R&D, it can add considerable value, offering a holistic view of the business.
Prepare for disruptive decision making
With technology we can connect everything to everything. Both the cost and benefit increase due to the integrated and holistic nature of Big Data. If the executive responsibility is only for one domain and the data spans multiple domains, then getting a decision on the business case involves a plethora of executives. That is disruptive to the legacy decision making model. It means restructuring decision making processes especially as demand for increased productivity and performance means more analysis of data will be done in real time.
