cloud computing, big data and mobile enhancing innovation through busine
Post on 27-Jan-2015
105 Views
Preview:
DESCRIPTION
TRANSCRIPT
Central Connecticut State University School of Business
Spring 2014
Spring 2013
© 2013-2014 Michael Gendron 1
Cloud Computing, Big Data and Mobile: Enhancing Innovation Through Business Intelligence
Michael Gendron
6/4/2014
Central Connecticut State University School of Business
Enabling Innovation at the BI Sweet Spot THE BI ECOSYSTEM
6/4/2014 © 2013-2014 Michael Gendron 2
Central Connecticut State University School of Business
Agenda • Background
– The Cloud Ecosystem
– Big Data
– Mobile
• Innovation – The business cycle
– CAPEX, OPEX
• Business Intelligence – The BI Sweet Spot
6/4/2014 © 2013-2014 Michael Gendron 3
Central Connecticut State University School of Business
Industry? Healthcare
Manufacturing Education
Insurance, Banking Or Other Financial Services Other
How would you categorize yourself?
Senior Staff (VP and Above) Middle Manager
Staff Consultant
6/4/2014 © 2013-2014 Michael Gendron 4
Central Connecticut State University School of Business
Setting the Stage With Some Definitions
Cloud
Big Data Mobility
6/4/2014 © 2013-2014 Michael Gendron 5
Central Connecticut State University School of Business
THE CLOUD COMPUTING ECOSYSTEM
6/4/2014 © 2013-2014 Michael Gendron 6
Central Connecticut State University School of Business
6/4/2014 © 2013-2014 Michael Gendron 7
What Cloud Computing Isn’t
• The Cloud vs. Cloud Computing
– We use the term cloud to mean anything connected to the Internet…
– Cloud Computing requires Internet connection…But… • Not everything Internet connected is Cloud Computing
• E.G., Email might be offered on a cloud computing platform but may not be either.
• E.G., A USB hard drive accessible over the Internet is not cloud computing
THE CLOUD COMPUTING ECOSYSTEM
Central Connecticut State University School of Business
What is Cloud Computing
6/4/2014 © 2013-2014 Michael Gendron 8
THE CLOUD COMPUTING ECOSYSTEM
Central Connecticut State University School of Business
Best Practices
• Understand the difference between Cloud and Cloud Computing
• Have a firm grasp on how you are going to deploy a cloud computing environment: IaaS, PaaS, SaaS, Private, Public, Hybrid, Community
• Make sure your employee’s have the right skills • Know who the actors are in your cloud computing
project as they drive the project including cost and availability
6/4/2014 © 2013-2014 Michael Gendron 9
THE CLOUD COMPUTING ECOSYSTEM
Central Connecticut State University School of Business
BIG DATA
6/4/2014 © 2013-2014 Michael 10
Central Connecticut State University School of Business
• A phenomena not just large data bases
• Ubiquity of transactions: – Cloud Data Providers
– Mobile Computing
– Online transaction processing systems
– Anything else you can imagine
• High Volume, Velocity, Variety
• Technologies are associated with Big Data but is a phenomena: E.G. , Hadoop, SAS, NoSQL
6/4/2014 © 2013-2014 Michael Gendron 11
Big Data
Central Connecticut State University School of Business
6/4/2014 © 2013-2014 Michael Gendron 12
Database
and DBMS, OLTP etc.
