In a self-service analytics model there should be a separation of concern between data delivery and end user data consumption. This separation closely mimics the difference between the value of highways and surface streets in urban transportation: highways take you 90% of the way and then you exit and spend the remaining 10% of the trip home on surface streets.
The same paradigm should be seen with data delivery in a self-service model where integration engineers and DBAs (traditional data professionals) are like the highway and end users are the surface streets. The data professionals are responsible for getting data 90% of the way home, then end users-- who are increasingly data and tech literate-- are responsible for taking it home that last 10%.
The lesson here is there can be – rather there should be – a balance struck in self-service data analytics models. No highway takes me directly to my home and no surface street takes me efficiently either. In order to get home as quickly as possible, I have to use both but in the correct balance. So too must you wisely and efficiently leverage your data team and your end users to efficiently deliver data home.
This article was originally published on LinkedIn by John Heisler on February 18, 2020.
https://www.linkedin.com/pulse/data-delivery-self-service-world-lessons-from-highway-john-heisler/