Data & Analytics

Overview

Data should inform decisions, not sit in silos. We design data architectures, build ETL pipelines, and create dashboards that surface the metrics that matter. From real-time operational dashboards to predictive models that forecast demand, we focus on systems that are reliable, understandable, and actionable.

Most analytics projects don't fail on technology—they fail on trust. Two dashboards disagree about revenue, nobody knows which is right, and everyone quietly goes back to their own spreadsheet. So we start where trust starts: agreed metric definitions, pipelines that check their own inputs, and one place where a number lives. Once the numbers are trusted, using them gets easy—including asking questions of them in plain language, no SQL required.

Capabilities in Detail

Business intelligence dashboards that answer the questions your team asks daily—revenue, conversion, churn, cohort analysis—with filters, drill-downs, and exports. Each dashboard is built around decisions someone actually makes, which is why ours get opened on Monday mornings instead of at the quarterly review.

Predictive analytics for forecasting, anomaly detection, and scoring. We start with honest baselines, tell you plainly when your data can't support a reliable prediction yet, and put models into production with monitoring—not into a notebook that never leaves a laptop.

Data warehousing that consolidates sources into a single source of truth. Metric definitions live in version-controlled models, so "active customer" means one thing everywhere—finance, product, and the board deck included.

ETL pipelines that ingest, transform, and load data on schedule or in real time. Pipelines validate their inputs, alert on anomalies like volume drops or schema changes, and fail loudly—a broken pipeline should page an engineer, not quietly corrupt a quarter of reporting.

Custom reporting tailored to your workflows: investor packs, regulatory exports, per-client statements—generated automatically from the same governed data as the dashboards, so nothing is hand-assembled at midnight before a deadline.

Real-time analytics for live metrics and event streams where minutes matter—operations, fraud signals, campaign monitoring. We're honest about the tradeoff: real-time costs more to run, so it has to earn its place per use case.

Our Approach

We start with the questions you need answered. We map data sources, identify gaps, and design schemas that support both current and future use cases. We prefer SQL-first analytics and tools that your team can maintain. We validate data quality early and build monitoring into pipelines. Above all we optimize for the moment someone challenges a number—lineage and definitions should settle the argument in minutes, not meetings.

How an engagement runs

1. Metrics workshop. Before any tooling: what decisions does this data serve, who makes them, and what does each metric mean, precisely? Disagreements surface here—which is the point.
2. Source mapping and architecture (weeks 1–2). We inventory sources and their quality, design the warehouse schema and pipeline architecture, and put the metric definitions in writing.
3. Pipelines and modeling (weeks 2–6). Ingestion, transformation models, and data-quality tests—shipped incrementally, so the first trusted tables arrive early rather than at the end.
4. Dashboards and training (weeks 4–8). Dashboards built around real decisions, working sessions with the people who'll use them, and adjustments based on what they actually click.
5. Operate and extend (optional). Data-quality monitoring, new sources and metrics as the business grows, and a path from descriptive reporting toward forecasting when readiness is there.

First trusted dashboards typically land in four to six weeks; a core warehouse in six to ten. Scope and fees are set after an initial call.

Tools and stack

PostgreSQL and BigQuery for storage, dbt for version-controlled transformations and tests, Python and SQL for modeling, Airflow or Dagster for orchestration—n8n where a lighter scheduler is the honest fit. Metabase, Looker Studio, or Power BI on top, chosen to match the tools your team already knows. Event data flows in from GA4 exports, webhooks, and managed streams. For the conversational layer we use Anthropic's Claude: ask questions in plain language and get answers grounded in your governed metric definitions—with the underlying query visible, so trust never depends on faith in a black box.

FAQs

Do you work with our existing BI tools or recommend new ones?
We work with what you have—Power BI, Looker, Metabase, custom—and recommend changes only when the current stack limits what you can do. We prioritize tools your team already knows.

How do you handle data governance and security?
We design for access control, audit logging, and compliance from the start. We document data lineage and retention policies.

What's the typical timeline for a data warehouse build?
Initial schemas and core pipelines often ship in 6–10 weeks. Full warehouse maturity depends on source complexity and reporting requirements.

Our data is a mess. Can we still start?
Yes—messy data is the normal starting point, not a disqualifier. The source-mapping phase quantifies exactly how messy, and cleanup happens inside the pipelines with tests that keep it clean, so quality improves as a byproduct of building rather than as a prerequisite project.

Do we need machine learning, or just better dashboards?
Usually dashboards first. Reliable reporting is the foundation predictions are built on—and often it answers the question that motivated the ML idea. When forecasting or scoring will genuinely change a decision, we add it on top of the same warehouse, so nothing is thrown away.

Will non-technical people actually be able to use this?
That's the design goal. Dashboards are organized around decisions rather than tables, definitions are spelled out where the numbers appear, and the Claude-powered plain-language layer means the answer to "can I get this by region?" is a question typed in English—or Greek—not a ticket to a data team.