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Build data validation rules directly into ingestion layers so that insufficient data is stopped at the gate and not detected after damage is done. Use lineage tooling to trace data from source to report. Understanding how datatransforms and where it breaks is crucial for audibility and root-cause resolution.
Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. The CEO also makes decisions based on performance and growth statistics. Who are the data owners?
This is also an important takeaway for teams seeking to implement AI successfully: Start with the keyperformanceindicators (KPIs) you want to measure your AI app’s success with, and see where that dovetails with your expert domain knowledge. What datatransformations are needed from your data scientists to prepare the data?
What if, experts asked, you could load raw data into a warehouse, and then empower people to transform it for their own unique needs? Today, dataintegration platforms like Rivery do just that. By pushing the T to the last step in the process, such products have revolutionized how data is understood and analyzed.
It provides the KeyPerformanceIndicator that I consider to be the holiest of the holy in web analytics: Task Completion Rate (segmented by Primary Purpose). I am forgetting the other 25 features these tools provide for free. 4Q is a " site level survey. " If you want to see how 4Q looks and works click here.
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