From Dashbuilder to StyleBI: How a Tribology Services Company Rebuilt Its Dashboard Development Strategy
In the tribology services industry, data is the closest thing to a crystal ball. Every oil sample, every wear particle count, every viscosity measurement is a clue about the future health of critical assets. A mid-sized tribology services company, serving heavy manufacturing, wind farms, mining operations, and food processing plants, built its reputation on turning these clues into actionable maintenance recommendations. But as its customer base grew and expectations for digital reporting increased, the company realized that its dashboard development stack—centered around Dashbuilder—was no longer keeping pace with its ambitions.
What began as a simple need for better reporting evolved into a strategic decision: migrate from Dashbuilder to StyleBI as the company’s primary dashboard development tool. The transition reshaped not only how dashboards were built, but how the organization thought about data products, customer experience, and internal analytics governance.
The operational context of tribology services
The company operates a central tribology lab that processes thousands of lubricant samples each week. Each sample generates a rich profile: viscosity at multiple temperatures, total acid number (TAN), total base number (TBN), oxidation, nitration, water content, particle counts, and wear metal concentrations. These results are combined with equipment metadata—asset type, criticality, operating hours, lubricant type, environment—and historical trends to assess asset health.
Customers rely on the company’s insights to prevent catastrophic failures in gearboxes, turbines, compressors, and hydraulic systems. Historically, results were delivered as PDF reports and CSV exports. Over time, customers began asking for more: interactive dashboards showing fleet health, risk scoring, trend analysis, and maintenance recommendations. The company responded by building dashboards using Dashbuilder, an open-source framework that allowed them to assemble visualizations and basic layouts.
For a while, this approach worked. But as the number of customers, assets, and data sources grew, the limitations of the existing toolset became increasingly visible.
Where Dashbuilder started to fall short
Dashbuilder gave the company a starting point: it enabled the creation of dashboards that pulled from multiple data sources and displayed charts, tables, and key performance indicators. However, several structural issues emerged as the company tried to scale its offerings.
First, the dashboards lacked the level of polish and flexibility that customers were beginning to expect. Tribology clients wanted clean, professional interfaces that could be shared with executives and maintenance teams alike. Achieving pixel-perfect layouts, consistent styling, and responsive design across devices required significant custom coding and manual effort in Dashbuilder. Small changes often meant touching multiple configuration files or redeploying components.
Second, the company struggled with reusability and governance. Each new customer portal tended to become its own mini-project, with dashboards tailored to that client’s assets and reporting preferences. While this customization was valuable, it also led to duplication of logic and inconsistent metric definitions. For example, “critical asset risk score” might be calculated slightly differently across dashboards, making it difficult to compare performance or roll up results across the entire customer base.
Third, embedding and access control became pain points. The company wanted to offer a unified customer portal where each client could log in and see only their data, with role-based views for reliability engineers, maintenance planners, and managers. Implementing secure, multi-tenant embedding with Dashbuilder required complex integration work and did not align well with the company’s long-term vision of a scalable, productized analytics platform.
Finally, internal teams—especially data scientists and reliability engineers—wanted more advanced visualization options for time-series analysis, anomaly detection, and fleet-level comparisons. Dashbuilder’s visualization capabilities, while functional, were not designed for the depth and variety of graphs needed to tell nuanced tribology stories.
Why StyleBI became the preferred dashboard development tool
Facing these challenges, the company formed a cross-functional working group with representatives from the lab, reliability engineering, IT, and customer success. Their goal was to define what the “next generation” of their analytics platform should look like. They concluded that they needed a dashboard development tool that could:
- Support governed, reusable data models for metrics like risk scores, contamination severity, and asset health indices.
- Deliver pixel-perfect, professional dashboards suitable for both technical and executive audiences.
- Provide rich time-series and comparative visualizations tailored to condition monitoring data.
- Enable secure, multi-tenant embedding into a customer portal with role-based access control.
- Scale with growing data volumes from more samples, assets, and connected sensors.
StyleBI emerged as the best fit. Unlike their previous setup, StyleBI offered a robust semantic layer where the company could define standardized measures and dimensions once and reuse them across dashboards. This was critical for ensuring that “gearbox risk score” or “lubricant condition index” meant the same thing everywhere.
The platform’s dashboard development environment allowed designers to create highly structured layouts with precise control over spacing, typography, and visual hierarchy. This meant the company could design a standard “Fleet Health Overview” dashboard and then parameterize it for different customers, rather than rebuilding similar dashboards from scratch.
