Today, we want to pull back the curtain on one of the most significant infrastructure investments we have made in FieldPulse history: a complete rebuild of our reporting and analytics engine. This is not a feature announcement. This is a look at why we are doing what we are doing, and what it unlocks for your operations.

The Problem with "Good Enough" Reporting

For years, our reporting capabilities have served the fundamentals well. Work order counts. Technician utilization. Revenue by customer. These are the metrics that keep a field service operation running day to day.

But "running" is not the same as "optimizing."

We have heard consistently from operations leaders that they need deeper, more flexible insight. Not just how many jobs were completed, but which factors predict a successful first-visit resolution. Not just when technicians are busy, but why some routes consistently outperform others despite similar distances.

The legacy reporting stack was not built to answer these questions. It was built to answer yesterday's questions efficiently. As our customers have grown more sophisticated, we needed to grow with them.

What We Are Building: Three Pillars

Our new analytics infrastructure rests on three foundational commitments:

  • Real-time data freshness. Reports that reflect the state of your operation as it exists right now, not fifteen minutes ago. This requires fundamental changes to how we process and index event streams.
  • Composable metrics. Pre-built dashboards are helpful, but operations differ. We are building a system where you can define, save, and share custom metrics without writing code or opening a support ticket.
  • Correlation, not just aggregation. The new engine is designed to surface relationships between data you might not think to connect: weather patterns and appointment cancellations, inventory availability and job duration, technician tenure and customer satisfaction scores.

The Technical Reality

I will not bury you in implementation details, but a few notes for the technically curious. We have migrated from a traditional relational reporting warehouse to a columnar store with streaming ingestion. This sounds like jargon, and it is, but the practical impact is meaningful: complex queries that previously timed out now complete in seconds. Data that required overnight batch processing is available within moments of an event occurring.

This transition has been underway for the better part of eighteen months. It is the reason some of you experienced brief reporting unavailability during our maintenance windows in late 2025. We appreciate your patience. The foundation is now solid.

What Comes Next

The infrastructure is in place. What follows is delivery.

In the coming quarters, you will see:

  • A redesigned Report Builder with drag-and-drop metric composition
  • Scheduled report delivery via email and webhook
  • Native data export APIs for customers operating their own BI tools
  • Pilot access to predictive scheduling recommendations based on historical pattern analysis

We are also evaluating deeper integrations with platforms like Tableau, Power BI, and Looker. If your organization has specific requirements here, we want to hear from you.

Our Commitment

Reporting is not a peripheral feature. For many of you, it is the lens through which you understand whether your operation is healthy, where your teams are stretched thin, and where opportunity lives.

We are treating it accordingly.

The rebuild we have undertaken is not quick, and it is not cheap. We believe it is essential. Our goal is to give you not just more data, but more useful data — the kind that shapes decisions rather than merely documenting them.

Questions about what is coming? I will be monitoring replies to this post for the next several days.

  • It is worth noting that infrastructure changes of this magnitude carry significant audit and governance implications. From a compliance perspective, I would highlight three areas where additional detail would be valuable:

    1. Data residency: Will the columnar store maintain the same geographic boundaries as our current relational warehouse?
    2. Access logging: Does the new engine capture query-level audit trails, or only administrative actions?
    3. Third-party subprocessors: Are any new vendors introduced as part of this streaming ingestion pipeline?

    Our security review cycle is approaching, and this information will determine whether we need to schedule an interim assessment.

  • Keiko, thank you for these thoughtful questions. I want to make sure you're set up for success as we roll this out.

    On Power BI specifically: we have a working prototype of a direct connector internally, and it's performing well. I don't have a committed date I can share yet, but I would love to get you into our early access program when we open it. I'll follow up directly to coordinate.

    For data retention: the granularity you have today is preserved. We know how critical historical fidelity is for compliance and trend analysis, and we built the new architecture with that constraint front and center.

    Happy to schedule time to walk through what this means for your consolidated reporting workflow if that would be helpful.

  • Appreciate the transparency on the infrastructure investment. Two questions:

    First, what is the timeline for native Power BI connectivity? Our finance team runs consolidated reporting across three platforms, and a direct connector would eliminate significant manual work.

    Second, will the new engine retain historical data at the same granularity, or are you planning aggregation beyond a certain retention window? This affects our compliance posture and whether we need to implement additional archiving.

    Strategically, this direction makes sense. Execution will determine adoption.