Why Enterprise Analytics Is Harder Than It Looks
What does it really take to build, maintain and grow an enterprise analytics platform that customers rely on every day. As part of preparing for our recent 2026 roadmap webinar, I pulled together the delivery data from 2025. Seeing it laid out like this was a powerful reminder of just how brilliant our development and cloud teams really are, and how much disciplined, often invisible work sits behind a platform customers trust in production.
Continuous Delivery in Action , 2025 by the Numbers
In 2025, the team delivered 11 major monthly releases using a true continuous delivery model. No big bang drops and no risky releases. Just steady, well governed change that customers can safely adopt. That level of consistency and confidence does not happen by accident. It is a sign of a highly mature, deeply experienced engineering organisation. That cadence translated directly into customer value. Over 100 customer facing features and enhancements were delivered, including more than 25 improvements specifically for embedded analytics and SaaS use cases. We shipped over 20 visualisation and UX improvements, and 15 plus enhancements across enterprise security, governance and authentication. This was not innovation for show. It was practical, production ready capability shaped by how customers actually use Panintelligence.
Our customers use the platform in different ways, and the bar is high in both cases. For some, Panintelligence underpins internal decision making across operations, finance and leadership. For others, it is a customer facing product feature, embedded directly into their platform and relied on by thousands of external users. Serving both internal and external use cases demands a level of robustness, performance and security that goes far beyond traditional reporting tools.
Reliability, Quality and Production Scale
What I am most proud of is the focus on reliability and quality. The team delivered more than 300 behavioural improvements, significantly reduced export, scheduling and rendering issues, and resolved long standing edge cases across organisations and subscriptions that only surface once you are operating at scale. This is the unglamorous work that reduces support burden, increases trust, and keeps analytics available when the business depends on it. Under the hood, the investment continued. Forty plus third party library upgrades, twenty five plus security and vulnerability fixes, and multiple architectural improvements across rendering, authentication, caching and exports. Customers may never see these changes directly, but they feel the impact through better performance, fewer failures, stronger security and greater confidence as usage grows.
Crucially, this was customer driven delivery. The majority of releases were tied directly to customer feedback, support tickets and real world usage patterns. The focus was firmly on production usage, not features that look impressive at configuration time but fail under real pressure.
Build vs Buy Analytics , The Reality
We often hear the same objections when organisations consider building analytics themselves.
We can build this internally. You can build a first version, but sustaining this level of delivery, security, reliability and cloud maturity month after month is where most teams struggle. Our customers benefit from a dedicated team whose sole focus is analytics done properly.
Our requirements are too specific. In reality, the hardest problems tend to be embedding, multi tenancy, permissions, governance and scale. These are exactly the problems our teams solve every day, allowing customers to move faster without carrying the underlying risk. We will lose control if we buy. Customers retain full control of their data, models and access rules. What they gain is a platform that evolves continuously, is hardened by real world usage, and is supported by people who genuinely care about quality. It feels expensive compared to building. The true cost is not the first release, it is everything that follows. Security patching, architectural change, cloud operations, reliability improvements and support never stop. That ongoing investment is built into Panintelligence.
When analytics is internal, failure slows teams down. When analytics is external and customer facing, failure is visible and damages trust. Our delivery model is designed with that reality in mind.
Building and Sustaining Enterprise Analytics at Scale
Looking at this data as I prepared for the roadmap webinar reinforced something I already knew. Building and sustaining analytics at this level is hard. It takes brilliant people, strong discipline and constant investment.
That is exactly what our development and cloud teams deliver, month in and month out, so our customers can focus on what differentiates their business rather than rebuilding the same hard problems again.