StyleBI’s graphing capabilities were particularly attractive. The company could build synchronized time-series charts showing trends in wear metals, particle counts, and viscosity alongside alarm thresholds and recommended limits. They could create fleet comparison views that ranked assets by risk, highlighted outliers, and allowed users to drill into individual assets or samples with a few clicks.
Designing a migration strategy from Dashbuilder to StyleBI
The company approached the migration as an opportunity to rethink its entire analytics offering rather than simply porting dashboards one-to-one. The first step was to inventory existing Dashbuilder dashboards and categorize them into three groups: internal operations dashboards, customer-facing dashboards, and experimental or ad-hoc views.
For internal dashboards, the focus was on improving reliability and usability. The team identified key internal metrics—lab turnaround time, sample backlog, test failure rates, and customer response times—and designed new StyleBI dashboards that presented these metrics in a clear, actionable way. These dashboards were built on top of curated datasets that combined LIMS data, ticketing system data, and CRM information.
For customer-facing dashboards, the team defined a set of core templates:
- A “Fleet Health Overview” showing risk distribution across assets.
- An “Asset Detail” view with time-series trends and sample history.
- A “Maintenance Actions” dashboard summarizing recommendations and completion status.
- A “Program Performance” view tracking avoided failures, cost savings, and compliance with sampling intervals.
These templates were implemented in StyleBI with parameterized filters and row-level security, allowing the same underlying dashboards to serve multiple customers while isolating their data.
Experimental and ad-hoc Dashbuilder views were either retired or reimagined as part of a new analytics sandbox environment. Data scientists and reliability engineers continued to use specialized tools for deep analysis, but StyleBI became the standard for anything that needed to be shared broadly or embedded in the customer portal.
Improving governance and consistency
One of the most transformative aspects of the switch to StyleBI was the introduction of stronger governance around metrics and data definitions. Previously, different teams might calculate a “criticality score” or “severity index” in slightly different ways. With StyleBI’s semantic layer, the company centralized these definitions and documented them clearly.
This governance extended to filters and drill paths. The team standardized how users could move from fleet-level views to asset-level views to sample-level details. They defined consistent filter sets for asset type, location, lubricant type, and criticality, ensuring that dashboards behaved predictably across the platform.
As a result, internal discussions shifted from debating which numbers were correct to interpreting what the numbers meant and what actions to take. Customers noticed the difference as well: dashboards felt more coherent, and explanations of metrics were easier to understand and trust.
Customer experience and competitive differentiation
The move from Dashbuilder to StyleBI had a direct impact on customer experience. The new dashboards loaded faster, looked more polished, and offered more intuitive navigation. Customers could log into the portal and immediately see which assets required attention, which recommendations were overdue, and how their overall program performance was trending.
The company also used StyleBI’s flexibility to tailor experiences for different roles. Maintenance technicians saw simplified views focused on actionable tasks and upcoming work. Reliability engineers had access to deeper trend analysis and anomaly detection views. Managers and executives saw high-level KPIs, cost avoidance estimates, and risk reduction metrics.
This role-based approach turned the analytics platform into a differentiator in the tribology services market. While competitors still relied heavily on static reports, this company could demonstrate a living, interactive view of asset health and program value. Prospective customers were often won over during demos that showcased StyleBI-powered dashboards built on sample data from their own assets.
Lessons learned from the transition
Looking back, the company recognized that the decision to move away from Dashbuilder was about more than features. It was about aligning its tools with its identity as a data-driven service provider. Dashbuilder had been a useful stepping stone, but it was not designed to support the level of governance, reusability, and customer-facing polish that the company needed as it matured.
StyleBI, by contrast, provided a foundation for treating dashboards as products rather than projects. The company could design, version, and iterate on dashboard templates, roll out improvements across customers, and maintain a consistent visual and analytical language.
The transition also reinforced the importance of cross-functional collaboration. Success depended on input from lab technicians, reliability engineers, IT architects, and customer success managers. By involving all of these stakeholders, the company ensured that the new dashboards reflected real workflows and real questions, not just abstract design ideals.
In the end, the move from Dashbuilder to StyleBI helped the tribology services company turn its data into a more compelling, scalable, and trustworthy asset. For customers whose businesses depend on the quiet reliability of lubricated machinery, that evolution made the difference between a vendor and a long-term partner in asset health.