In development

TDRA Consulting is being built and isn’t formally taking engagements yet. The services and projects described here reflect completed work and current capability. If something here matches a problem you have, get in touch anyway. Early conversations are welcome.

CapabilitiesSix services

Start with the problem. The service follows from it.

You bring the business problem. These are the capabilities used to investigate it. The objective isn’t more reporting. It’s answering a question well enough to act on it.

You don’t need to know which service you need.

Start with what’s happening. Pick what sounds familiar, or describe it in a sentence, and the finder suggests where an investigation would usually begin. Or skip it and just tell Troy what’s going on.

Six capabilities, one business question at a time.

Each is a way of investigating a problem, not a product to buy. Most engagements combine two or three.

01 / 06

Business Intelligence

Reporting and visibility · 1–2 weeksUseful when nobody trusts the numbers, or reporting eats hours every week.
What
Dashboards, management reporting, KPI definitions and refresh workflows.
So what
Leadership can see current performance without anyone rebuilding a spreadsheet by hand.
Now what
Agree the KPIs that matter, model the data behind them, and hand over reporting that maintains itself.
Power BIData modelingKPI definitionRefresh workflows

Questions it can answer

  • What happened?
  • Where did it happen?
  • How is it changing?
  • What needs attention?
  • What should we investigate next?

A dashboard isn’t automatically insight. The work sits in the layer between the data and the decision: what the numbers are saying, and what deserves attention.

02 / 06

Business & Operational Analytics

Patterns and drivers · 1–3 weeksUseful when a number moved and nobody can say why.
What
Sales, margin, payroll, inventory and customer analysis; variance analysis; driver and trend analysis.
So what
You learn which factors actually move the number, rather than which ones people assume do.
Now what
A prioritized set of findings, each tied to a lever you can pull, with the size of the opportunity estimated.
Variance analysisDriver analysisSegmentationCohort analysis

Questions it can answer

  • Where are we performing well, and where are we falling behind?
  • What is changing over time?
  • Which customer behaviours or segments are emerging?
  • Where are inefficiencies, bottlenecks or unusual patterns appearing?
  • Where does the data point to an opportunity worth investigating?

The method follows the question: exploratory analysis, segmentation, trend and variance analysis, correlation or visualization. The objective is simple: move from numbers to understanding.

03 / 06

Workflow & Process Analysis

Process and throughput · 2–3 weeksUseful when work slows down and the bottleneck isn’t obvious.
What
Process mapping, throughput analysis, bottleneck identification, labor analysis and exception analysis.
So what
The step that is actually constraining output becomes visible, along with what it costs you.
Now what
Quantified improvement opportunities, sequenced by effort and impact, with a way to measure the change.
Process mappingThroughputBottlenecksLabor analysis

Questions it can answer

  • Where does work wait, and for how long?
  • Which steps add cost without adding value?
  • Where do handoffs break down?
  • What would change if the bottleneck moved?

Process problems often look like people problems in the reports. Mapping how work actually moves usually shows where the delay and cost really sit.

04 / 06

Predictive Analytics

Forecasting and planning · 2–4 weeksUseful when the plan depends on what comes next.
What
Use historical data to estimate likely outcomes: demand, conversion, retention or volume.
So what
Planning shifts from extrapolating last year to working with an estimate and a stated confidence.
Now what
A model with its accuracy reported honestly, plus the practical segments or thresholds a team can act on.
Propensity modelingForecastingModel validationScenario inputs

Questions it can answer

  • What exactly are we trying to predict?
  • What information is available before the outcome?
  • Are there sources of leakage that would flatter the model?
  • How should success be measured?
  • What would someone actually do with the prediction?

A model can produce a number. The business still needs to know what that number means. A more complicated model isn’t automatically a better one: the useful model is the one that reliably supports the decision it was built for.

05 / 06

Applied Data Science

Method and modelling · Scoped to needUseful when ordinary reporting can’t answer the question.
What
Machine learning and statistical methods applied to a specific business question.
So what
A question that ordinary reporting cannot answer gets a defensible answer, with its limits stated.
Now what
Findings translated into business language, with what was established separated from what still needs testing.
Pythonscikit-learnFeature engineeringValidation

Where it can help

  • Information discovery and search
  • Document and knowledge processing
  • Classification and prediction
  • Recommendation
  • Workflow support

Before asking how to use AI, ask what you’re trying to improve. TDRA is most interested in tools that help people make better decisions rather than replace their judgment, which means weighing accuracy, interpretability, reliability, human oversight and business usefulness.

06 / 06

Decision Support

Recommendations · Scoped to needUseful when a decision has stalled and the trade-offs aren’t clear.
What
Executive analysis, scenario comparison, business cases and recommendation briefs.
So what
A decision that has been circling for weeks gets the evidence and trade-offs laid out side by side.
Now what
A short brief: the options, what the evidence supports, what it does not, and a recommended next step.
Scenario comparisonBusiness casesPrioritizationExecutive briefs

Questions it can answer

  • What decision are we trying to improve?
  • What information does that decision require?
  • Where does it come from, and how reliable is it?
  • What analysis is actually needed?
  • What should happen next?

Better decisions start with knowing what the data is for. Decision support works from the decision backward: options, evidence and trade-offs, ending in a recommended next step.

Every engagement runs the same way.

01

Business

Understand the problem, and the decision actually being made.

02

Data

Structure and analyze the evidence you already have.

03

Decisions

Identify what the evidence means, and what it doesn’t.

04

Action

Turn the insight into a practical next step you can measure.

Bring the business problem.

You don’t need to know which service you need. Start with what’s happening, what you’re trying to understand, or the decision you’re stuck on.

Start the conversation ↗