Your management team is making decisions on last week's data, pulled from a spreadsheet someone built in 2019.
The weekly report arrives Monday morning as a PDF. By then the numbers are five days old. The spreadsheet it was built from pulls data from three different systems, requires two hours of manual assembly, and breaks whenever anyone changes the source format. Half the meeting is spent debating whether the numbers are right, not what to do about them.
We build business intelligence dashboards and analytics systems that give your leadership team live operational visibility. Custom dashboards, automated reporting, and self-service analytics built on a single reliable data layer. Decisions made on current data, not reconstructed history.
Executive dashboards with live data that update automatically, not on someone's Monday morning schedule
Single agreed source of truth for every KPI so nobody debates the numbers in the meeting
Self-service reporting for department heads who need answers without waiting for an analyst
Automated report distribution that replaces manual data assembly and PDF email chains
RaftLabs builds business intelligence and analytics systems including executive dashboards with live data, KPI frameworks with agreed metric definitions, self-service reporting for non-technical teams, automated report generation and distribution, and custom visualizations for operational and financial data. We build on Power BI, Tableau, Metabase, or custom front ends depending on your team and infrastructure. Every BI engagement starts with a discovery phase that assesses your data sources, reporting requirements, and current analytical infrastructure -- then scoped at a fixed price.
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The decision support gap
Leadership teams in most $1M-$100M businesses have a data problem that looks like a reporting problem. The real problem is not the dashboard -- it is that the underlying data is inconsistent, assembled manually, and a week behind by the time it reaches the meeting.
Better reporting cannot solve a broken data foundation. We fix both layers: the data infrastructure that makes the numbers reliable, and the presentation layer that makes them accessible to the people who need to act on them.
Capabilities
What we build
Executive dashboards
Executive dashboards built on a reliable data layer with agreed metric definitions rather than on top of inconsistent source data that produces different numbers depending on how someone ran the query. Platform selection based on your team and infrastructure: Power BI (best for Microsoft-stack organisations -- Azure, MSSQL, Office 365 -- with strong DAX modeling for complex metrics); Tableau (strongest visualisation capabilities, preferred for data teams doing publication-quality analysis); Metabase (open source, accessible to non-technical users for self-service, low licensing cost); Looker via Google Cloud (semantic layer approach, strong for organisations with a central data warehouse that want governed metric definitions enforced at the BI layer); or a custom React/Next.js dashboard with Recharts or D3.js for organisations that need deep product integration or white-labelled analytics inside their own application. Dashboard components per leadership function: commercial dashboards (ARR/MRR, new vs expansion vs churn revenue, pipeline coverage, conversion rate by stage, win rate by segment, customer acquisition cost); operations dashboards (throughput, cycle time, queue depth, utilisation by team, SLA attainment rate); finance dashboards (cash position, receivables ageing, burn rate, margin by product and channel); customer dashboards (NPS score, churn rate, net revenue retention, support ticket volume and first response time, LTV by cohort). Data refresh cadence configured per dashboard: real-time streaming for operational dashboards monitoring live transactions; hourly or daily batch refresh for financial and executive dashboards where intra-day variance is noise rather than signal.
KPI framework design
KPI framework design is the discovery work that prevents the most common BI failure mode: a beautifully designed dashboard that the leadership team stops using after three months because the numbers are contested. The framework work happens in workshops with the stakeholders who will own and act on each metric -- resolving ambiguities before they are encoded into data models. Typical contested definitions that the workshop surfaces: what counts as a customer (anyone who has ever paid, or only those with an active subscription); what counts as revenue (at booking, at invoicing, or at cash receipt, and how cancellations are handled); how churn is defined (any non-renewal, or only explicit cancellations, and how trials and payment failures are classified); how conversion rate is calculated (which funnel stages are counted, which sessions are excluded). For each metric in scope: name, definition (the exact business question it answers), calculation method (formula with specific field references), source system (which database table and column is authoritative), refresh cadence (daily, hourly, real-time), responsible owner (who is accountable for the metric's accuracy), and thresholds (what values trigger an alert or need a flag on the dashboard). The documented framework becomes the specification for the dbt models and the BI layer -- both are built from the agreed definitions. Metric definitions are versioned: when a definition changes (for example, when the business changes its revenue recognition policy), the change is documented, the historical data is recalculated where possible, and the dashboard shows the change point with annotation.
