2nd Athens Big Data Meetup - 1st Talk - From Data Visualization to Visual Analytics

Athens Big Data Meetup
Friday, March 18th 2016
The CUBE – Athens, Greece
From Data Visualization to Visual Analytics
Dr. Andreas S. Maniatis
Commercial Manager / Head of BI
CyberStream LTD

© Copyright 2000-2016 CyberStream LTD
Systems Integrator
Established in 2000
Strong software
engineering skills
Focus on development
of custom & reusable solutions
Partner relationship with
established technology
companies
Involvement in ΒΙ-DW-
Analytics since 1997
CyberStream LTD
The human face of technology …
Business process
automation
Unified
communications
Enterprise
networking
Portals
E-Learning
Digital
Signage

Agenda
PART I
Visualization in Big Data [Business Intelligence, Business Analytics]
PART II
Data Visualization Techniques and Practices
PART III
Visual Analytics in Practice

Part I
Visualization in Big Data [Business Intelligence,
Business Analytics]

Issues to be discussed
•The Big Data hype …
•The many Vs for Big Data
•Some definitions on Visualization and Analytics
•Visualization concepts and Principles

The Data Science Hype Cycle [2014]
Gartner Hype Cycle
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp

The Data Science Hype Cycle [2015]
Gartner Hype Cycle
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp

•Factors demanding for usability differentiators (I):
• Flood of data – The “BIG DATA” era:
• Social Networks, Media (Facebook, Twitter, LinkedIn etc.)
• Search engines (Google etc)
• Variety of new data types / streams:
• 3 (4) V’s: Volume, Velocity, Variety, (Veracity)
• More V’s: Viability, Value, Volatility, Virtualization …
• 1 V only: Variety (Damianos Chatziantoniou)
Key usability differentiators for Visual
Analytics (I)

IBM Data Hub
http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
IBM: Big Data Defined by 4 Vs

IBM Data Hub
http://www.ibmbigdatahub.com/infographic/extracting-business-value-4-vs-big-data
IBM: Big Data Defined by 5 Vs

“V” for Visualization
•Why Visualization is the biggest “V” of them all:
• Visualization is the key to making Big Data an integral part
of decision making
• Visualization is the only way to make Big Data accessible
to a large audience
[Storytelling and Narration]
• Visualization is essential to the analysis of Big Data so it
can be of highest value

•Factors demanding for usability differentiators (II):
• Faster cycle times
• New devices (Smartphones, Tablets, Touch and Voice
Activated Displays, Video Walls etc.)
• The introduction of Analytics by itself
• Involve more power users in exploratory data analysis
Key usability differentiators for Visual
Analytics (II)

Biology in Visual Perception

•Seeing 83 percent
•Hearing 11 percent
•Touching 3 ½ percent
•Smelling 1 ½ percent
•Tasting 1 percent
•6th Sense … ?
The importance of senses
Source: Oklahoma State University
http://www.oces.okstate.edu/washita/uploaded_files/4h_Learning_Styles.doc

The importance of context
The Washington Metro Experiment with Joshua Bell
http://www.washingtonpost.com/lifestyle/style/joshua-bell-is-playing-in-the-metro-again-this-time-you-can-plan-to-be-there/2014/09/23/7a699e28-4282-11e4-9a15-137aa0153527_story.html

A Rabbit or a Duck?

What is Data Visualization
Kim Rees - Founding partner of Periscopic, a socially-conscious data visualization firm
https://about.me/krees

The Art of Visualizing Data

Visual Analytics: A Definition
“... the science of analytical reasoning
facilitated by interactive visual interfaces …”
Relative Terms:
• Visual Analysis
• Visual Data Analysis
• Visual Data Mining
Illuminating the Path: Research and Development Agenda for Visual Analytics
James J. Thomas, Kristin A. Cook
IEEE Press, 2005

The Scope of Visual Analytics
Source: http://www.visual-analytics.eu/faq/

Research Fundamentals for Visual
Analytics

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The Power of Visual Analytics
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= 8
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32

Part II
Data Visualization Techniques and Practices

• Some History …
• Data Visualization Techniques
• Visualization practices to avoid
• The Visual Analytics Mantra
• Interactive Dynamics for Visual Analysis

Some History (1)
Map from 600 BC - Babylon

Some History (2)
Meet … Jon Snow (1 of 2)

Some History (2)
Meet … John Snow (2 of 2)

Some History (2)
Cholera Outbreak in London (1854)
Sources: “The Guardian” Interactive Map
http://www.theguardian.com/news/datablog/interactive/2013/mar/15/cholera-map-john-snow-recreated

Some History (4)
Napoleon’s March to Moscow (C.J.Minard, 1869)

Sources: http://www.extremepresentation.com
http://annkemery.com/essentials/ | http://labs.juiceanalytics.com

Visualization Practices to Avoid (1)
Can you interpret the Chart? This is better …
0
5
10
15
20
25
30
35
A B C
3D Bar Chart
0
5
10
15
20
25
30
35
40
A B C
Bar Chart
Creating More Effective Graphs
Naomi B. Robbins
Statistical Society of Ottawa, Ottawa – Ontario, December 8th 2009

