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Reporting, Analytics & Business Visibility

See the Numbers the Business Runs On, and Trust Them Enough to Decide on Them.

Your business data is spread across ERP, CRM, spreadsheets, and other systems. We bring it together in data warehouses and reporting platforms, then build the models, dashboards, and analytics your teams need to understand performance.

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A Situation We Hear Often

Follow One Problem from Start to Finish.

This is how the problem usually shows up, and what changes at each step of the fix. Switch to By Service to see the work behind it.

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Every Report Has a Different Number

Follow one question through four steps: why the numbers disagree, what one trusted view looks like, how it explains a change, and the foundation behind it.

Every Report Has a Different Number

Three Systems, Three Answers to the Same Question

The QuestionWhat was revenue this quarter?

ERP
$3.43MInvoiced work
CRM
$3.61MClosed deals
Finance Spreadsheet
$3.38MAfter manual adjustments

Each figure is correct by its own definition. Reporting needs one agreed measure, so the answer is the same wherever people look.

What We Can Work On Here

  • Data Platforms & Warehousing
  • Preparing Data for Reporting & Analysis (applies to this view)
  • Business Intelligence & Reporting
  • Data Analytics
  • Data Architecture & Shared Measures (applies to this view)
  • Operational & Cost Visibility

Scope of Work

What We Can Work On Here

Each of these can be a project on its own or part of a larger one. We start with the one your situation calls for.

Case Studies

See This Work in Practice.

Real projects in this area: the business problem, what we built, and what changed.

98%
Reporting accuracy, up from 65%
300+
Staff using BI or the analyst agent
4 Months
Project timeline

Retail & Fulfillment

How a Retailer Got 98% Accurate Reports and a Faster Order System from One Data Warehouse.

A retail and fulfillment business faced peak-hour slowdowns and crashes from heavy reporting, conflicting definitions across teams, and developers pulled away from product work to answer data requests. We built a separate data warehouse with shared definitions, BI dashboards, and AI-assisted answers for more than 300 staff.

  • Snowflake
  • dbt
  • AWS DMS / AWSAWS DMS / AWS
  • DagsterDagster
  • dltdlt
  • Power BI
  • Terraform
  • Vercel eveVercel eve
  • Data Assist
  • GitHubGitHub
Read the Case Study
20%
Sales revenue increase reported by the project owner
30 Min
Pipeline processing, down from 5–6 hours
~60
Property attributes added per record

Property Services

How a Property Services Business Grew Sales Revenue 20% After Rebuilding Its Data.

A property services business needed current location, property, and weather data to focus its sales outreach. We rebuilt the enrichment pipelines, cutting processing from 5–6 hours to 30 minutes. The project owner reports a 20% increase in sales revenue following the work.

  • Python
  • Apache AirflowApache Airflow
  • Microsoft Azure
  • Azure Database for PostgreSQL
  • Terraform
  • GitHub ActionsGitHub Actions
  • Datadog
Read the Case Study
View All Case Studies

How We Work

How We Approach the Work

Begin with the decision, the person making it, and the questions they need to explore. Then work back to the records, definitions, refresh schedules, and access needed to support that view.

  1. Start with the Decision

    Who decides, how often, and what they need in front of them. A report nobody acts on is not worth building.

  2. Trace the Figures to Their Sources

    We find which systems hold the records and how each one defines the measure, so we can see exactly where two reports part ways.

  3. Agree One Definition for Each Measure

    The teams who use a figure agree what it includes. That definition is written down and built into the data, not left to each report.

  4. Build the Foundation, Then the Views

    A warehouse with tested, scheduled loads comes first. Dashboards follow, checked against figures the business already trusts before anyone relies on them.

Measuring It

What Useful Progress Looks Like

  • Anyone can explain what a figure includes and where it came from.
  • A difference between two reports can be traced to its cause.
  • Performance can be compared without rebuilding the report.
  • The information is current enough for the decision it supports.

These questions help define the improvement with your team and the measures appropriate to the engagement.

What It Looked Like in One Project

98%

Reporting accuracy, up from 65%

Read the Case Study

For Technical Leaders

Technical Considerations

Technologies We Work With

These are the platforms and tools we use most for this work, and we are not limited to them.

Where Do Your Systems Run?
Warehouses & Platforms
  • Snowflake
  • Amazon Redshift
  • Databrickson AWS
Pipelines & Modeling
  • dbt
  • dlt
  • Dagster
  • Apache AirflowMWAA
  • Python
  • AWS Glue
BI & Dashboards
  • Power BI
  • Tableau
  • Looker
  • Metabase
AI Agents
  • Vercel eve
  • Claude
  • OpenAI
  • Snowflake Cortex
  • Amazon Bedrock Agents
Databases
  • PostgreSQLRDS
  • MySQLRDS
  • Microsoft SQL ServerRDS
  • Oracle DatabaseRDS
  • MongoDBDocumentDB

If your business runs on a different stack, we adapt to it and build on what you already have.

How It Connects

Reporting and business analytics make current and past performance understandable. Data intelligence extends that work into forecasting, anomaly detection, recommendations, and operational AI, where machine learning does the work. Both rely on usable data; analytics is not exclusive to AI.

When a Smaller Change Is Enough

A clearer report inside an existing system may be enough. A separate platform should address a need for combining, checking, or reusing information.

A Useful Starting Point

Common Questions

Do we have to replace the systems or reporting tools we already use?

No. Your ERP, CRM, and finance systems stay where they are, and we bring their data together alongside them. If your current BI tool can support the views, access, and connections you need, we build on it. We only suggest a change when the tool is what is holding the reporting back.

Our data is messy and spread across systems. Does it need cleaning up first?

No, that is part of the work. We find where records are missing, duplicated, or defined differently, fix what can be fixed at the source, and write down the rules for the rest. You see what was changed and why, so a corrected figure can always be explained.

How long before we see something useful?

We start with one decision or one area of the business, so the first trusted view arrives before the whole platform is finished. The full timeline depends on how many systems are involved and the state of their data. As a reference, the data warehouse project in our retail case study took four months.

How do we know the numbers can be trusted, and that they stay that way?

Each measure has one agreed definition, and new figures are checked against ones the business already trusts before anyone relies on them. After that, data loads are checked automatically, so a failed or incomplete load is caught before it reaches a dashboard. A reader can trace any figure back to its source.

Who owns the data, the warehouse, and the code?

You do, always. The data, the warehouse, the models, the dashboards, and the code we write belong to your business, and they stay with you if the engagement ends.

Tell Us What Your Team Needs to See, Compare, and Understand.

Share the problem, its business impact, and any systems or constraints involved. We can work from the business context, the technical detail, or both.

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