Self-service analytics
Self-service analytics enables department heads and business analysts to answer their own data questions without queuing requests to the data team -- but only when the data layer is designed correctly. Self-service that connects non-technical users directly to raw production database tables produces wrong answers; self-service that connects them to a governed semantic layer with clearly named, pre-calculated business metrics produces right ones. The data layer for self-service: dbt models transform raw source data into business-concept tables (fct_revenue, fct_orders, dim_customers, dim_products) with column-level documentation written in plain language. Metabase is deployed on top of this layer with question templates for the most common query patterns (revenue by period, customers by segment, orders by status) that non-technical users can filter and extend without writing SQL. For analysts who want SQL access, the curated tables are available in the query editor with auto-complete for column names and business logic pre-calculated. Data catalog: DataHub or dbt Docs provides a searchable catalog of every available table and column with owner, description, source system, and last-refreshed timestamp -- users can find the right data without asking an engineer which table to use. Row-level security in the self-service layer: finance department users see all company revenue data; regional managers see only their region's data; customer success managers see data only for their assigned accounts. Access is controlled at the data layer, not enforced by dashboard design. Training: 2-hour session for non-technical users covering question building, filter application, and how to verify answers -- the session that determines whether self-service gets used.
Automated report distribution
Automated report generation replaces the weekly analyst task of pulling data from multiple systems, pasting into a spreadsheet template, formatting, and emailing -- a process that takes 2--4 hours per report and must be repeated every cycle. The automation reads from the same data warehouse layer as the dashboards (ensuring the numbers match), calculates the configured metrics for the report period, formats the output using the approved template, and delivers it to the configured recipient list without anyone triggering it. Report types automated: weekly operational summary (key operational KPIs vs prior week vs target, exception flags for metrics outside threshold, top changes with data-backed commentary); monthly business performance report (month-over-month and year-over-year comparisons, departmental performance vs budget, rolling 12-month trend charts); daily exception alerts (metrics that have moved outside a configured threshold in the last 24 hours -- a cash balance drop, an SLA attainment rate decline, an order processing queue spike); and board-level summary packs (quarterly metrics with pre-calculated variance analysis, condensed into a format suitable for board circulation without an analyst producing a manual narrative). Delivery channels: email via SendGrid or your SMTP server with HTML-formatted body and PDF attachment; Slack via Slack API with formatted block kit message; Microsoft Teams via webhook with adaptive card. Report scheduling: cron-based scheduler with business-hours awareness (monthly reports trigger on the first business day of the month, not the first calendar day). Failed report generation alerts the data operations owner so delivery failures are caught before stakeholders notice the report did not arrive.
Operational analytics
Operational analytics built for each department's specific questions -- the difference between a dashboard that gets used daily and one that is opened twice before being ignored. Sales pipeline analytics from Salesforce, HubSpot, or Pipedrive: stage conversion rates (MQL to SQL, SQL to opportunity, opportunity to close) with period-over-period comparison; average deal size and cycle time by segment, product, and sales rep; pipeline coverage ratio (open pipeline value / revenue target for the period); win rate by segment, deal source, and competitive displacement; forecast accuracy tracking (committed forecast vs actual close). Finance analytics from QuickBooks, Xero, NetSuite, or Sage: cash flow with 13-week rolling forecast; accounts receivable ageing (current, 30, 60, 90, 90+ days) with customer-level drill-down; cost by department vs budget with variance analysis; gross margin by product line and customer segment; monthly recurring revenue movements (new ARR, expansion ARR, churn ARR, net new ARR). Operations analytics: throughput by team (items processed per hour, per day, per agent); queue depth and wait time by channel and priority; SLA attainment rate by team and time period; utilisation (active time vs available time); cycle time distribution per process step. Customer success analytics from Gainsight, Totango, or custom data: churn rate by segment and tenure cohort; net revenue retention (NRR) and gross revenue retention (GRR); customer health score distribution and trend; support ticket volume, first response time, and CSAT by customer tier.
