OKR template to enhance the quality of data through augmented scrubbing techniques
This OKR centers on improving data cleanliness using advanced scrubbing techniques. A major objective, denoted in the title, indicates a particular focus on improving data quality through these modernized methods. It makes mention of explicit targets and initiatives to accomplish the goal.
The first outcome sets a goal of training 80% of the data team on newer, robust data scrubbing methods. This will occur through the identification of team members for training, the scheduling of detailed lessons focusing on newer robust data scrubbing techniques, and subsequent enforcement of regular assessments to ensure successful training execution.
The second aimed outcome is a reduction in data scrubbing errors by 20%. This will be achieved through implementation of strict error-checking procedures, utilizing automated data cleaning tools, and providing comprehensive training on data scrubbing techniques to the entire team to prevent human errors.
The third and final outcome is the design of three new data scrubbing algorithms before the quarter ends. Achieving this will involve best practice research, careful design, and coding of the new algorithms. Following creation, these algorithms will be tested and applied to existing data sets.
The first outcome sets a goal of training 80% of the data team on newer, robust data scrubbing methods. This will occur through the identification of team members for training, the scheduling of detailed lessons focusing on newer robust data scrubbing techniques, and subsequent enforcement of regular assessments to ensure successful training execution.
The second aimed outcome is a reduction in data scrubbing errors by 20%. This will be achieved through implementation of strict error-checking procedures, utilizing automated data cleaning tools, and providing comprehensive training on data scrubbing techniques to the entire team to prevent human errors.
The third and final outcome is the design of three new data scrubbing algorithms before the quarter ends. Achieving this will involve best practice research, careful design, and coding of the new algorithms. Following creation, these algorithms will be tested and applied to existing data sets.
Enhance the quality of data through augmented scrubbing techniques
Train 80% of data team members on new robust data scrubbing techniques
Identify specific team members for training in data scrubbing- Schedule training sessions focusing on robust data scrubbing techniques
- Conduct regular assessments to ensure successful training
- Reduce data scrubbing errors by 20%
- Implement strict error-checking procedures in the data scrubbing process
- Utilize automated data cleaning tools to minimize human errors
- Provide comprehensive training on data scrubbing techniques to the team
- Implement 3 new data scrubbing algorithms by the end of the quarter
- Research best practices for data scrubbing algorithms
- Design and code 3 new data scrubbing algorithms
- Test and apply algorithms to existing data sets