Data strategy consulting is a short, structured engagement that assesses how your data is actually architected, governed, and consumed — then produces a written, prioritized plan to close the gap between where you are and what your business decisions require. One Big Table runs data strategy engagements for mid-market and enterprise teams whose architecture grew by accretion: sources multiplied, dashboards multiplied faster, and nobody designed the system underneath. In four to six weeks you get a strategy your team can execute — not a deck that decorates a shelf.
Book a Discovery CallThese are the four symptoms we see in nearly every organization that comes to us — and none of them is fixed at the reporting layer.
Reports exist for everything, but decisions still run on exported spreadsheets — because when a number looks wrong, nobody can trace where it came from.
Finance, sales, and marketing each define "revenue," "customer," and "churn" differently. Without a governed semantic layer, every meeting starts with reconciliation.
The platform bill grows every quarter while query performance doesn't. Cost without architecture governance is the default state of the modern data stack.
The models work in the demo. Then they meet your data — undocumented, inconsistent, ungoverned — and the pilot quietly dies in Q3.
Six named artefacts, delivered as working documents your team keeps — the same standard as our project engagements.
A written data strategy with a prioritized action list: what to build, in what order, and why — each item costed and mapped to a business decision it unlocks.
An honest assessment of your environment across five pillars — architecture, governance, cloud infrastructure, AI/analytics readiness, and BI delivery — with a scored maturity baseline.
The architecture you're building toward: medallion architecture for your sources, a governed semantic layer for your metrics, and the modeling approach that fits your scale.
Data ownership, quality standards, access control, and metric governance — designed as an operating rhythm your team can actually run, not a policy binder.
A cloud and tooling cost baseline with the specific optimization moves — workload tuning, storage tiering, contract posture — ranked by savings and effort.
The strategy sequenced into our 30/60/90 roadmap — foundation by day 30, build by day 60, intelligence by day 90 — so execution starts the week the strategy lands.
The strategy phase typically runs four to six weeks, remote or hybrid, working directly with your data team and the executives who consume its output. It's structured as a fixed-fee diagnostic sprint: stakeholder interviews, architecture and pipeline review, cost analysis, and a final executive readout.
From there, the build phase is optional. The strategy is written to be executable by your internal team or any competent partner — and if you want the architects who designed it to build it, we implement on the same 30/60/90 roadmap, milestone by milestone.
Want a read on your maturity before committing to anything? Start with our free readiness assessments.
Typically four to six weeks from kickoff to executive readout. The written strategy with its prioritized action list is delivered within six weeks. Timelines flex with the number of source systems and stakeholders, but we deliberately keep the strategy phase short — a strategy that takes six months to write is already stale.
A written data strategy with a prioritized action list, a current-state architecture assessment across five pillars, a target-state blueprint covering medallion architecture and the semantic layer, a governance operating model, a cloud cost baseline with an optimization plan, and a 30/60/90 execution roadmap. Every artefact is a working document your team keeps and updates — not a slide deck.
Both. The strategy phase stands on its own — you can take the blueprint and roadmap to your internal team or another partner. Most clients continue into an optional build phase where we implement the architecture we designed, structured on the same 30/60/90 roadmap. Either way, the strategy is written to be executable, with named deliverables and owners, not aspirational.
AI readiness is a property of your data architecture, not a separate initiative. The same foundations a data strategy establishes — governed sources, a semantic layer, quality frameworks, access control — are exactly what AI initiatives stall without. Our data strategy engagements include an AI readiness scorecard, and our AI readiness assessment covers the dimensions in depth.