Executives ask the same question three ways and get three different answers. The problem is not the volume of data. It is the absence of a measurement architecture that connects numbers to decisions, and decisions to the teams accountable for acting on them.

Gradion builds analytics systems that close that gap. Not reporting layers on top of existing chaos, but structured measurement frameworks tied to business objectives, operational monitoring that surfaces problems before they become escalations, and domain-specific analytics built around how each operation actually runs.

Analytics Sits on Top of Data Engineering

Data Engineering builds the infrastructure: pipelines, warehouses, transformation layers, and quality monitoring. Analytics & BI builds the intelligence layer on top of it: the KPI frameworks, dashboards, domain-specific reporting, and predictive models that turn reliable data into decisions.

Many engagements include both. When the data infrastructure is already sound, we build the analytics layer directly. When it is not, the data engineering work comes first. The two are sequential phases of the same objective - reliable data in, reliable decisions out.

KPI Framework Design

Before a single chart is built, the measurement architecture needs to be right. We work with operations, finance, and commercial teams to define what to measure, why each metric exists, and which team is accountable for moving it.

The output is a governed KPI hierarchy - not a dashboard spec, but a structured map of business objectives to leading and lagging indicators. Metric definitions are documented and versioned. When the CFO, the logistics director, and the e-commerce lead look at the same number, they are looking at the same definition.

What the framework includes. A tiered metric structure linking strategic objectives to operational KPIs. Documented definitions for every metric - calculation logic, data source, refresh frequency, and the team that owns it. Explicit distinction between leading indicators (which you act on) and lagging indicators (which confirm whether the action worked). A dependency map showing which metrics feed which decisions.

For a European multi-brand e-commerce portfolio, we delivered a KPI framework spanning five brands with different business models. The framework established shared metric definitions across brands while preserving the operational specifics that made each brand's analytics meaningful. Leadership could compare portfolio performance on consistent terms for the first time.

What We Build

Operational reporting Real-time visibility into the metrics that drive daily decisions. We build reporting layers that give commercial, operations, and finance teams same-day access to performance data - replacing manual reconciliation, weekly report cycles, and the spreadsheets that fill the gap when official systems are too slow.

For Vietnam’s largest coffee chain, this meant consolidating four fragmented databases across 928 outlets in Vietnam into a reporting layer that gave commercial and operations teams real-time visibility across every store. The shift from weekly manual reconciliation to same-day decisions was the difference. Revenue grew 12% within three months - not because the data was new, but because teams could finally act on it in time.

Manufacturing and production analytics OEE by line and shift, yield analytics, throughput bottleneck identification tied to production scheduling data. We build analytics that connect to how manufacturing operations actually run - not generic dashboards that require a data analyst to interpret.

For Senior Aerospace Thailand, teams had defaulted to Google Sheets because querying the ERP directly was too slow and too complex. We built an analytics dashboard integrated with their Infor Syteline ERP that gave line managers live visibility of production status, efficiency metrics, and output by line and by shift. The analytics layer - not the data integration underneath it - was what changed behavior. Operational efficiency moved from 55% to 95% because managers could see problems in real time and act within the shift, not at the end of the week.

Back-office and process analytics Procurement status, supplier performance, workflow bottlenecks, approval cycle times. We build shared operational views that replace the email threads, PDF trails, and spreadsheets that back-office teams default to when no system gives them what they need.

A leading German designer furniture retailer had supplier operations running through email, PDF invoices, and spreadsheets with no shared view across purchasing, warehouse, and finance. We delivered an integrated supplier order management system with AI-powered PDF parsing in eight weeks. Manual work reduced by 70%. All three teams now operate from a shared system with consistent data and clear workflow visibility.

E-commerce analytics Full funnel from session through purchase and return, broken down by channel, product category, and market. Anomaly detection at the pipeline level so exceptions surface as alerts, not surprises. For roadsurfer, the analytics layer built alongside the platform modernization gave the team visibility into the full booking funnel - from search through rental and return - enabling the data-driven decisions that contributed to a doubling of bookings and revenue within a year.

Predictive Analytics

Demand forecasting, anomaly detection, and churn prediction are engineering problems, not research projects. We deliver scoped systems with defined inputs, accuracy thresholds, and deployment targets. Models run in production, monitored alongside operational metrics, with drift detection and retraining built in from the start.

