From Kyvos to StyleBI: Dynamic Dashboarding for an Urban Aquaculture Pioneer
BlueCurrent Farms is a mid-sized urban aquaculture company operating a network of indoor recirculating aquaculture systems (RAS) across three major cities. Its business model depends on precise control of water quality, feed conversion ratios, energy consumption, and fulfillment logistics to supply fresh fish and leafy greens to local retailers and restaurants. As the company scaled, leadership realized that its existing analytics stack, centered on Kyvos for OLAP-style analysis, was no longer aligned with the real-time, operations-focused decision-making they needed on the ground.
What began as a traditional BI initiative—aggregating historical data for executives—had evolved into a demand for dynamic dashboards that operations managers, hatchery technicians, and logistics coordinators could use in the flow of work. The company’s data team initiated a strategic review of its analytics tools and ultimately decided to migrate from Kyvos to StyleBI as the primary dynamic dashboarding solution. This shift was not just a technology swap; it represented a rethinking of how analytics should function in a high-variability, sensor-driven industry like urban aquaculture.
Limitations of the Existing Kyvos-Centric Approach
Kyvos had originally been selected for its ability to handle large-scale OLAP cubes on cloud data platforms. For BlueCurrent’s early needs—monthly performance reviews, cost analysis, and long-term trend reporting—it performed adequately. However, as the company expanded its farm footprint and installed thousands of IoT sensors across tanks, filtration systems, and grow beds, the analytics requirements shifted dramatically.
Operations teams needed dashboards that could:
- Surface near real-time alerts on dissolved oxygen, pH, and temperature deviations.
- Blend sensor data with production schedules, feed plans, and maintenance logs.
- Allow non-technical users to drill from a portfolio view of all sites down to a single tank or pump.
- Support interactive “what-if” scenarios for stocking density, feed regimes, and harvest timing.
In practice, the Kyvos-based environment felt rigid to these users. Dashboards were tightly coupled to pre-modeled cubes, and changes often required coordination between data engineers and BI developers. Iterating on visualizations, adding new sensor metrics, or reconfiguring hierarchies for different roles became a slow, ticket-driven process. The result was a growing gap between what the business needed and what the BI stack could deliver.
Why StyleBI Emerged as the Preferred Dynamic Dashboarding Platform
The analytics team evaluated several options but found StyleBI particularly well-suited to the company’s operational and architectural needs. They were looking for a platform that could sit close to their existing data lake and streaming infrastructure while still providing a highly interactive, web-based dashboarding experience. StyleBI’s combination of flexible data connectivity, strong visual composition, and dynamic parameterization made it a compelling choice.
Several factors drove the decision:
- Operational focus: StyleBI’s dashboarding paradigm is oriented toward live, interactive views rather than static, cube-bound reports. This aligned with the company’s need to monitor tank conditions, energy usage, and production KPIs in near real time.
- Flexible data blending: BlueCurrent could combine sensor streams, ERP data, inventory records, and maintenance logs without having to remodel everything into a single OLAP cube. This was crucial for quickly incorporating new sensor types and external data sources.
- Role-based experiences: The platform’s ability to parameterize dashboards by site, role, and responsibility allowed the team to design tailored views for hatchery managers, water quality specialists, and logistics coordinators.
- Iterative design: StyleBI’s visual design environment made it easier for BI developers to prototype, test, and refine dashboards in collaboration with end users, shortening the feedback loop.
The decision was not about dismissing Kyvos as a technology, but about recognizing that BlueCurrent’s center of gravity had shifted from batch analytics to continuous operational insight. StyleBI was chosen as the platform that could embody that shift.
Designing Dashboards for Urban Aquaculture Operations
The migration project began with a clear principle: dashboards should mirror the way the farms actually operate. Instead of starting from data models, the team started from workflows. They interviewed staff at each facility to understand how decisions were made during a typical day, what information was missing, and where delays or blind spots occurred.
From these sessions, they defined a set of core dashboard experiences to implement in StyleBI:
- Farm portfolio overview: A top-level dashboard showing all sites, with key KPIs such as survival rate, feed conversion ratio, energy use per kilogram of biomass, and current risk flags. Users can click into any site to drill down.
