Cloud Analytics for Health Information
Daily data cube rebuilds replaced with on demand reporting on a HIPAA compliant Azure data platform, adding peer benchmarking for providers.
Data & AI
Data foundations for clearer business decisions.
We connect data sources, build analytical models and create reporting that helps teams answer defined business questions.
Data engineering
Connected sources.
Usable data.
Usable data.
Customer dataOperations
Validate & modelQuality · structure · ownership
Applications & reportingPrepared data for review and action
Built around your needs
What this makes possible.
- Connected data sources and pipelines
- Validated models and useful reporting
- Analytics and forecasting for decisions
Illustrative work context.
When it fits and how we approach it
Details
When this helps
Reports may use different definitions, arrive too late or leave important questions unanswered. We help teams identify the decisions that need support and the data available to inform them.
What we assess
We review source quality, metric definitions and how data is collected, transformed and reported. We distinguish observed patterns from assumptions and check the limitations of the available information.
How we approach it
We build data pipelines, models and reporting around agreed questions. Validation, documented definitions and ongoing ownership help teams interpret the results and keep the reporting useful.
What we deliver
What the engagement can include.
Data pipelines & foundations
Details
We connect source systems, prepare and model the data, and validate it before it reaches applications or reports. Pipelines and clear ownership make the path from source to use easier to operate.
- Connect internal and external data sources
- Prepare, model and validate data for its intended use
- Build a dependable path into applications and reporting
- Make data quality, dependencies and ownership visible
Business Performance Analysis
Details
We analyze revenue, cost and operational data to investigate changes in performance. The analysis considers customer behavior, pricing and process constraints, with the limits of the data made clear.
- Clear breakdown of what impacts revenue instead of surface level totals
- Hidden inefficiencies show up once data is aligned across teams
- Trends are measured over time so short term noise doesn't mislead decisions
- The focus stays on questions leadership actually asks
- Results are explained in plain language, not analyst jargon
Customer and User Behavior Insights
Details
We study how users interact with products and services, from first touch to repeat usage. The goal is to understand what drives engagement, drop off, and lasting value.
- User journeys mapped using real behavior, not assumptions
- Churn signals identified before they become visible in revenue
- Segmentation based on actions instead of demographics alone
- Insights that directly inform product and marketing decisions
- Patterns are validated against actual outcomes
Forecasting and Data Driven Planning
Details
We build forecasting models using historical data and known constraints, with assumptions and uncertainty made clear.
- Forecasts grounded in real historical behavior
- Seasonality and anomalies handled properly
- Planning scenarios tested before money is spent
- Assumptions are visible so they can be challenged
- Updates are simple when new data arrives
Data engineering and analytics work
Published examples of data pipelines, reporting foundations and automated recommendations. Clients are anonymized and outcomes are stated as published.
Automated Customer Recommendations
Manual promotion building replaced by an automated, machine learning supported offer workflow delivering recommendations in near real time.
How the work runs
Understand the Decision Context
We start by identifying the decisions this analysis needs to support. This keeps the work focused and prevents data overload.
Audit and Prepare the Data
Data sources are reviewed, cleaned, and aligned. Inconsistencies are fixed so results can be trusted.
Analyze and Validate Findings
Patterns are tested against multiple angles to avoid false conclusions. Assumptions are challenged early.
Deliver Insights Teams Can Use
Findings are shared with context and recommendations. The goal is clarity, not just charts.
Where it applies
Begin with a business question, agree the definitions and build a reporting path the team can maintain.
Ecommerce
Review sales and customer behavior
Compare channels, purchases and margins with consistent definitions.
Details
Analytics can combine order, campaign and customer data to examine patterns. We document attribution assumptions, data gaps and the limits of conclusions drawn from the available records.
Healthcare
Connect operational reporting
Bring validated sources into a shared reporting model.
Details
Healthcare reporting may combine several operational data sources. Validation, access requirements and metric definitions need clear ownership.
SaaS
Understand retention and product use
Track customer activity against agreed measures.
Details
A reporting model can connect subscription and product usage data to examine retention, churn and expansion. Definitions and time periods need to stay consistent so teams can compare results.
Tools and platforms
Details
Listed to show fit with existing environments. Tools are chosen per project; a listing is not a partnership claim.
Programming Language
- Python
Data Visualization
- Tableau
- D3.js
BI Tool
- Power BI
BI Platform
- Looker
Big Data Processing
- Apache Spark
Database
- PostgreSQL
Data Warehouse
- BigQuery
- Snowflake
Workflow Orchestration
- Apache Airflow
Data Modeling
- dbt
Web Analytics
- Google Analytics
Product Analytics
- Mixpanel
Visualization
- Grafana
What do you need your data to explain?
Tell us about your data sources, reporting needs and the decisions you want to support.