Why poor-quality data costs more than it seems, how to estimate it, and why investing in data is, at its core, risk management.
Few things are as expensive and as invisible as poor-quality data. There is no invoice that says "wrong data", but its cost spreads across the organisation as lost hours, wrong decisions and distrust in the numbers.
A widely accepted quality principle: the cost of fixing an error multiplies the further it advances. An error caught at the source is minor; the same error found in a leadership report may already have driven a costly decision.
The most effective lever is validating at the entry point so bad data never enters, complemented by deduplication, normalisation and automated quality rules. Assigning data owners per domain and monitoring quality indicators turns a diffuse cost into a managed risk.
Preventing a data error at the source is always cheaper than fixing the decision it caused.
Poor data quality has a real but invisible cost: wrong decisions, rework, lost hours and eroded trust. The cost of an error grows the later it is caught, so validating at the source is the most profitable investment, backed by owners per domain and continuous quality monitoring.
Approximately yes: by summing hours reconciling data, rework, errors with economic impact and opportunities lost to poorly informed decisions.
To prevent. The cost of fixing grows the later an error is caught, so validating at the source is the most profitable investment.
The business defines what a correct data point is and names owners per domain; technology provides the automation to measure and maintain quality.
Because no invoice says "wrong data". It spreads across lost hours, rework, bad decisions and distrust, rarely attributed to its real cause.
The later an error is caught in the chain, the more expensive it is to fix — minor at the source, costly once it has driven a decision.
Validate at the entry point, deduplicate and normalise, automate quality rules, and assign data owners per domain who monitor the indicators.
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.
