Best practices for getting started and driving adoption with tableau

@AlanMorte @ThreeVentures #SDVM
Sac. Data Viz. Meetup
@AlanMorte
Director of Analytics
@ThreeVentures
amorte@threeventures.com
Best Practices With Getting
Started & Driving Adoption
With Tableau
#SDVM

Before we get
started
● Thank you for coming, sponsors
● What the presentation assumes
● What’s the goal of this presentation
● Data is hard, focus on 3P’s
○ People, process, platforms
● Presentation will be emailed to meetup
members

Presentation assumptions

You have a
Very basic understanding of...
● Tableau desktop
○ IE: sheets, dashboards, & stories
● Tableau online
○ IE: you can publish visualizations to this
web service portal.
● Dimensions, measures (groupings / metrics)

The goal of this presentation

Introduce you
to a proven
framework
Which forces you into best practices
for data analytics projects, and can be
used to perform the following:
● Getting started with Tableau
● Generating business value with Tableau
quickly
● Driving adoption with Tableau

Tableau Desktop 101 (quickly)

Tableau
desktop
A desktop application which you hook
up to data sources and build
visualizations in.
● This is a sheet
● A sheet is where you build one visualization

Tableau
desktop
visualizations in.
● This is a dashboard
● A dashboard is where you combine multiple
sheets together for analysis by a user

Tableau
desktop
visualizations in.
● This is a story
● A story is where you create a tabbed
presentation to walk an end user through an
analysis using sheets or dashboards

Tableau Online 101 (quickly)

Tableau
online
A web service which houses your
visualizations in the cloud where team
members can access interact with
them via a portal.
● This is Tableau online
● Projects house all visualizations associated
with it
● User access best controlled in groups at project
level

Tableau
online
them via a portal.
● Click on a project, and you can access
workbooks or associated views
● Users can then click a workbook / view and see
an interactive dashboard...

Tableau
online
them via a portal.
● Like this executive dashboard for profitability

Meet the data analytics
lifecycle framework

Data analytics
lifecycle
framework
Forces you into best practices for
Tableau using an iterative approach.
Data
analytics
lifecycle
1.
2.
3.
4.
5.
6.

Why use this
framework
Which forces you into best practices
for Tableau?
● Helps decision makers understand the
resources required to execute a project
● Forces you to define clear details and
definitions for what business value Tableau
adds to your organization
● Ensures that teams do the appropriate work
up front, and at the end of projects, in order to
succeed with Tableau

How to apply the data
analytics lifecycle
to Tableau

Focus on the
following
● Roles / responsibilities needed for the data
analytics lifecycle
● Defining each phase of the lifecycle for your
Tableau project

Roles / responsibilities needed
for the data analytics
lifecycle

Roles /
responsibilities
needed
For the data analytics lifecycle
framework.
Role Responsibility
*Business user Has domain area expertise
*Project sponsor Identifies the business problems to solve
Project manager Ensures the project meets defined objectives
BI Analyst Has deep understanding w/ data to be used
DBA Provides needed environments, access
Data Engineer Provides technical skills - think ETL
Data Scientist Knows analytic techniques & modeling

Defining the phases of the data
analytics lifecycle for your
Tableau project

Phase 1 -
Discovery
This is where you plan your Tableau
project.
1. Domain experience - focus on an area of
expertise
2. Identify the business need: use domain
experience to identify a business need
3. Draft an analytic plan -
a. Audience
b. Problem definition
c. Questions to be answered
d. Data needed
e. Outcomes to be produced

Phase 2 -
Data Prep
This is where you assess and prepare
the data needed.
1. Assess - do you have enough good data to
answer your business problem
2. Create Clarity - matching technical data
definitions to business jargon
3. Explore - are you familiar with the data you’re
using
4. Prep - transform your data as needed to
answer business problems, questions

Phase 3 -
Model Plan
This is where you select what
visualizations answer your questions
best.
1. Revisit your plan - what do your
visualizations need to answer
2. Understand inputs - what data is required to
answer your questions
3. Identify dimensions / measures - how many
of each do you need to answer a question
4. Assess visualization options - which
visualizations can you use given the number
of dimensions, measures - more on this at end of
presentation
5. Sketch it out: draw out your visualizations
and dashboards - do they make sense

Phase 4 -
Model Build
This is where you build visualizations
in Tableau.
1. Start small - build out one visualization at a
time
2. Attention to detail - focus on colors, sizes,
labels, annotations, axis, and aggregate
functions that make sense
3. Assess what you built - does it look accurate,
make sense, avoid intolerable mistakes, need
more data...

Phase 5 -
Communicate
Results
This is where stakeholders assess if
the visualizations produce the desired
outcomes.
1. Involve the right roles - business users and
project sponsors assess the outcomes
2. Assess outcomes - do the visualizations
answer the defined questions, produce
desired outcome
3. Did you fail / learn? - identify & document
what’s missing, go back to phase three
4. Did you find success? - identify & document
key findings & major insights, move to phase
six

Phase 6 -
Operationalize
This is where the visualizations are
integrated into day-to-day operations.
1. Communicate benefits - w/ project sponsors,
business users, & first adopters
2. Use a pilot project - deploy visualizations in a
controlled way w/ Tableau Online
3. Manage risk - by starting small, mastering
feedback loops and updates before deploying
department / company wide
4. Drive adoption - communicate value & how to
use w/ stakeholders & users via
presentations & weekly standups

Summary

Data Analytics
Lifecycle
Summary
Phase Key Takeaway
1. Discovery Plan your Tableau project
2. Data Prep Assess and prepare data needed
3. Model Plan Select the right visualizations
4. Model Build Build the visualizations
5. Communicate
Results
Stakeholders assess if project produces
desired outcomes
6. Operationalize Integrate visualizations into day to day
operations

Assessing Visualization Options

Periodic
Table of Data
Viz.
http://www.visual-literacy.org/periodic_table/
periodic_table.html

Assess your
options
Understand how many measures / metrics
each viz needs to identify if you can use it to
answers a question from your data analytic
plan.
Simply roll your mouse over the one you like.

Q&A

Thank you
@AlanMorte
Director of Analytics
@ThreeVentures
amorte@threeventures.com
#SDVM

Best practices for getting started and driving adoption with tableau

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