Data Quality Management is a set of practices that aim at maintaining a high data quality within an organization. It incooporates the acquisition, maintenance, disposition and distribution of data. Efficient Data Quality Management needs a combination of people, processes and technologies to harmonize in order to improve the data quality.
At Sigma, we have established a Data Quality concept called DQ by Sigma. This is our well-proven process for identifying and eliminating data quality problems for our customers.
These are the steps we follow in DQ by Sigma:
- Measurement of data quality maturity
- Data flow and process analysis
- Data classification and inventory
- Identify business rules – what should the data look like?
- Data quality measurements – what does it look like?
- Which deviations are most important to address?
- Root cause analysis – we find the root cause of the deviation
- Improvement proposal
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