Looker: Vendors You Should Know (2017)

VYSK - Looker Image.png
VYSK - Looker Image.png

Looker: Vendors You Should Know (2017)

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[August 2017]
 Part of NextWave Business Intelligence's
"Vendors You Should Know" series.

This six page paper covers the essentials that data product teams need to know about Looker's embedded analytic capabilities. Focused on providing an overview to product teams rather than technical architects, this paper covers Looker's use cases and key features, the pricing model, who it's for, and who it isn't for.


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When I built my first data product, I was at a loss as to where to begin the journey. So, like many, I started by doing as much research as possible, seeking out every analyst report I could find on the options for analytics.  The problem was, there just weren’t very many resources for data product builders. Most of the reports available at that time were focused on “enterprise” analytics—those analytics designed for implementing analytics inside the business for Sales, Marketing, Operations, etc. There was a lack of material for leaders seeking to discover the options for building analytics for customers. For those looking to turn idle data into actionable insights and hopefully, generate a little revenue along the way.

Unfortunately, the times haven’t changed much, and while more materials on data products and product-oriented embedded analytic platforms exist, most are written by analysts, not practitioners. The information provided is quite good, but it lacks the clarity that comes from having built data products yourself, from having lived through all of the worries associated with taking the journey from strategy to design to implementation to operations management. That is why this series of papers exists—to give you the perspective of a practitioner who has been through the process themselves.

This series is designed to help data product teams understand the options for building great data products on the market today. It’s not a comprehensive survey of all of the platforms available—it’s a curated list of what I believe to be the best choices based on what I’ve learned building data products and helping others through the process.

I hope you find it useful.