Data-centric projects are becoming both bigger in size and complexity, which makes execution that much more difficult. This not only creates delays in project execution but also results in poor data quality. More and more projects are facing:
- Longer time to Market – The time required for projects is increasing, with many cloud data migration projects having multi-year timelines.
- Delayed or failed projects – Data teams are underestimating the complexity of the data projects resulting in last moment surprises as well as cost over runs.
- Poor Data Quality – Projects are delayed due to testing issues that are discovered too late in the project lifecycle.
- User dissatisfaction and Complaints – Data quality is an afterthought, resulting in high rates of user dissatisfaction.
- High Production Cost Fixes – Lack of test automation has resulted in lots of refactoring or patchwork in production.
- Testing on big data volumes – The large volumes have made is generally impossible to test the data manually.
- Regression testing nearly impossible – After the delivery of the project, code revision or ETL processes require complete regression testing. However, these concepts are missing in the data engineering side.
Costly Manpower – The manual and repetitive tasks are still not automated and either requires manual work or custom coding, which often will take highly skilled talent off of other critical work.