- Tank health and water quality: A dynamic view of each tank’s sensor readings, historical trends, and threshold alerts. Operators can filter by species, growth stage, or risk level and see recommended actions based on patterns.
- Production planning and harvest readiness: Dashboards that combine biomass estimates, growth curves, and customer orders to suggest optimal harvest windows and stocking adjustments.
- Maintenance and downtime analytics: Visualizations that correlate equipment failures, maintenance schedules, and environmental anomalies to identify root causes and prioritize interventions.
StyleBI’s dynamic parameterization allowed the team to build these dashboards once and reuse them across sites and roles. For example, a single tank health dashboard could be filtered by site, room, and tank, with user permissions controlling which combinations each person could access. This reduced duplication and made maintenance more manageable compared to the previous environment.
Technical Migration from Kyvos to StyleBI
On the technical side, the migration involved decoupling dashboards from Kyvos cubes and reconnecting them to the company’s underlying data platforms. BlueCurrent had already invested in a cloud data lake and a streaming layer for sensor data, so the main task was to re-map the semantic layer and rebuild visualizations in StyleBI.
The team followed a phased approach:
- Inventory and prioritization: They cataloged all existing Kyvos-based dashboards and reports, then ranked them by business impact and usage. Only the most valuable assets were targeted for migration; others were retired or consolidated.
- Data connectivity and modeling: StyleBI was connected directly to the data lake tables, sensor streams, and operational databases. Lightweight semantic models were defined to standardize key measures such as survival rate, feed conversion ratio, and energy intensity.
- Rebuilding dashboards: Instead of copying layouts one-to-one, the team used the migration as an opportunity to redesign dashboards around user workflows, simplifying cluttered views and adding interactive filters and drill paths.
- Validation and parallel run: For a period, Kyvos and StyleBI dashboards ran in parallel, allowing users to compare numbers and build trust in the new environment before decommissioning the old one.
Throughout the process, the BI team emphasized transparency with stakeholders, sharing early prototypes and incorporating feedback quickly. This collaborative approach helped avoid the common pitfall of “big bang” BI migrations that surprise users with unfamiliar tools and layouts.
Business Impact of the Switch to StyleBI
Within six months of going live with StyleBI as the primary dynamic dashboarding platform, BlueCurrent Farms began to see tangible benefits. Operations managers reported that they could identify emerging water quality issues earlier, often before they triggered alarms, by watching subtle trend changes in the dashboards. This led to fewer emergency interventions and more stable tank environments.
The production planning team used the new dashboards to better align harvest timing with demand, reducing last-minute order reshuffling and improving customer satisfaction. By visualizing biomass projections alongside confirmed orders and capacity constraints, they could make more confident decisions about stocking and harvest schedules.
Perhaps most importantly, the perception of analytics within the company shifted. Under the Kyvos-centric model, BI had been seen as a back-office function that produced reports for leadership. With StyleBI, dashboards became part of daily operations, used on tablets on the farm floor and in morning stand-up meetings. Analytics moved from retrospective analysis to real-time guidance.
Looking Ahead: Scaling Analytics with StyleBI
The migration from Kyvos to StyleBI marked a turning point in BlueCurrent’s data journey, but it was not the endpoint. The company is now exploring advanced use cases such as predictive modeling for disease risk, optimization of aeration and pumping schedules for energy savings, and scenario planning for expansion into new cities.
Because StyleBI sits at the center of their dashboarding layer, these advanced capabilities can be surfaced as intuitive visual experiences rather than isolated data science experiments. For example, a predictive model that estimates disease risk based on water quality patterns can be embedded directly into the tank health dashboard, with risk scores and recommended actions displayed alongside raw sensor readings.
In an industry where biological systems, urban infrastructure, and market demand all interact in complex ways, having a flexible, dynamic dashboarding platform is not a luxury—it is a strategic necessity. By moving from a cube-centric Kyvos environment to a more agile StyleBI-based architecture, BlueCurrent Farms has positioned itself to operate with greater resilience, responsiveness, and insight as urban aquaculture continues to grow.