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.
Business analytics & applied AI · Winnipeg, Manitoba
Turn business data into better decisions.
TDRA Consulting helps organizations turn business data into clearer answers and practical next steps, from reporting and business analytics to predictive analytics and applied AI. You bring the problem. The analysis follows.
Most of the problems TDRA works on start the same way. Something in the business doesn’t add up, and the reports don’t explain it. Pick the one closest to yours.
On AI
Sometimes the answer is AI. More often it’s a number everyone agrees on, a cleaner process, or a simple model people trust. TDRA starts with the decision and reaches for AI only when it earns its place.
Data is only useful when it helps you make a better decision. Sales, customers, operations, inventory and financial performance all tell a story. The hard part is knowing what that story means.
TDRA doesn’t start with a technology. It starts with the business question and works backward into the data. Every engagement follows the same four steps, whichever tools end up being used. Sometimes that means a clear report. Sometimes it means segmentation, a forecast, a predictive model or an applied AI tool.
You don’t need to know which service you need.
Describe what’s happening in the business. Working out whether that calls for a dashboard, a driver analysis or a forecast is part of the job.
Bring the business problemThese are tools for answering a question, not products to buy. Most engagements are fixed-scope projects of one to four weeks, and the problem decides which tools get used.
Lit chevrons show which of the four steps, Business, Data, Decisions, Action, each tool serves.
Dashboards, management reporting and KPI definitions, so the numbers are visible without rebuilding a spreadsheet every Monday.
ExploreFind the drivers behind sales, margin, payroll, inventory or customer behaviour, rather than the ones people assume.
ExploreMap how work actually moves through the business, and where the delay and cost really sit.
ExploreUse historical data to estimate likely outcomes, with the accuracy stated honestly.
ExploreStatistical and machine learning methods applied to one specific business question, with their limits stated.
ExploreOptions, evidence and trade-offs laid out side by side, ending in a recommended next step.
ExploreTDRA is founder-led by Troy Dela Rosa. Before analytics, he spent more than a decade in retail operations, most recently directing a $2M+ location with direct responsibility for P&L, margin, payroll, inventory and staffing. He then trained formally in data science and machine learning.
The point isn’t to be the most technical person in the room. It’s to understand how the operation actually works, then use analytical methods to investigate the data behind the decisions.
The goal isn’t to build complicated models for their own sake. It’s to understand the business problem, investigate the data, find what matters, and turn the analysis into something a decision maker can actually use.
Two kinds of evidence, kept separate on purpose.
Operating experience
From more than a decade running retail operations.
Retail and business operations.
Direct responsibility for P&L, margin, payroll, inventory, staffing and day-to-day operating performance. The decisions were made with whatever numbers were on hand.
Annual revenue managed with direct P&L responsibility.
Roughly 11% EBITDA sustained through a corporate-directed wind-down, with about 40% gross margin and 20% payroll managed along the way. More on the About page
Applied analytics
From portfolio projects on public, public-style or self-collected data. Not client results.
ROC-AUC for a predictive conversion model on held-out data.
A two-stage model on a public-style GA4 dataset. The top tenth of scored sessions converted at about three times the base rate. Not a client deployment. Read the case
Applications coded in a funnel analysis.
Direct applications converted to interview at 9.7%, while one job board converted at zero. The same method applies to a sales or recruitment funnel. Read the case
TDRA doesn’t publish client outcome data. Client engagements are described by what they produced: the analysis, the finding and the recommendation.
Projects where a business question, the data, and the analysis meet: business analytics, machine learning and applied AI, each written up from the question to what it changed.
Have a business problem, a reporting challenge, a data question or an AI idea worth exploring? You don’t need to know which service you need. Start with the question.
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