In today’s fast paced business world, where data insights are needed with a very short turn around time, it is important to not lose sight of the quality of the data. Also, with such a large amount of data available, big data can lead to big problems if quality assurance is not a strategic focus. Imagine this scenario: your client, an online radio service, tells you they need data driven support making a decision regarding which music licences should be acquired based on song popularity. The client also has a big meeting tomorrow with the record labels and needs a recommendation fast. In order to get these recommendations, you have to develop code to query the database of user listening data, establish filters to pull the data, then do an analysis, and finally make recommendations. It is easy to see how a very short turn around can lead to a number of issues during each of these steps.
In the next few weeks, we are going to address how to monitor quality at each of these steps, along with developing a comprehensive plan to deliver consistent and reliable analysis.
In the next few weeks, we are going to address how to monitor quality at each of these steps, along with developing a comprehensive plan to deliver consistent and reliable analysis.
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