Self-service business intelligence (BI) is all the rage. If Google analytics is any indication, it wasn’t until January 2015 that the keyword “self-service BI” appeared consistently on its search radar. There are a lot of claims that point toward self-service BI being the magic bean for business users trying to make sense of huge data volumes.
What self-service can resolve (in a way):
The stalk rising from this magic bean is a fast track highway that enables business users to help themselves to information they need without any dependency on more tech savvy folks, or IT departments. Tableau and Qlik are examples of companies who claim to fulfill this highly sought need. No more waiting for IT to pull reports, no more waiting on tech savvy developers and coders to decipher volumes of business intelligence data that you have accumulated–but have no clue how to digest. Now, you can just go in yourself and pull beautiful visualizations that turns terabytes of raw data into meaningful, and presentable, information any business person can digest.
What story is the data is telling? Self-service models fall short in answering this question.
What self-service mostly does not resolve:
As convenient as it may be, self-service BI is insufficient when on its own. Yes, it resolves the issue of dependency, but in what way? In one of our white papers on dbseer.com, we discuss the ideal framework for maturing your analytics platform. It speaks to two approaches: the descriptive versus the diagnostic approach to understanding analytics.
The diagnostic approach & its insufficiencies
Self-service BI can easily fulfill a diagnostic approach. (For more on the framework for understanding your analytics, see this paper). In the diagnostic approach, you can slice and dice the data on your own. However, the descriptive approach that answers the where, what, and hows of your data cannot be so easily fulfilled in this way. What is the story the data is telling? Self-service models fall short in answering this question. After all, there is an expertise data scientists have to manipulate and extract information from data that many end-users may not have. The opportunity to unveil and attain these findings is lost if and when business end-users purely rely on self-service offerings.
The Better Solution:
In no way am I suggesting we get rid of self-service BI. There is a well-established need for it and SaaS vendors in the BI world should definitely offer it. However, the better solution is to use self-service solutions as the exception, and not the rule for your business intelligence needs. There is a lot of business intelligence to be attained that self-service solutions are incapable of unearthing.
Jack set his eyes on what was not his for the taking when he climbed the magic beanstalk, the golden egg, the harp, the coins. I’m not claiming you can’t have them (!), I’m just saying you should get the goods through the appropriate means, rather than necessarily helping yourself in the dark!