5 Tricks to Becoming a Service Data Expert

Editor’s note: This story by Mark Homer, VP of global customer transformation at ServiceMax from GE Digital, was adapted from Connected Technology Solutions Magazine for Field Service, a quarterly print magazine by Field Service Digital and ServiceMax. Check out the full magazine in print or online.

“We need more data diversity,” analytics and big-data expert Bernard Marr said at a recent big-data conference in Berlin. “Companies that do well are usually more diverse and have more-diverse data sets.” The problem is that the technology development and data generation are moving fast, and service leaders struggle to keep up. We need more data diversity and more-intelligent, more-connected data to inform decision making, but we also need people who understand it and can turn that data into something valuable.

The challenge for all organizations is understanding where that value lies and how to utilize it. There’s so much data: from CRM systems, asset performance management, finance, logistics, HR, and, increasingly, from IoT-connected devices. Throw in service data—which is becoming richer and 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 that executives need to do in order to become data experts for their organizations?

Step 1: Beware of Data Hype

Mark Homer

There has been a lot of buzz over the past few years about big data and artificial intelligence (AI). The industry conferences are loaded with speakers telling businesses to jump on the data wagon and roll. But what’s the point, unless those businesses align data strategies with their business strategies? Executives shouldn’t fear big data; they should 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 managers achieve those goals? If the data isn’t helping a business to make money and improve customer experiences or services, then it’s not worth having.

Step 2: Start Small and Focus

Data for data’s sake is a losing strategy. Instead, managers should focus on an area where they know it could have the biggest impact. Field service data, for example, can give a business intel on equipment performance through diagnostics, while also highlighting inefficiencies in how products are serviced. Are engineers being sent to the wrong jobs geographically? Are they turning up with the wrong tools and spare parts? Are the products they’re repairing showing consistent problems that the R&D department should know about for future models? Are customers ready for an upgrade, and can sales teams be alerted? Focusing on an area such as service will help businesses to understand how data can be beneficial to multiple departments.

Step 3: Play Clever Detective, Not Mad Scientist

Executives need to direct their data scientists to discover new business insights using the data sets and tools at their disposal. They need to be creative and use their experience to determine where products are working well, where there are consistent faults and issues, and where the business may be losing money and can improve. The data will help to support theories or offer alternative viewpoints. It can show inefficiencies, but also provide evidence for improving processes and practices. Can cost centers become profit centers? How can the organization monetize the data once it has been captured?

Step 4: Know Your Data “Lakes” and “Gravity”

Not all data is equal, of course. It’s important to understand how data can be harvested, and which data sets carry more weight. (This will vary depending on your initial objectives.) “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 a concept in which data grows in size and weight and attracts applications and additional data toward it as it flows through an organization. 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 to that the service warranty or service contract and entitlement data; triage information collected as background to a service request; and the consumption of parts, labor, and diagnostic results—and it’s clear how service data becomes so weighty.

Step 5: Prepare for Disruptive Decision Making

Modern technologies allow service leaders to connect nearly everything. The challenge is often analysis. Leaders should examine data points across the entire organizational ecosystem — production, supply chain, finance and service, among others. As a result, there are many executive stakeholders involved in real-time data analytics, which is disruptive to legacy decision-making models. The influx of new data sources, spanning multiple executive roles, requires companies to restructure decision-making processes, especially as demand for increased productivity and performance means that a greater share of data analysis will be done in real time.