This document discusses best practices for effective data visualization. It defines data visualization and explains that it involves creating visual representations of data to clearly communicate information. The document outlines topics that will be covered in an analytics webinar, including effective big data visualization, necessary data transformations, visualizing patterns, and the differences between data discovery and descriptive analytics visualization. It discusses why organizations should use data visualization and provides examples of visualization types and tools. The key takeaways are that data visualization can be used for data discovery and manipulation, telling a story is important, and standardization can improve effectiveness with big data.

1. The First Step in Information Management
looker.com
Produced by:
MONTHLY SERIES
In partnership with:
Keys to Effective Data Visualization
November 2, 2017

2. Topics for Today’s Analytics Webinar
 Best Practices in Effective Big Data Visualization
 Transformations Necessary to Enable Effective Visualization
 Visualizing Patterns
 Data Discovery vs. Descriptive Analytics Visualization
 Key Take-Aways
 Q&A
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3. Definition of Data Visualization (a.k.a. Data Viz)
 Data visualization is a general term that describes any effort to help people
understand the significance of data by placing it in a visual context. Patterns,
trends and correlations that might go undetected in text-based data can be
exposed and recognized easier with data visualization software.
Source: Whatis.com
 Data visualization or data visualisation is viewed by many disciplines as a
modern equivalent of visual communication. It involves the creation and
study of the visual representation of data, meaning "information that has
been abstracted in some schematic form, including attributes or variables
for the units of information." A primary goal of data visualization is to
communicate information clearly and efficiently via statistical graphics, plots
and information graphics.
Source: Wikipedia
pg 3© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

4. Polling Questions
 Are you or is anyone in your organization actively using
data visualization?
− Yes, we’re actively using it
− Yes, but we’re just starting to experiment with it
− No, we’re not using it at all
− I’m not sure if we’re using data visualization
 What impact is data visualization having on your organization?
− It’s too early to tell its impact
− It’s helping us in several ways, e.g., spotting trends, allowing us to interact more
with our data, etc.
− We’re not seeing any benefits at all
− I’m personally not aware of its impact to the organization
pg 4© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

5. Why Use Data Visualization?
 Helps you understand large amounts of data
 Explains findings easily and more quickly
 Clarifies areas of importance or required actions
 Allows you to discover and understand trends,
patterns and correlations
 Improves recall of information
 It’s more interesting and engaging!
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Best Practices in Effective
Big Data Visualization

7. Data Visualization Best Practices
 Know your audience.
− What do you want to communicate and/or what problem are you
trying to solve?
 Keep it simple.
− Visualizations should be easy-to-comprehend, yet still meaningful.
 Tell a story with the data.
− Good stories have a structure and flow; think about the set-up and
conclusion.
 Exploit technology and types.
− Choose the right data visualization type for your purpose.
 Document data sources, collection method, timing, etc.
− Grow understanding and impact by providing the full context.
pg 7© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

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Transformations Necessary to
Enable Effective Visualization

9. Standardization and Transformation Precede Visualization
 Standardization, curation or “harmonization” of the data comes first.
 After gathering the data, there is still work to be done.
− For example, data scientists often spend 50 – 80% of their total time getting data
ready for analysis.
 Many organizations need to reduce “time to market” of results through
standardizing and positioning.
pg 9© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Gather Data
Format
Data
Use
Data
Standardize and position for use in visualization
Get reference data,
taxonomies, dimensions,
etc., lined up
”Join” data sets, position
data sets, to support
processing of visualization

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Visualizing Patterns

11. First, What Do You Want to Show?
pg 11© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Source: datavizcatalogue.com

12. Your Options Are Virtually Endless!
pg 12© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Source: datavizcatalogue.com

13. Data Visualization Types by Category
pg 13© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
GRAPHS/PLOTS
Arc
Brainstorm
Chord
Flow Chart
Illustration
Network
Non-ribbon Chord
Sankey
Timeline
Tree
Venn
Calendar
Gantt Chart
Heatmap
Stem & Leaf Plot
Tally Chart
Time Table
Parallel Coordinates Plot
Point & Figure Chart
Population Pyramid
Radar Chart
Radial Bar Chart
Radial Column Chart
Scatterplot
Span Chart
Spiral Plot
Stacked Area
Stacked Bar
Stream
Violin Plot
Area
Bar
Box and Whisker Plot
Bubble Chart
Bullet
Candlestick Chart
Density Plot
Error Bars
Histogram
Kagi
Line Graph
Marimekko
Multi-set Bar
OHLC Chart
TABLES
Bubble
Choropleth
Connection
Dot
Flow
MAPS
Circle Packing
Donut Chart
Dot Matrix Chart
Nightingale Rose Chart
Parallel Sets
Pictogram Chart
Pie Chart
Proportional Area Chart
Sunburst Diagram
Treemap
Word Cloud
OTHERDIAGRAM
Source: datavizcatalogue.com

14. Data Visualization Examples
pg 14
How People
Spend Their Time
flowingdata.com
#GDPR Global
Tweet Map
mapdm.com
Deaths in the Grand
Canyon (1869 – present)
arcgis.com
Why Snow Leopards
Are Disappearing
public.tableau.com
Monthly Average Temps.
Tokyo & London
highcharts.com/demo

15. Data Visualization Tools and Software
 Tableu
 Qlikview
 FusionCharts
 Highcharts
 Datawrapper
 Data-Driven Documents
(D3.js)
 Plotly
 Sisense
 Google Charts
pg 15© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Traditional BI with Some
Visualization Capabilities:
 Microsoft Power BI (and Excel, as
usual)
 MicroStrategy
 OBIEE
 SAP
 IBM

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Data Discovery vs.
Descriptive Analytics Visualization

17. DISCOVERY
Learning through
interacting w/ visualization
• Manipulate data
through interacting
with the visualization
• Best for exploring data
Data Discovery vs. Descriptive Analytics Visualization
 Determine if you need to
do analysis to grasp and
explain causality or do
you need to explore the
data some more to
develop a hypothesis of
causality?
pg 17© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
DESCRIPTIVE ANALYTICS
Seeing clearly what has
happened and grasping
why it happened
• Best for recognizing
causality
• Supports responding to
data-based inputs

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Key Take-Aways

19. Key Take-Aways
 Remember that data visualization tools can actually be
used for interacting with or manipulating the data, as well
as for data discovery.
 Visualization is a powerful tool, but remember to tell a story with it.
 Invest some time to be creative – and if you haven’t tried some of
the free tools that are available, set a goal to start next week!
 While most data viz tools will work for Big Data sets, they will be
more effective with some sort of standardization.
 Data visualization can point out patterns, but causality and
correlation can still be at odds.
pg 19© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

20. Questions?
© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
MONTHLY SERIES

21. Thank you for being here today!
Please join our next webinar on Thursday, December 7,
Trends in Data Analytics: From Database to Analyst.
John Ladley @jladley
john@firstsanfranciscopartners.com
Kelle O’Neal @kellezoneal
kelle@firstsanfranciscopartners.com