For a DACH e-commerce operator, we deployed a demand forecasting model that integrated with existing inventory planning workflows. The model runs daily against live sales and traffic data, flags anomalies that deviate from seasonal patterns, and feeds directly into the replenishment process. The output is a production system with defined accuracy targets and automated monitoring - not a notebook that a data scientist reruns manually.

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Tools and Platforms

We are not tied to a single BI stack. The platform choice depends on what your team already uses, what your data infrastructure supports, and what level of customization the analytics require.

Tableau and Power BI for organizations with existing Microsoft or Salesforce ecosystems where enterprise BI is the standard. Looker for teams that want a metrics layer tightly coupled to their cloud warehouse. Metabase for teams that need a fast, lightweight analytics layer without enterprise licensing overhead. Custom dashboards (React, D3) for operational analytics that require real-time data, embedded workflows, or integration with internal tools that commercial BI products cannot reach.

We do not recommend tooling before understanding the use case. The analytics assessment defines the requirements; the tool choice follows.

How We Work

Every analytics engagement starts with the decisions that need to be supported, not the dashboards that need to be built.

Week 1–2: Analytics assessment. We map the decisions your teams make, the data those decisions require, and the gap between what exists and what is needed. This covers data availability, quality, and the current reporting landscape. The output is a KPI framework, a dashboard specification, and a build plan with priorities.

Weeks 3–10: Build and iterate. Dashboards and reporting layers are built in structured sprints. Each sprint delivers working analytics that the team can use immediately - not a final presentation at the end. We iterate based on how teams actually interact with the data, adjusting views, metrics, and alert thresholds as usage reveals what matters most. We design for adoption, not just accuracy. A dashboard that is technically correct but doesn't fit the team's decision rhythm will be ignored within weeks. We observe how teams actually make decisions - in standups, in weekly reviews, on the shop floor - and build the analytics layer into those existing moments rather than expecting teams to change their behavior to accommodate a new tool.

Handover and training. Every engagement includes documentation and hands-on training for the teams who will use and maintain the analytics layer. Dashboard logic is documented. Metric definitions are versioned. Where the analytics layer connects to data pipelines, we provide runbooks and data contracts so the team can debug issues independently.

Proof in Production

Vietnam’s largest coffee chain - 12% revenue growth from analytics-driven decisions. 928 outlets across Vietnam. Four fragmented databases. Weekly manual reconciliation before any analysis could begin. Gradion built a reporting layer that gave commercial and operations teams real-time visibility across every store. Revenue grew 12% within three months - because decisions that previously took a week of data preparation could now happen the same day.

Senior Aerospace Thailand - operational efficiency from 55% to 95%. Two production lines with no single operational view. Line managers defaulting to Google Sheets because the ERP was too slow to query. Gradion built an analytics dashboard with live production visibility by line and by shift. Operational efficiency moved from 55% to 95% because managers could see problems in real time and act within the shift.

The retailer - 70% reduction in manual work, shared visibility across three teams. Supplier operations running through email, PDF invoices, and spreadsheets. No shared view across purchasing, warehouse, and finance. Gradion delivered an integrated supplier management system with AI-powered PDF parsing in eight weeks. Manual work dropped 70%. Three teams now operate from a single source of truth.

Engagement Structure

Analytics Assessment 1–2 weeks. We map the decisions your teams need data to support, evaluate the current reporting landscape, and define the KPI framework and measurement architecture. The output is a dashboard specification, a prioritized build plan, and a clear recommendation on tooling. Scoped as a fixed-fee engagement.

Analytics Build 2–4 months. Design and implementation of dashboards, reporting layers, and domain-specific analytics. Built in structured sprints with working deliverables at each iteration. Includes metric documentation, team training, and handover materials. Where data engineering work is required first, this phase follows the data platform build. Scoped based on the number of domains, dashboards, and integration complexity.

Ongoing Analytics Support For organizations that want Gradion to maintain and evolve the analytics layer as the business changes. This covers new dashboard development, metric definition updates, alert threshold tuning, and periodic review of whether the measurement architecture still reflects business priorities. A named analyst maintains continuity with your analytics environment. Scoped as a monthly retainer.