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Data quality

When Bad Data Becomes a Management Risk

Data quality is rarely treated as a risk until a decision, a regulatory return or a budget is built on a figure that will not hold.

5 min read  |  Management intelligence briefing

Not all data quality issues matter equally

Every operational system contains imperfect data. The relevant question is not whether errors exist, but whether they change a decision, a payment, a statutory return or a stated position.

Treating all data quality issues as equally urgent guarantees that the ones which genuinely matter are not prioritised.

Judging materiality

Changes a decision

High

Fix first

Changes a payment

High

Fix first

Affects a return

Medium

Correct and control

Cosmetic only

Low

Log and monitor

Where the risk usually sits

In our experience the most consequential issues are rarely the most visible:

  • Records that are open in one system and closed in another
  • Dates recorded when the entry was made rather than when the event occurred
  • Free-text fields carrying information the reporting relies on
  • Categories that changed meaning part-way through the year
  • Manual overrides applied after extraction
  • Duplicate records created by a process change

The governance point

A data quality issue becomes a management risk at the moment a figure derived from it is published, funded or defended. From that point it is no longer a systems problem — it belongs on a risk register with a named owner and a remediation date.

Fix the process, not just the record

Correcting individual records without addressing the point of capture produces the same errors next quarter.

Sustainable improvement usually comes from validation at the point of entry, clearer definitions for frontline staff and a small set of automated exception reports that surface problems while they can still be corrected.

Questions for a leadership team

  • Which published figures would change if known data issues were corrected?
  • Do you know the completeness rate of the fields your KPIs depend on?
  • Who is accountable for data quality in each operational system?
  • Are data issues visible to the board, or only to analysts?
  • How long does a known error take to be corrected at source?

Potential management response

  1. 01Rank data issues by decision impact rather than by volume
  2. 02Add material data quality risks to the corporate risk register
  3. 03Introduce exception reporting on the fields KPIs depend on
  4. 04Validate at the point of capture rather than correcting downstream

Any figures shown are illustrative and used to demonstrate an analytical approach. They do not describe a real Coreridge Solutions client.

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