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Why Some Self-Service Analytics Deployments Fail

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Today I have the pleasure of introducing Nick Akincilar as our guest blogger. 


nick.pngNick is a Principal Pre-Sales Solutions Architect based in the Philadelphia area with over 20 years of experience in both pre-sales and technical roles. Nick’s expertise ranges from data & analytics, to business workflow automation & coding, within various verticals markets. He truly enjoys the challenge of being the liaison between executives, business users & technical teams. Nick loves tech and is passionate about helping large organizations become more effective using all of their data through the power of modern analytics. We appreciate his valuable contribution to the Qlik Desigbn Blog. Thank you Nick!

In this multi-part blog, Nick shares some areas where self-service analytics deployments fail and how Qlik's analytic platform addresses them. Take it away Nick!

Self Service Analytics

Have you ever try to do something thinking you knew how, but ended up failing in the end?  This is frequently what happens when self-service BI is implemented based purely on past SQL, Reporting and traditional BI experiences.

As a principal BI solutions architect, it is part of my job to listen to companies looking at self-service BI including educating them on what I have learned from listening to others, especially on what can go wrong!

Self-service analytics is a way to allow business users & data analysts to answer their own questions by giving them access to various data sets and intuitive tools that can blend, filter & visualize these data sets. If you are planning to roll out a self-service analytics project to a large user base then read the attached PDF document that covers areas where self-service BI within larger scales can and will fail when using traditional query-based solutions.

Have a comment or question? Post it below and Nick or myself will be happy to address it for you.



great post. I've shared this with my customers. when can we have Part2????
Valued Contributor II

If the dashboard in the screenshot is indicative, that particular deployment will fail because it completely fails to follow basic design guidelines... (-;


In my experience, the single biggest reason self-service implementations fail is because the users are unwilling to simplify the data set to the point where self service is feasible. Self-service and complex data sets simply don't mix well - the users can't be expected to know how to manage complex situations such as set analysis or advanced aggregations, yet these are typical requirements for a quality dashboard.