What does “self-serve” in analytics mean to you?
As mentioned it means different things to different people and I recognise its ambiguity and definitional issues.
Currently, I think of it as infrastructure that allows business users to easily access, engage and query appropriate data from the warehouse (semantic or reporting layer) and customise or augment data from warehouse with their own data to create a new curated set.
I also see it as a spectrum as a opposed to a binary.
The infrastructure I spoke of consists:
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Analytics tools (e.g. Tableau, Amplitude)
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Context/documentation (e.g. data dictionary, analysis wiki pages)
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Resources for learning (e.g. courses, workshops, office hours)
Thinking about data culture and where it fits into self-service is an interesting question. Perhaps we can say a strong data culture increases the take up rate of self service analytics.
How self-service is analytics currently at your company?
Given the companies maturity and stage, confident it’s above average.
Currently we are using Tableau and Amplitude as our two key self serve tools.
In terms of other infrastrucutre we run Tableau workshops, provide data office hours, and hold a community of practice every 2 weeks for all business users interested in data.
We have light metadata on our data (on Tableau Online and YAML files) and interestingly no data dictionary as yet. Although it is something we are starting to consider.
How self-service do you want analytics at your company to be?
Make progress in getting business users comfortable so that a portion of ad-hoc requests can be done by themselves.
Also getting more predictive with our work. That is a number of functions in the business are in a good position to predict what will happen based on their data.
What are the barriers you’ve encountered to creating an empowered, self-service culture?
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Lack of data literacy and skills amongst business users
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Unaware of data that is available
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Keeping with the status quo
How important is data documentation in creating and maintaining a self-service culture?
As mentioned before have done little documentation, so perhaps a little unaware of the importance. Will have a better
Sense when we do increase documentation and notice (if any) difference.
But we have paid close attention to naming of events, dimensions, measures, tables and columns very very carefully.
Is there anything you’ve found to be particularly useful in creating a self-service culture?
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Data Community of Practice
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Weekly Business Reviews with leadership
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Onboarding workshops for BI tools
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Office hours
Currently working on a Data 101 course that covers data warehousing, data sensemaking (asking the right questions, systematically analysing the data from a number of angles, presenting results etc.), experiments, BI tools and more. Thinking it could be a mixture of in person delivery and also online course content.