At Data Culpa, we’re building solutions to help data engineers and data scientists catch data quality problems before they jeopardize data pipeline results.
Nearly every business today is becoming more data-centric. Data not only drives operations and decision making; it’s also often the thing that distinguishes a great product or service from lackluster competitors.
Of course, raw data isn’t the only type of data that’s important for businesses. Data analysis is critical, too, transforming raw data into useful information– or as Peter Drucker put it, into “data endowed with relevance and purpose.” And data analysis is only as good as the data being analyzed and the algorithms being applied.
As data pipelines become more sophisticated and complex, data quality problems become more difficult to detect. These problems could take the form of a sudden change in data schemas. Or they might take the form of a a slow, almost imperceptible corruption of data values – a problem known as “data drift.”
We’re building solutions to detect these problems quickly, so data scientists and data engineers correct their pipelines before losing days or even weeks of work. Ultimately, our goal to make sure that the results of data analysis are worthy of being applied in the lives of everyday people.
In this blog, we’ll present our own thoughts on data quality as well as the thoughts and best practices of industry experts.
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