Your cost model can only be as good as the data that feeds it. This question assesses whether your organization's data infrastructure enables effective cost and profitability analysis, or whether siloed systems and manual processes create a practical ceiling on analytical capability.
The single largest barrier to effective cost management is not methodology or willingness. It is data. Research identifies siloed departments as the number one obstacle, cited by forty-six percent of organizations, followed by outdated technology tools at thirty-nine percent. When cost-relevant data lives in disconnected systems across finance, operations, sales, and supply chain, building an accurate cost model requires manual extraction and reconciliation that is slow, error-prone, and unsustainable.
The scale of the data challenge is significant. A financial services organization implementing TDABC reported working with a database exceeding one terabyte, encompassing fifty million transactions from over three million clients. Even a mid-market manufacturer typically needs to integrate data from ERP, CRM, production scheduling, warehouse management, and payroll systems to build a complete cost picture. When these integrations do not exist, the finance team spends eighty percent or more of its time collecting and reconciling data rather than analyzing it.
The practical impact is that data infrastructure maturity sets the ceiling for every other dimension of the Health Check. An organization can have sophisticated cost allocation methodology, but if the data to feed the model requires three weeks of manual preparation, the model will be updated infrequently and become stale. Advanced scenario modeling is impossible if the underlying data cannot be refreshed quickly. The transformation from thirty-three days to three to five days for profitability reporting demonstrated in one implementation was primarily a data infrastructure achievement.
Question 13 evaluates the state of your data infrastructure specifically for cost and profitability analysis. Each level represents a different capacity to feed, maintain, and leverage cost models.
Answer: “Our data lives in silos and requires manual extraction and reconciliation for any cost analysis.”
Cost-relevant data is scattered across disconnected systems with no automated integration. Building a cost analysis requires manually pulling data from multiple sources, reconciling inconsistencies, and assembling it in spreadsheets. This process is time-consuming, error-prone, and limits the frequency and granularity of analysis. The finance team spends most of its time on data collection rather than insight generation.
Example from the Health Check: A manufacturer needs production volumes from the MES system, cost data from SAP, customer orders from the CRM, and labor data from the payroll system. Each extraction is manual, formats differ, and reconciliation takes two weeks before any analysis can begin.
Answer: “We have a basic ERP system but cost data integration with other systems is limited or manual.”
A central ERP system exists and provides the core financial and transactional data. However, operational data from production, warehouse, CRM, and other systems either is not integrated or requires manual bridging. The ERP provides standard cost reports, but building multi-dimensional profitability analysis still requires significant manual effort. Cost models use the ERP as a primary data source but supplement with manual data from other systems.
Example from the Health Check: A company runs SAP for finance and procurement but uses separate systems for production scheduling and customer management. Monthly cost analysis pulls general ledger data from SAP automatically but adds production volumes, customer delivery data, and service hours from separate spreadsheet exports.
Answer: “We have business intelligence tools that integrate some data sources, but our cost models still run in spreadsheets alongside the BI platform.”
The organization has invested in business intelligence tools that consolidate data from multiple sources for reporting and visualization. However, the actual cost modelling still happens in spreadsheets, using BI data as input. This creates a disconnect between the reporting layer and the analytical layer. BI dashboards show what happened, but the cost models that explain why and what to do about it operate separately.
Example from the Health Check: A services company uses Power BI dashboards fed by a data warehouse that integrates ERP, CRM, and project management data. Financial controllers export data from Power BI into Excel-based cost models to run profitability analysis. The models are powerful but disconnected from the live data infrastructure.
Answer: “We have a fully integrated data platform where cost models are fed automatically from operational systems, with AI and machine learning enhancing analysis.”
Data flows automatically from ERP, CRM, production, warehouse, and other operational systems into an integrated costing platform. Cost models are maintained within the platform rather than in spreadsheets, enabling automated updates and real-time analysis. AI and machine learning capabilities enhance pattern detection, anomaly identification, and predictive analytics. The finance team focuses on interpretation and strategic insight rather than data collection and model maintenance.
Example from the Health Check: A financial services firm operates an integrated platform processing fifty million transactions from three million clients. Cost models update automatically with each data refresh. Machine learning algorithms flag unusual cost patterns and identify emerging profitability trends. The finance team produces weekly profitability insights that were previously only available quarterly.
| Industry | Typical Level | Key Insight |
|---|---|---|
| Manufacturing | Level 2–3 average | ERP systems are common but integration with production systems and cost models is the gap; manufacturers with IoT data feeds are beginning to achieve Level 4 capabilities |
| Healthcare | Level 1–2 average | Clinical and financial data systems are often deeply siloed; interoperability challenges make integration one of the most difficult problems in healthcare cost management |
| Financial Services | Level 2–3 average | Transaction data volumes are massive; the institutions that have achieved Level 4 have invested heavily in data warehousing and automated ETL pipelines specifically for cost analysis |
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