Find revenue opportunities
Identify customers, products, routes, markets or segments that are outperforming, underperforming or hiding commercial potential inside large datasets.
Revenue · Cost · Insight · Automation
Turn large datasets into revenue opportunities, lower costs and faster decisions.
I help businesses answer high-value commercial and operational questions from large, messy or fragmented datasets — then build reusable analytical workflows so the team does not have to solve the same problem again next month.
Where the value comes from
Data analysis should either make money, save money or improve an important decision.
Processing more rows is not the business outcome. The useful question is what changes once those rows become understandable.
Reduce expensive manual work
Replace recurring exports, spreadsheet manipulation, repetitive joins and reporting work with reusable analysis workflows that give teams time back.
Lower data processing costs
Improve how data is stored, filtered, partitioned and processed so teams do not repeatedly scan, move or compute more data than the business actually needs.
Make decisions faster
Turn fragmented operational and commercial data into clear answers so important questions do not sit in a reporting backlog for weeks.
The economics
Small inefficiencies become expensive at scale.
The value of better data work depends on your volumes, labour costs, cloud spend and commercial exposure. These examples show where I look for economic impact.
Manual reporting adds up fast
Example: if three people each spend six hours per week extracting, cleaning or reconciling data, the business spends 936 staff-hours per year before anyone acts on the information.
Small revenue leaks become expensive
On $1 million of annual revenue, even a 1% issue in conversion, pricing, retention or operational leakage represents $10,000 of economic exposure.
Data infrastructure can have direct ROI
In previous work, I redesigned a cloud data-processing workflow and reduced its processing cost by roughly 90%. Better architecture can materially change recurring data costs.
Results-oriented
The technical work matters because of what it changes economically.
90%
Lower cloud processing cost
I redesigned a data-processing workflow that reduced recurring cloud processing costs by roughly 90%.
Improved sales
Analysis tied to commercial decisions
My background spans finance, operations, analytics, data engineering and growth. I start with what the business needs to understand — not which tool I want to use.
Reusable by design
Stop paying twice for the same answer
When a question will come back, I can turn the investigation into automated, high-ROI workflows.
Questions worth answering
Businesses that thrive, ask these questions.©
These are the kinds of questions where large-scale analysis can become commercially valuable.
Where are we losing revenue?
Analyze customer journeys, transactions, routes, products, pricing or operational events to find where commercial performance breaks down.
Which customers or markets deserve more investment?
Compare segments, locations, products, partners or cohorts to identify where demand, conversion, margin or retention is strongest.
What is costing us more than it should?
Investigate processing costs, operational bottlenecks, repeated manual work, inefficient data reads and workflows that consume unnecessary resources.
Why did performance change?
Connect historical data, customer behaviour and operational signals to understand what changed and which factors are most commercially relevant.
Can this reporting process be automated?
Turn analyses that are recreated every week or month into repeatable workflows with consistent business logic and reusable outputs.
Can we trust the numbers?
Find duplicated records, broken identifiers, missing data, inconsistent categories and logic problems before they become business decisions.
What you get
Not just analysis. Something the business can use.
A commercially useful answer
The work starts with the decision the business needs to make — not with producing another dashboard or notebook.
Validated analysis
Data types, joins, identifiers, missing values, duplicates, outliers and business rules are checked before results are treated as trustworthy.
Reusable analytical workflow
When the question will return, the analysis can be structured so it is easier to rerun, maintain and automate.
Decision-ready output
Clear findings, charts, tables, limitations, commercial implications and recommended next actions for technical and non-technical stakeholders.
Technical depth
The stack is chosen to make the analysis faster, cheaper and reusable.
Tools are not the offer. They matter because the wrong storage format, query pattern or workflow can make recurring analysis slow, fragile and unnecessarily expensive.
01
BigQuery & SQL
Analyze event, transaction, customer and operational data at scale while keeping query logic understandable and controlling unnecessary processing.
02
Python
Build reusable analysis, automation, API integrations, data validation and business logic when SQL or dashboards alone are not enough.
03
Polars
Use fast DataFrame workflows and lazy execution to process larger datasets efficiently without loading unnecessary data into memory.
04
Parquet
Replace slow heavyweight exports with compressed columnar datasets that are easier and cheaper to filter, store and reuse analytically.
05
Apache Arrow
Move analytical data efficiently between tools and formats while reducing unnecessary serialization and transformation overhead.
06
Cloud storage & AWS
Work with S3, object storage, partitioned datasets and scalable analytical workflows where storage structure directly affects speed and cost.
How I work
From expensive question to reusable answer.
I do not start by choosing Python, SQL or a dashboard. I start by understanding what the answer could change for the business.
01
Quantify the business question
What are we trying to increase, reduce, understand or decide? What would the answer be worth to the business?
02
Inspect the data
Review sources, size, schema, identifiers, quality, time ranges and whether the existing data can actually answer the question reliably.
03
Find the signal
Analyze the data using the simplest workflow capable of finding the commercial or operational pattern we care about.
04
Validate the economics
Separate technically interesting findings from findings that could materially affect revenue, cost, risk or decision-making.
05
Make it reusable
If the question will return, turn the work into a reusable query, script, dataset or automated analytical workflow.
Technical proof
I can go from the commercial question into the implementation.
The business value comes first, but the implementation still matters. I have worked directly with Python, Polars, Apache Arrow, Parquet, AWS, large files and internal automation workflows.
These articles provide hands-on technical proof behind the service rather than asking you to take a list of technologies at face value.
Polars, Apache Arrow & AWS S3
Technical work combining Polars, Apache Arrow and AWS S3 for scalable analytical workflows.
Utility Python functions for AWS projects
Reusable Python utilities for cloud-based data processing and analytical projects.
Partitioning Polars DataFrames on S3
Working with partitioned analytical data on S3 using Polars and Apache Arrow.
Excel & SharePoint automation with Python
Using Python to connect internal systems and remove repetitive reporting workflows.
What business question is your data not answering today?
Send me the question, the type of data you have and what the answer could change. I can tell you whether the problem is worth analysing, how I would approach it and where I see the likely business value.