From the course: Data Ingestion with Python
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KPIs
- [Instructor] You're going to monitor your data pipeline, and as time goes, you'll have more and more metrics and it will be hard to look at all of them at once. Also, some metrics will be more low-level, and others might be computed. To make sense of all of this, you need KPIs. KPI stands for key performance indicator. These are metrics that have direct impact on business, and ones we deem important. The actual KPIs will vary from company to company, and from one team to another. Make sure to have some. If your company doesn't have data KPIs, go ahead and start the process. Remember, that you're the customer of the data, and you should have a saying about its features and quality. KPIs should be smart, specific, measured, attainable, relevant, and time framed. For example, say you're writing a fraud detection system. You have data flowing from several sources, and you're running your models on this data. A KPI can be millions of records processed per day.
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