Custom data visualisation
Custom visualisation components are built when the insight requires a visual pattern that standard BI chart libraries cannot produce -- or when the analytics need to be embedded inside a product rather than in a standalone BI tool. Geographic visualisation: Mapbox GL JS or Leaflet for operational maps showing field team locations, delivery routes, territory performance heatmaps, or site-level metrics on an interactive map. Network graphs: D3.js force-directed graphs for account relationship mapping, supply chain node visualisation, or API dependency graphs -- pattern recognition in relational data that adjacency matrices cannot surface. Cohort analysis: retention cohort heatmaps showing week-0 through week-52 retention for user cohorts acquired in each period, built to replicate the standard cohort chart that identifies product stickiness inflection points. Funnel visualisation with statistical significance: A/B test funnel comparison charts with confidence intervals so the team can distinguish a real conversion rate improvement from noise. Time-series overlays: multi-period comparison charts showing this year vs last year vs target with configurable period alignment (week-over-week, month-over-month, or custom). Gantt and timeline views for project or production scheduling data. All custom visualisations are built in React with D3.js, Recharts, or Apache ECharts and are designed as embeddable components -- they can live in a standalone analytics dashboard, inside your existing web application, or inside a Power BI or Tableau report via an iframe or custom visual SDK integration.
What business question can your team not answer today because the data is not there?
Tell us your reporting pain and what decisions it is blocking. We will scope a BI system that gives your leadership team the visibility they need.
For most $1M-$100M businesses, Metabase and Power BI are the default starting points. Metabase is open source, inexpensive, and easy for non-technical users to build their own queries without a SQL background. It works well for operational dashboards and self-service reporting where the goal is making data accessible across the organisation. Power BI is stronger for organisations already deep in the Microsoft ecosystem -- Office 365, Azure, MSSQL -- and handles more complex modeling through its DAX calculation language. Tableau has the strongest visualisation capabilities and is the choice for data teams that need highly customised, publication-quality charts, but it comes with higher licensing cost and steeper learning curve. For organisations that want full control over the data layer and custom UI, we build dashboards on top of a data warehouse using a custom front end. We recommend the platform that fits your team's technical capacity, existing infrastructure, and budget -- not the platform with the highest margin for us.
Accuracy starts in the data layer, not the dashboard layer. Before we build a single chart, we design the underlying data model with clear, agreed definitions for every metric that will appear in the product. What counts as an active customer? When is a sale recorded -- at order, at invoice, or at payment? How are returns handled in revenue figures? These definitions are documented, agreed by the relevant stakeholders, and encoded into the data transformation layer. From there, every dashboard reads from the same underlying metric definitions. When the CEO dashboard shows revenue and the finance dashboard shows revenue, they show the same number from the same source because they are both reading the same metric. The debates in meetings shift from 'whose number is right' to 'what do we do about it.'
A BI dashboard is an interactive visual interface that displays key metrics, allows filtering and drill-down, and updates automatically as underlying data refreshes. It is designed for regular monitoring -- daily or weekly check-ins on operational health. Custom reporting is structured data output -- formatted tables, summaries, and calculations -- typically scheduled for delivery to specific recipients. Automated report distribution takes the reports that currently require someone to manually pull data and assemble them, and runs that process automatically on a schedule, delivering the finished report to the right recipients. Most organisations need both: dashboards for active monitoring and custom reports for scheduled delivery to stakeholders who do not log into dashboards. We build both and connect them to the same data layer so the numbers always match.
Yes. If you have an existing data warehouse, database, or data tool, we assess what exists and build on top of it where it is sound. If the existing data layer has quality issues or structural problems that would produce inaccurate dashboards, we address those first and tell you why before we start building the presentation layer. We have built BI layers on top of Snowflake, BigQuery, Redshift, PostgreSQL, MSSQL, MySQL, and custom data pipelines. The BI layer is separate from the data infrastructure layer. They do not have to be rebuilt together.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Business Intelligence and Analytics in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
Scope and cost agreed before work starts. No surprises. No obligation.
Working prototype within 3 weeks of kickoff.
Pay by milestone. You see progress before each invoice.
60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
