Big & Little Data
Volume, Velocity, and Variety
Rob Woodward
Sterling Stoudenmire
• CEO
• Katana Software, Inc.
Van Dyke Holdings, LLC
[Moderator]
Drew Reynolds
• CEO
• TrackResults Software.
Jeff Farr
• VP, Revenue Strategy
• Grand Pacific Resorts
Introduction
Big Data
A term used to describe the exponential growth and
availability of data.
• Volume
• Velocity
Analyze
Visualize
• Variety
An ever growing river of unstructured data, flowing
wider, deeper and faster with each passing day
generated by:
• Web Logs
• Images and Video
• Social Media
• Documents
• Industrial Sensors
Little Data
A term used to describe a new category of
data…personal data.
• Who we know
• What we like
• What we do – “Auto Analytics”
In 60 seconds…
Time to create 5 Exabytes of digital data
10 minutes
2 days
A new frontier paradigm…
HBR Survey
0%
5%
10%
15%
20%
25%
28.0%
23.0%
6.0%
3.5%
30%
New Rules
• Privacy
• Security
New Tools
• Hadoop
• MapReduce
• Machine Learning
• Visual Analytics
New Skills
• Data Scientist
But Remember…
Not everything that can be counted…
counts,
Not everything that counts…
can be counted
The only thing that
interferes with my
ability to learn is…
AND…even with all this data
We can’t find
My
Education
Big Data
Rob Woodward,
Katana Software, Inc.
Big Data ???
Volume
• Yesterday = Gigabytes
• Today = Terabytes and Petabytes
• Tomorrow = Exa, Zetta, Yottabytes
Big Data ???
Velocity
•
2012 = 855 Petabytes
• 2014 = Doubling Each Year
• 2017 = 11.3 Exabytes/Mo
Big Data ???
Content
•
2010 = Structured, Homogeneous
•
2014 = Unstructured,Heterogeneous
•
2018 = Robotic, Predictive, Nano-Tech, Bio-Metric
Value ???
Data = The New Currency
•
Data = Consumer & Process Knowledge
•
Knowledge = Relationship Power
•
Relationship = Survival & Growth
Examples ???
•
Link On-Site Service Data with Survey Data
•
Predict and Prevent Service & Facility Failures
•
Know What Attracts and Keeps Guests & Owners
•
Optimize Internal & External Relationships
Impacts ???
• Data-Driven Management
•
Quality is Measurable and Predictable
•
KPIs as Culture
•
Adaptive Learning
Thank You!!!
Please complete the session
survey on the Mobile App
where you can also find
session documents.
Big & Little Data
Individual User
Drew Reynolds,
TrackResults Software
BIG DATA
Little data
Little Data or Big Data?
Little Data or Big Data?
Little Data or Big Data?
Little Data or Big Data?
BIG Data In The News
Disney (build)
BIG Data In The News
Individual User
Your Little Data Should Be In The News
Departments (aka workload)
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Timeshare Sales and Marketing
Traditional Marketing
Housekeeping
Front desk
Maintenance
HOA
Contracts
C-level
Operations
Human resources
Payroll
Customer service
Call Center
Information Technology
Levels In These Departments
•
•
•
•
•
•
•
C-level
Presidents
Vice Presidents
Directors
Mangers
Supervisors
Employees
In the Beginning
One a day, or One at a time
Once a day with multiple reports
Does it look like this?
The Cloud
Your Databases
End User BI Tools
The Plan?
1. When will it be a priority?
2.
3.
5 GREAT IDEAS!
The Plan?
Who is going to be the champion?
What tools are you going to use?
•
•
•
•
•
•
•
•
Information Technology?
Data specific department?
CDO?
Head of Data for each
Department?
Report Building?
Big Name BI Experts?
Consulting Analytic Companies
Industry Specific Tools?
Takeaways
• BI
• KPI
• DSS
• Analytics
• Data Visualization
• Reporting
Jeff Farr
Vice President of Revenue Strategy and Operations
(760) 827-4172
jfarr@gpresorts.com
Big Data
•
Structured
•
Consumable
•
Informed Decisions
•
Mature Programs
•
“I Know”
Boldly Go in One Direction
Big Data
Little Data
•
Structured
•
Unstructured
•
Consumable
•
Anecdotal, every day evidence
•
Informed Decisions
•
Gut Decisions
•
Mature Programs
•
Start-Ups, Pilots, Fast Failure
•
“I Know”
•
“I think”
Boldly Go in One Direction
Test Multiple Ideas
You can’t manage what you can’t measure.
Without Data
I think…
I heard…
I feel…
With Data
I KNOW!
•
Efficiency In Process
•
Efficiency in Meetings
•
Supports “Best idea
wins culture”
•
Validation of
tactics/strategy
•
Stay ahead of curve
with trends
Challenges
•
Fear
•
Disparate Systems
•
Change
•
Paralysis
o Tipping point of too
much
o Deciphering relevance
Call Center Example
Common Knowledge: More hours = More Expense
Proof: Everybody knows that.
Test:
Expand Hours of Operation without expanding call center labor costs
Results
•
Decreased Abandon Rate by
25%
•
Improved Customer
Satisfaction by 12%
•
16 hours of extra operational
capacity
•
No expansion in labor costs
Impact on Organizational
Decision Making
Without Data
•
Best idea wins culture
•
Enables everyone to ask why
•
Drive innovation via fast failure
•
Takes the Emotion out of decision
making
With Data
Thank You!!!
Please complete the session
survey on the Mobile App
where you can also find
session documents.
Consider:
Every 60 seconds…
•
•
•
•
•
•
4,380
200,000
546,0000
4,112,500
39,000,000
200,000,000
items ($120,000+) ordered on Amazon
photographs uploaded on Facebook
tweets posted on Twitter
queries on Google
data requests on Amazon S3 cloud
text messages sent