How I work

Start with the decision. Then build the analysis.

A repeatable six-step approach for turning operational problems into evidence, action and measurable impact. The tools change; the logic does not.

Professional lens

Business, data and process — with the system before the solution.

My professional identity sits between business, data and process. I like understanding how a system works before deciding how it should work better. That means clarifying the business question, understanding the workflow and then using data to make the problem measurable.

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1
Understand the Business
Stakeholders, process and the decision at stake.
2
Explore the Data
Shape, quality, gaps and what is measurable.
3
Diagnose Root Causes
Explain why the problem is happening.
4
Design KPIs
Metrics that would change a decision.
5
Recommend Improvements
Specific, prioritised and ownable actions.
6
Estimate Impact
A transparent baseline-to-target business case.

What I bring to the process

The mindset behind the method
01 / Understand

Start with the problem.

I break down complex operational processes, clarify what is actually happening, and identify where the real bottleneck sits.

02 / Structure

Make the data useful.

I work with SQL, Python, Power BI, Power Query, Jira and enterprise systems to turn fragmented operational data into something people can use.

03 / Improve

Turn analysis into action.

The goal is not a dashboard for its own sake. It is a clearer decision, a better workflow, less manual effort or a measurable improvement.

What each step means in practice

Business professionals brainstorming and collaboratingStakeholder workshop
01

Understand the Business

Before opening a dataset, I map who is affected, what process generates the data and what decision changes if the analysis goes one way or another.

  • Identify stakeholders and ownership.
  • Map the process end to end.
  • Define the decision the analysis needs to support.
Professionals analysing data charts togetherData exploration
02

Explore the Data

Profile the data before building a dashboard. The objective is to understand whether the data can actually answer the question.

  • Check types, ranges, nulls and duplicates.
  • Compare distributions across relevant categories.
  • Separate data-quality problems from process problems.
Analysing graphs and trends on a laptopInvestigate the signal
03

Diagnose Root Causes

Look for where the outcome should vary and test whether it actually does. A good analysis explains the mechanism, not only the symptom.

  • Segment by priority, team, category or workflow stage.
  • Distinguish process failure from data-capture failure.
  • Use evidence to narrow the cause before recommending a fix.
Analytics dashboard and data visualisationMeasure what matters
04

Design KPIs

A KPI earns its place only when someone would do something differently because of it.

  • Connect each KPI to a decision or root cause.
  • Define targets explicitly.
  • Design for a cadence the team can realistically maintain.
Collaborative problem-solving work sessionPlan the change
05

Recommend Improvements

Recommendations should be specific enough to assign: a process change, system rule, control or policy.

  • Prioritise effort against expected impact.
  • Assign ownership and a measurable outcome.
  • Sequence the changes instead of presenting a wish list.
Charts, graphs and business analysisQuantify the outcome
06

Estimate Business Impact

When the impact is an estimate, I label the assumptions. A transparent number is more useful than false precision.

  • Show baseline versus projected state.
  • State assumptions explicitly.
  • Flag sensitivity where the estimate is assumption-heavy.
Supporting visuals: Pexels. The diagrams and professional case-study visuals elsewhere are original portfolio graphics.
The principle: the dashboard is usually the last step, not the first. The valuable work is deciding what should be measured, why it matters, and what action the signal should trigger.
Recruiter route

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