The impact of common data quality issues – Orgvue Knowledge Base

Actionable insight requires accurate data. When errors remain hidden your analysis is compromised and your credibility is at risk. This article summarizes the impact of common data quality issues.

Issue	Impact
Broken Hierarchies: Positions are disconnected (orphaned) from the reporting hierarchy due to issues with the ID/Manager ID.	Unable to visualize a single, complete reporting hierarchy (see illustration below). Span of control for individual managers is misreported. ‘Rolled-up’ values for teams and sub-groups are inaccurate due to ‘orphans’ being excluded.
Inconsistent category values: Properties used to define subgroups for analysis and reporting have inconsistent values (e.g., USA, US, America all existing as Country property values).	Subgroup totals are inaccurate. Unable to get the value of ‘slicing and dicing’ the organization to identify where action is needed.
Missing values: datasets are incomplete with some records having blank or missing property values.	With numeric properties: calculations result in incorrect values (e.g. total workforce cost) as records with blank values are excluded from totals. The impact can become extreme if a number (e.g. ‘0’) has been used to denote missing values. With Text properties: Subgroup totals are inaccurate as records with blank values are excluded.
Extreme numeric values: due to poor data management and/or governance, some properties have extreme values, e.g. 999999999	Calculations result in incorrect values due to extreme values being included.