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Data & Analytics

Your business already produces the data. We build the infrastructure that turns it into one source of truth — numbers your teams can actually act on.

One source of truth

When every team keeps its own spreadsheet, every meeting starts with an argument about whose numbers are right. We build the data infrastructure that ends that argument: one trusted place where information flows in cleanly and comes out ready to use.

Data pipelines

The plumbing that moves information automatically from where it is created to where it is needed.

Warehousing

One organized, central home for your business data, instead of numbers scattered across tools and spreadsheets.

Decision analytics

Turning raw data into clear reports and dashboards that answer real business questions.

Governance

The rules for who can see what, and the checks that keep your data accurate and safe.

From raw data to daily decisions

Data work fails when it ignores the people who will use it. We start with your decisions, build the infrastructure to support them, and maintain it as your business grows.

  1. Start with the decisions

    We learn which questions your teams need answered before we touch any technology.

  2. Build the foundation

    We design and deliver the pipelines and warehouse that bring your data into one place.

  3. Keep it dependable

    We maintain what we build, so the numbers stay accurate as your business changes.

Work behind this practice

A few engagements where this practice did the heavy lifting.

Sports technology · Alley Analytix

From raw telemetry to coaching intelligence

Problem

Streams of noisy sensor data meant little on their own — players and coaches needed interpretable metrics and development trends, not raw signals.

Approach

We built the full analytics path: a motion-intelligence pipeline that corrects, fuses, and classifies each throw, cloud ingestion designed for large simultaneous player populations, and dashboards that track development over time.

Outcome

Coaches read progression across sessions instead of judging throw by throw, on a platform built to keep scaling.

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Experience economy · Counterfoil

One data backbone for pricing, inventory, and channels

Problem

Revenue-critical data sat fragmented across booking, pricing, and distribution tools, so operators could not see how one decision affected the others.

Approach

We normalized the operating domain into one canonical data backbone, with a data layer designed to serve both transactional execution and the analytical feedback loops that refine rules over time.

Outcome

Operators manage pricing, inventory, and channel exposure as connected levers, with decisions informed by shared, current data.

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