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As it is in the natural world, data also experiences a lifecycle, from gestation through birth, metamorphosis, maturity and eventual death.
Effective data stewards spend a great deal of time ensuring that data quality is maintained throughout the DML. Data quality refers to data that is accurate, valid, complete, consistent, accessible, unique, timely, relevant and reliable. Ensuring effective data quality requires a comprehensive approach, integrating people, process and technology.
* Projects were not created timeously, meaning that when consultants captured their time, rather than ask for projects to be created, they would capture their time against a similar project for the same client. Realising that many of our issues stemmed from data quality processes over the data management lifecycle, we implemented incremental changes, focusing on all aspects of people, process and technology.
We created standard tasks that followed our development methodology for each key consulting area, and consultants were trained on how to capture their time accurately. Timesheet capture is now also a part of induction.
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