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The First Step in Information Management
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Keys to Effective Data VisualizationNovember 2, 2017
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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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 variablesfor 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
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Polling Questions
Are you or is anyone in your organization actively usingdata 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
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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 EffectiveBig Data Visualization
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.
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Transformations Necessary toEnable Effective Visualization
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
First, What Do You Want to Show?
pg 11© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Source: datavizcatalogue.com
Your Options Are Virtually Endless!
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Source: datavizcatalogue.com
Data Visualization Types by Category
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GRAPHS/PLOTSArc
BrainstormChord
Flow ChartIllustrationNetwork
Non-ribbon ChordSankey
TimelineTreeVenn
CalendarGantt Chart
HeatmapStem & Leaf Plot
Tally ChartTime Table
Parallel Coordinates PlotPoint & Figure ChartPopulation Pyramid
Radar ChartRadial Bar Chart
Radial Column ChartScatterplotSpan ChartSpiral Plot
Stacked AreaStacked Bar
StreamViolin Plot
AreaBar
Box and Whisker PlotBubble Chart
BulletCandlestick Chart
Density PlotError BarsHistogram
KagiLine GraphMarimekko
Multi-set BarOHLC Chart
TABLES
BubbleChoroplethConnection
DotFlow
MAPS
Circle PackingDonut Chart
Dot Matrix ChartNightingale Rose Chart
Parallel SetsPictogram Chart
Pie ChartProportional Area Chart
Sunburst DiagramTreemap
Word Cloud
OTHERDIAGRAM
Source: datavizcatalogue.com
Data Visualization Examples
pg 14
How People Spend Their Timeflowingdata.com
#GDPR Global Tweet Mapmapdm.com
Deaths in the Grand Canyon (1869 – present)arcgis.com
Why Snow Leopards Are Disappearingpublic.tableau.com
Monthly Average Temps.Tokyo & Londonhighcharts.com/demo
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
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
Key Take-Aways
Remember that data visualization tools can actually beused for interacting with or manipulating the data, as wellas 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
Questions?
© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
MONTHLY SERIES
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 @jladleyjohn@firstsanfranciscopartners.com
Kelle O’Neal @kellezonealkelle@firstsanfranciscopartners.com