Data Quality Management
Why checking data's quality?
In the short term, implementing data quality management has a cost. However, in the long term these costs are largely offset by operational gains and new business opportunities.
avoid making bad decisions
improve operational performance
be more competitive by quickly reorienting the strategy
comply with regulations
What makes quality data?
To evaluate the quality of a data, it is necessary to define it's purpose. However, for any data, a set of intrinsic characteristics must always be checked.
Functional & technical
To ensure the functional and technical consistency of the data, it is necessary to verify:
Validity of data format
Cleaning of non qualitative data
Homogeneity of data
Deduplication of data
How to get quality data?
Manage with an integrated platform
Transactionnal database to ensure homogeneity and deduplication
Generalized audit and integrated documentation for full transparency
Define the scope of the controls
Completeness and consistency check
Standard or customizable validation
Global or sampled control
Low or high alert thresholds
Validate at each stage of the process
When importing or encoding data
When changing or cleaning data
Before and after calculating analytics
When sharing or publishing reports
Automate controls regularly
Process scheduling by portfolio or by business
Frequency definition by complexity or by SLA
Error-based automation or 4 eyes validation
E-mail alert and user-friendly dashboards
Set up and monitor KPIs
Graphical display of executions
Follow KPI by portfolio or by process
Improve planning and control integration