From six-week dashboard cyclesto production in week three.

The Situation

This leading marketing consulting firm serves a growing portfolio of clients across industries — delivering campaign analytics, performance reporting, and data-driven strategy. Their analytics operation ran on Google Cloud Platform, with BigQuery as the data warehouse and Looker as the reporting layer. But the team had outgrown their ability to keep pace with client demand.

With an expanding client roster and an AI roadmap on the horizon, the firm needed to modernize their analytics delivery model without disrupting ongoing client commitments. They needed execution — not a strategy deck.

The Complication

Every new dashboard request started from scratch. Analysts were manually pulling data, cleaning it, mapping it to reporting schemas, and rebuilding logic that had been built a dozen times before for other clients. The dashboard delivery lifecycle stretched to six weeks — far too slow for a business where client expectations are set in days.

The friction was not just speed. The gap between the data preparation layer in BigQuery and the visualization layer in Looker meant reports were inconsistent — the same metric calculated differently across clients. Data engineers were spending the majority of their time on plumbing rather than insight.

The core engineering team was already at capacity. Adding more ad-hoc work would break delivery commitments. They needed embedded execution capacity — people who could work inside their environment, their tools, and their cadence from day one.

The Solution

DataWalkers deployed an embedded data engineering team directly into the client workflow — operating inside their Slack, Monday.com, GCP, BigQuery, and Looker environment from week one. No onboarding lag. No discovery phase that stretched into months. Execution from day one.

In the first two weeks, we mapped the end-to-end reporting workflow — from data source through transformations, report generation, approval, and delivery — and identified the highest-impact bottlenecks. By week three, the first production dashboards were live.

We rebuilt the BigQuery transformation layer to eliminate redundant logic and standardize data models across client engagements. Every dashboard was treated as a reusable component — building a growing library of Looker patterns that meant new client reports could be delivered in hours, not weeks.

In parallel, we assessed the firm AI roadmap and began laying the data infrastructure groundwork — clean ingestion pipelines for structured and unstructured data, and a prioritized roadmap for AI use cases the platform could now actually support.

The Impact

The results were immediate and measurable. Dashboard delivery went from a six-week cycle to production in week three. The analytics team reclaimed the majority of their time previously consumed by manual data preparation. And for the first time, every client report was built on the same governed, standardized data foundation.

• 4×Faster insight delivery across client engagementsSix-week dashboard cycles compressed to days through reusable Looker components and standardized BigQuery models.
• ↓70%Reduction in manual data preparation overheadAnalysts freed from repetitive data wrangling — time redirected to strategy and client insight delivery.
• Week 3First production dashboards liveEmbedded team delivered to clients within three weeks of engagement kickoff.
• AI-ReadyData infrastructure supporting the AI roadmapGoverned ingestion pipelines enabling the next wave of AI-assisted client analytics.