One of the most common barriers to implementing a proper cost model is the belief that you don’t have the data. Most of the time, that belief is partly right, but the gap is smaller than it appears.
Understanding what data you actually need (vs. what you think you need) is the first step.
The Three Data Requirements of TDABC
A TDABC model requires three types of data:
1. Financial data - cost pool totals. The total cost of each resource pool (department, team, or function) over a period. This comes from your chart of accounts. Almost every company has this, either from their ERP, accounting software, or ERP export. This is rarely the constraint.
2. Operational data - transaction volumes and types. The number and type of transactions processed by each resource pool: how many orders fulfilled, clients onboarded, invoices processed, quality checks performed, etc. This is the data that many companies lack at sufficient granularity.
3. Time estimates - how long each transaction type takes. The time required per transaction type in each resource pool. This does not require extensive historical data: a combination of system logs, operational metrics, and structured estimates from process owners is sufficient.
The Minimum Viable Dataset
You do not need a data warehouse, a business intelligence platform, or a full ERP to build a useful TDABC model. You need:
- A general ledger or chart of accounts segmented by department (to build cost pools)
- An operational report or manual count of key transaction types by department
- Time estimates per transaction type, validated with department heads
CostCtrl is designed to work with exactly this, plus ERP exports for companies in PT/ES/BR, which provides the financial layer automatically.
The Common Data Gaps (and How to Bridge Them)
Gap 1: No transaction-level operational data
Solution: Start with a 2-week activity sampling exercise. Department heads record their team’s activities in 30-minute blocks. This gives you an initial dataset sufficient for the first model iteration.
Gap 2: No time tracking by client or product
Solution: Use system logs as proxies (number of emails, tickets, orders) combined with time estimates per event type. Imprecise? Yes. Better than no model? Always.
Gap 3: No ERP / chart of accounts by department
Solution: Map your existing accounts to cost pools manually. A one-time exercise that takes 1-2 days for a mid-market company. Document the mapping so subsequent periods are automated.
Data Maturity as a Journey
The goal is not to achieve perfect data before building a model. The goal is to build a model with imperfect data, and then use the model to identify where data quality improvements will have the most impact on decision accuracy.
Often, 80% of the value comes from 20% of the data. The model shows you which 20%.
CostCtrl has a built-in data maturity assessment that tells you exactly which data gaps limit your model’s precision, and prioritises the gaps worth closing in the next 90 days.
Take the Profitability Health Check to see your current Data & Technology score and get a practical data readiness roadmap.
Frequently asked questions
What data do you actually need to build a cost model?
Three types: financial data (cost pool totals from your chart of accounts), transaction data (orders, invoices, shipments - what happened and for whom), and operational data (the time or driver behind each activity). Most companies already have the first two; the third is usually the smallest real gap, not the blocker people fear.
Do I need perfect data before modeling costs?
No. Perfect data is not a prerequisite. A useful TDABC model starts with the data you already export from your ERP and accounting system, and the gaps are mapped as part of the build rather than fixed first. Waiting for perfect data is the most common reason companies never start, and it is almost always unnecessary.
Do I need a new system or data warehouse first?
Usually not. The exports you already have - general ledger, transaction logs, basic operational records - are enough to build a first model. A dedicated data warehouse helps at scale, but it is an optimisation, not a precondition. The first model proves value and shows exactly which data is worth investing in next.
How does TDABC work with imperfect or partial data?
TDABC uses time equations, so it can model an activity from a small number of observed times plus the transaction volumes you already record. Where data is missing, reasonable estimates are used and flagged, then refined as better data arrives. This makes the method robust to the partial data that real companies actually have.