Visualization Practices to Avoid (2)
Can you sort the data? This is better …
Pie Chart
A B C D E
0
5
10
15
20
25
30
35
40
C E B D A
Bar chart sorting
Creating More Effective Graphs
Naomi B. Robbins
Statistical Society of Ottawa, Ottawa – Ontario, December 8th 2009

Visualization Practices to Avoid (Final)

Visualization Practices to Follow (2)
Gauges? Bullet Graph - This is better …
Gauges: The Black Sheep of Data Visualization
http://www.dundas.com/blog-post/gauges-the-black-sheep-of-data-visualization/

Devour the Pie Chart

Visualization Practices to Follow (Final)
Curious, what's your preference of these 4 ways to show Uber v Taxi wages? Know a better way?
Sometimes ONE Number Tells the Whole Story
If city-specific comparison is not needed, then why over-complicate?

The Visual Analytics Process

Part III
Visual Analytics in Practice

• Requirements for a Data Analytics Platform Architecture
• New methodology for Visual Applications
• Live Visual Analytics with TIBCO Spotfire
• The Data Scientist as a Story Teller

We are surrounded by data, yet most people
still make decisions without it

…visualization-based data discovery tools have far-reaching implications for how
business information is consumed….end-user organizations should adopt use as a way to
improve the success of their BI program. - Gartner, June 2011
“ “

Technical Differentiators
Dimension-Free
Data Exploration
Data Mashup
Predictive &
Event Driven
Contextual
Collaboration
Enterprise-Class
• Visual
• Interactive
• No Constraints
• Combine Data Sources
• No Scripting
• IT Free
• Data at Rest & In Motion
• Open Source & 3rd Party
• “Two Second Advantage”
• Bookmarks
• Guided Apps
• Portals & Social Platforms
• Unmatched
Performance
• Massive
Scalability
• 24x7
Expertise

IT Centric BI
ERP
CRM
SCM
E
T
L
OLAP
Cube
Data
Warehouse
Meta
Data
BI
Platform
Gather
Specifications
Design
Data Models
Source
the data
Load
the data
Construct
BI Metadata
Build
Reports
Publish
To User
Business
User
IT
Developer
If specifications change, repeat the process!

Business User centric Analysis
ERP
CRM
SCM
E
T
L
OLAP
Cube
Data
Warehouse
Meta
Data
BI
Platform
Gather
Specifications
Design
Data Models
Source
the data
Load
the data
Construct
BI Metadata
Build
Reports
Publish
To User
Business
User
IT
Developer
If specifications change, repeat the process!
Connect to
Data Source
… and/or
Multiple
Data Sources
Interact &
Visualize Data
Publish/Share
w/ others

Case Study
Environmental Analytics

Problem Description …
• Best Pilot Data Visualization on Air Quality in Athens
• Sample data from 2 measuring stations
• Want to locate “Outliers” and gain “Insights”
• Build an Interactive Visualization Dashboard

ENVisage:
Exploratory Visual Analytics on Open Access
Environmental Data
the CyberGreen Team
Athens Green Hackathon
14/12/2012 – 16/12/2012
Presentation @ Athinais Cultural Centre

Turning legacy reporting examples …

Project Metrics
70%
Data
Extraction,
Cleaning and
Transforming
30%
Interactive
Visual
Analytics
Application

… into exemplary Visual Analytics paradigms

LIVE DEMO

New Skills for the Data Scientist
• Data Scientist, Analyst or Statistician?
– Technical Skills
– Teamwork Skills
– Communication Skills
– Business Skills (with a little bit of empathy)
– Tool Mastery
• Story Teller?
Institute for Advances Analytics
http://analytics.ncsu.edu/

• Math & Statistics
• Machine Learning
• Statistical Modelling
• Experiment design
• Bayesian Interface
• Supervised Learning: Decision Trees,
Random Forests, Logistic Regression
• Unsupervised Learning: Clustering,
Dimensionality Reduction
• Optimization: Gradient Descent and
Variants
• Programming And Database
• Computer Science Fundamentals
• Scripting Languages (i.e., Python etc.)
• Statistical Computing Packages (i.e., R)
• Databases: SQL and NoSQL
• Relational Algebra
• Parallel Databases and Parallel Query
Processing
• MapReduce Concepts
• Hadoop and Hive/Pig
• Custom reducers
• Experience with xaaS
Modern Data Scientist
• Domain Knowledge & Soft Skills
• Passionate about the Job
• Curious about Data
• Influence without Authority
• Hacker Mindset
• Problem Solver
• Strategic, Proactive, Creative, innovative
and Collaborative
• Communication & Visualization
• Able to Engage with Senior Management
• Story telling skills
• Translate Data-Driven Insights into
Decisions and Actions
• Visual Art Design
• R packages like ggplot and lattice
• Knowledge of any of Visualizations Tools
& Platforms (i.e., TIBCO Spotfire,
Tableau, Flare, d3.js etc.)

A Visual Narration:
Are we Better Off than we Think?
@HansRosling
https://www.youtube.com/watch?v=jbkSRLYSojo

Thank you!
Dr. Andreas S. Maniatis
Commercial Manager / Head of BI
andreas.maniatis@cyberstream.gr
http://www.linkedin.com/in/andreasmaniatis
www.cyberstream.eu

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