This document discusses how an organization transitioned from an idea of data-driven decision making to a practice of it within a few months by adopting the self-service business intelligence tool Metabase. It provides a case study comparing Metabase to Tableau in terms of user access, usability, data discovery capabilities, and facilitating data democratization. Key benefits of Metabase included its ease of use, low cost, and ability for any employee to create and share dashboards and metrics without needing specialized training or tool expertise. While not perfect, Metabase was effective for the organization's needs and helped increase weekly active usage among approximately 200 employees.

1. Self service BI for humans
Formerly for analysts

2. Why am I presenting this?
Because a data driven culture, where management and operational personnel
consume data to optimise what they are doing is a goal, not a reality.
In our mission to help organisations make data driven decisions, there are many
obstacles.
Data access, tool knowledge, and acceptance of data are 3 obstacles where
metabase is particularly good at helping organisations in their journey.
Onward, I present how this tool was used to bring an organisation from an idea of data
usage, to a practice of data usage in the space of a few months.

3. Requirements for making data driven decisions
The Fogg behaviour model
dictates that in order to take
action, a motive, opportunity and
means are necessary.
In data this translates to:
● Goals (quantifiable goals)
● Ability (to analyze data)
● Availability (access to data)

4. What can you (the data person) do to facilitate data
driven decision making?
Goals Ability Availability
● Track goals and
process steps
● Make analysis easy
● Educate decisionmaker
in analysis techniques
● Teach self service tools
● Choose easy to use
self service tools
● Access for everyone
● Easy access
● Data marts or easy to
use reports
● Also non conformed
data access for the
exploratory analysis
inclined (eg, product
team can QA directly
on a production dump)

5. The usual suspects that prevent data
democratisation● High complexity to use the tools which leads to difficult training
● Lack of accessible documentation of data or complexity to access documentation (dictionary link not
easily reachable from point of data consumption
● Lack of or out of date documentation of tool (eg, tableau)
● Management slow to approve expense for licenses, since management is not the direct stakeholder
of data access for the masses.
● Management view that data should be hidden from employees for fear of leaks.
What to do?
Get a free, easy to use tool, with built in data dictionary and good documentation.

6. Why Metabase?

7. Case Study: Urban Sports Club
Tableau vs Metabase access
● 5 user accounts
● 4 of them for management
● ... of which 3 never logged in
● Further accounts pending
management approval, to be
discussed when it becomes of
interest to management.
● 200 view users + some
analyst/admin accounts cost
40k/year
● Accounts for all operational users
● User generated content
● 200 full access users cost 1-2k/year
in hosting cost
● Account creation and tool training
during employee onboarding.
● No approval needed, no
bottlenecks
● Management joined last (with
exceptions)
Tableau Metabase

8. Case Study: Urban Sports Club
Tableau vs Metabase Usability for analysts
● Offline files
● No versioning - people can and do fuck up.
● 5-10 min to save/upload turns into a 20min
watercooler chat/reddit binge
● Random crashes resulting in 30min lost
work
● 1-2h to create/download 12 dimension/filter
splits of a tabular view
● License management, user management
● Account types (viewers cannot edit)
● Outdated documentation
● Fully online
● Versioning and rollbacks
● Seconds to save
● Random crashes, work not lost
● 15min to create/download 12 splits
● Easy user management, no
licenses etc (just access/rights
groups)
● Up to date documentation
Tableau Metabase

9. Case Study: Urban Sports Club
Tableau vs Metabase usability for business
user
● 3-4 people 2x 60min sessions for
basic usage
● Designated analyst accounts can
create dashboards and charts
● No documentation/data dictionary
● Drill down
● Filters keep getting changed by
analysts
● 6-8 people 45min for basic usage
(including slack/email alerts
creation)
● Anyone can create charts, publicly
or privately
● Centralised metric definition with
documentation
● Data dictionary for reports
● No drill down, need to add other
dimension.
● Versioning for filters
Tableau Metabase

10. Case Study: Urban Sports Club
Tableau vs Metabase data discovery
● Search curated dashboards, find
topics
● Useful for finding something you
know exists
● Find questions among thousands
user generated questions, from
custom sql to various pivots with
auto naming
● Useful for searching by keyword in
large amounts of user generated
questions (discovery, eg, search for
‘churn’ and find everything about it)
● Centrally defined metrics accessible
through ask question wizard
Tableau Metabase

11. Case Study: Urban Sports Club
Tableau vs Metabase data democratisation
● Only analyst users can create
content
● Anyone can create and save
content in private or public spaces.
● Admins can curate data
dictionaries, segments and metrics,
to have an organisation-wide metric
definition
Tableau Metabase

12. Case Study: Urban Sports Club
Tableau vs Metabase usage statistics
● Can access own server usage
statistics after some configuration
heavy process
● Accessible by admin
● Postgres db containing:
● Users
● Refreshes
● Query duration
● Queries per day
● Downloads
● Top questions
● Queries or objects created by users
● Basically anything that exists in the
db
● Can create usage competitions :)
Tableau Metabase

13. How did we do it?
● Bottom up
● Ninja slack pushes
● Usage competitions with rewards
● Regular training sessions upon introduction (to 30% of
company, the rest picked it up from the trained ones)
● Training on onboarding to new employees
● Remainder of company followed

14. When did we
do it?
Approx 6
months span
*cumulative metrics are vanity,
feelgood metrics,
** they feel pretty good

15. How did we do?
We did pretty well, and so did the approx 200 employees. 30% of
company active weekly.

16. Is Metabase perfect?
No, but in data we learned to live with what works
● It crashes regularly on heavy loads, but comes back by itself. Work progress
is not lost because of URL parameterization.
● Basic visualisations only, but covers the vast majority of cases of
visualisation that add value. The trade-off for speciality visualisation is
probably not worth the extra complexity of something like tableau on an
organisation level.
● Has occasional bugs in axis labels, etc, that can be a little annoying, but not
game breaking.

17. Is it value yet?
Part of the journey.