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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. 

Internal requirements

  • avoid making bad decisions

  • improve operational performance

  • be more competitive by quickly reorienting the strategy

External requirements

  • satisfy customers

  • 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.

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Up-to-date

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Availability

Functional & technical

consistency

Traceability

Comprehensiveness

Security

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

  • Data enhancement

How to get quality data?

1

Manage with an integrated platform

Transactionnal database to ensure homogeneity and deduplication

Generalized audit and integrated documentation for full transparency 

2

Define the scope of the controls

Completeness and consistency check

Standard or customizable validation

Global or sampled control

Low or high alert thresholds

3

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 

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Check list

4

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

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Dashboard

5

Set up and monitor KPIs

Graphical display of executions

pre-and post-production

Follow KPI by portfolio or by process

Improve planning and control integration

KPI