The Data Dilemma: Navigating the Gap Between Too Much and Not Enough

Speaker: Gerardo Pelayo, Ph.D., VP of Research and Advisory, Service Council

Key Takeaways:

This session with Gerardo Pelayo explored the challenges organizations face in managing data and the gap between too much and not enough data.

• The challenge isn’t collecting data. It’s converting it into timely, actionable insight for service delivery.
• Teams get stuck at both extremes: overload and scarcity.
• Real-time signals plus AI accelerate decision quality when paired with human judgment.
• Automation should reduce noise so technicians can focus on high-value work.
• Integrating data across all service touchpoints drives a smoother customer experience.

The Great Data Gap

Service organizations collect information from equipment, customers, and every technician device. The question is not “How much do we have?” but “How much of it helps us decide what to do next?” Too often there’s a disconnect between the volume gathered and the insight applied. The core leadership task is to distill raw inputs into a clear, prioritized plan of action.

Understanding “Data That Is Data”

Resolving the data dilemma in fast-moving environments means understanding that yesterday’s patterns don’t always predict tomorrow’s failures. The first step is agreeing on what to collect and why.

The Right Data

It is not about gathering it all. Instead, businesses need to identify the data points that will be important in future service requirements. Consider the case of NVIDIA. They were forced to construct their database from the ground up, designing a system capable of capturing a continuous flow of operational data across every phase of the product lifecycle.

By collecting and analyzing information from the factory floor, the assembly process, in-field diagnostics, and even the customer’s data center, they built an evolving model of product performance. This infrastructure allows them not only to trace the root cause of issues but also to anticipate future service needs through predictive analytics.

The result is a self-improving feedback loop where every interaction, repair, and performance metric contributes to smarter service delivery and long-term reliability.

The Wrong Data

Some metrics distract more than they inform. Review what you collect on a regular cadence, retire stale fields, and keep a lean, comprehensible dataset.

Work With Imperfect Data

Admittedly, there is no perfect data. Field technician customer notes may be written creatively, and device-based data, such as data from smartwatches, may be inaccurate or misleading. The real question is when the evidence is “good enough” to act.

Qualitative & Quantitative

Blend quantitative and qualitative. Pair hard metrics (the what) with frontline feedback (the why). This mix is more challenging to scale, but essential for understanding reality in the field.

AI and Human Judgment

Use AI to augment, not replace. AI can cluster patterns, surface anomalies, and propose next actions. Planners will trust recommendations that are explainable and improve as the system learns from user decisions.

From Data to Actionable Intelligence

More data doesn’t equal better decisions. The goal is not bigger lakes but smarter surfaces.

Density of Intelligence

Give technicians only the few items that change today’s job: parts risk, likely fault, step-by-step fix, and customer context. Use prioritized dashboards so they spend time solving, not searching.

Proactive Planning

In fast-evolving product lines, historical data ages quickly. Tactics include simulation to create “artificial” failure data, plus resilient supply chains that can respond even when confidence intervals are wide.

Enhancing Data Utilization in Field Services

The following are ways in which companies can improve their field service data management and achieve improved results:

• The Role of Real-Time Data: Stream events to adjust routes, triage parts, and update ETAs as conditions change.
• Data-Driven Decision Making: Use trends to forecast demand, anticipate failures, and staff accordingly.
• Automating Routine Data Analysis: Let AI handle routine triage so technicians focus on complex, high-value work.
• Unified view: Integrate across channels and touchpoints to present one coherent customer and asset picture.

FSM Software as a Bridge

Field Service Management (FSM) software is the answer to the data dilemma. An effective FSM platform bridges the gap between raw data and actionable insights.

Using the appropriate FSM platform, organizations can make data collection in field services smarter, more efficient, and scalable.

FAQs:

1: What is the biggest data challenge for service organizations?

Either overload or scarcity. Many firms have volumes of records but struggle to convert them into clear, real-time decisions. The key is to introduce field service data management platforms that prioritize data relevance and clarity.

2: How can AI help manage imperfect data in field services?

AI plays a crucial role in field service data analytics, as it helps make sense of imperfect data. AI supports field analytics by spotting patterns, forecasting issues, and presenting practical recommendations. Human judgment remains essential, and technicians’ experience improves as the system learns from user decisions.

3: Why is real-time data crucial for service technicians?

Real-time information enables technicians to make decisions on the spot, reduce delays, and enhance customer service. This is one of the key elements of data collection in field services, as it helps simplify the entire service process.

4: How can organizations improve the quality of their field service data?

The secret to enhancing data quality lies in field service data management software. Focus collection on leading indicators, review fields regularly, automate cleansing and triage, and use FSM tools that transform inputs into actionable guidance.