Data Model
Defines
Database
Data Aggregation Layer
Data Warehouse Created
by Aggregating Sources
Business
Intelligence
Business Analytics
Application
Cloud Data
Provider
Cloud Data
Provider
BIG DATA – BI IN THE CLOUD ERA
Central Connecticut State University School of Business
6/4/2014 © 2013-2014 Michael Gendron 13
Data Stored
In House
Data Stored at
a Cloud Provider
BI
Applications
In House
BI
Applications
At Cloud
Provider
Decide Where Data
and BI Applications are
located
Any combination is possible
based on organizational
needs and objectives
BIG DATA – WHERE TO STORE DATA AND APPLICATIONS
Central Connecticut State University School of Business
6/4/2014 © 2013-2014 Michael Gendron 14
BIG
DATA
BIG
DATA
BIG
DATA
BIG
DATABig
Data
Big
Data
Big
Data
Big
Data
High
Volume
High
Velocity
High
Variety
BIG DATA – HIGH VOLUME, VELOCITY, VARIETY
Central Connecticut State University School of Business
6/4/2014 © 2013-2014 Michael Gendron 15
Predictive A
nalytic A
ccuracy
Vo
lum
e, V
elo
city
, Va
rie
ty o
f D
ata
Small
Large
Complexity of AnalyticsDescriptive Predictive
As the volume, velocity, and variety of
data increases so does the ability to create
more accurate and complex BI; small
amounts of data can be used for
descriptive analytics while larger amounts
of data can be suitable for more predictive
analytics
BIG DATA – IMPACT ON ANALYTICS
Central Connecticut State University School of Business
Best Practices
• Know your data sources and what they add to the BI project
• Know the concept of Big Data as a phenomena rather than just technologies and understand what that means for your organization
• Determine the best location for your data and applications
• Know what BI you need and what data that requires (within and outside your organization)
6/4/2014 © 2013-2014 Michael Gendron 16
BIG DATA
Central Connecticut State University School of Business
MOBILITY
6/4/2014 © 2013-2014 Michael Gendron 17
Central Connecticut State University School of Business
Definition of a Mobile Device
• Communicate over Mobile Networks
• Voice or Gesture Driven Interface
• Allow Apps
• Recognition of Geographic or Physical Context
• Computing Power
6/4/2014 © 2013-2014 Michael Gendron 18
Mobility
Central Connecticut State University School of Business
Innovation In the Enterprise • Changes the way we do business
– Healthcare Delivery • Patient interactions
– Manufacturing • Supervisory Interactions
– Retail • Remote Cash Registers
– Warehouse • Inventory Control
– Employee and Consumer Interactions • Bank Account Access, Insurance Cards, Collect data (e.g., Customer
Sentiments), Self-Service Business Intelligence, etc.
6/4/2014 © 2013-2014 Michael Gendron 19
Mobility
Central Connecticut State University School of Business
Best Practices
• Understand what a mobile device is – Smart phone, laptop, tablet….
• Know the limitations and advantages of mobile devices – Reach vs. Richness
– Type of interface, amount of data, security, etc.
• Coordinate desktop and mobile BI – Tethered and untethered
– Collection and dissemination
6/4/2014 © 2013-2014 Michael Gendron 20
Mobility
Central Connecticut State University School of Business
INNOVATION
6/4/2014 © 2013-2014 Michael Gendron 21
Central Connecticut State University School of Business
How Cloud Computing, Big Data and Mobility Come Together
6/4/2014 © 2013-2014 Michael Gendron 22
Innovation
Central Connecticut State University School of Business
Build VS Buy How CAPEX/OPEX is impacted
• The choices
– Build a BI infrastructure in house
– Buy a smaller in house infrastructure and use cloud providers for data, processing and self-service BI
6/4/2014 © 2013-2014 Michael Gendron 23
Innovation
Central Connecticut State University School of Business
CAPEX vs. OPEX
• Capital Expense (CAPEX) – Purchase equipment and depreciate it over time
– Attempt to match costs of asset with income it generates
– Example: Purchase IT infrastructure
• Operating Expense (OPEX) – Buy Products/Services and Expense it during the
period used
– Example: Services of a Cloud Provider
6/4/2014 © 2013-2014 Michael Gendron 24
Innovation
Central Connecticut State University School of Business
Scenario One: In House Infrastructure Budget YEAR
STARTUP
COSTS ONE TWO THREE FOUR FIVE
CAPITAL EXPENSES Initial In-House ICT 350,000 70,000 70,000 70,000 70,000 70,000
In-House Setup 65,000 13,000 13,000 13,000 13,000 13,000 Annual Capital Expenses (CAPEX) 43,000 43,000 43,000 43,000 43,000
Total 5 year CAPEX 215,000 OPERATING EXPENSES
In-House Personnel 160,000 168,000 176,400 185,220 194,481 In-House ICT Maintenance 59,500 62,475 65,599 68,879 72,323
Annual Operating Expenses (OPEX) 219,500 230,475 241,999 254,099 266,804 Total 5 Year OPEX 1,212,876
EXPENSES TO THE PROFIT AND LOSS STATEMENT
Annual Expenses 262,500 273,475 284,999 297,099 309,804 Total Five Year Expenses --> 1,427,876
Example – Build In house
6/4/2014 © 2013-2014 Michael Gendron 25
Innovation
Central Connecticut State University School of Business
Example – Use Cloud Providers
6/4/2014 © 2013-2014 Michael Gendron 26
Scenario Two: External Cloud
YEAR
STARTUP COSTS ONE TWO THREE FOUR FIVE
CAPITAL EXPENSES
Initial In-House ICT 75,000 15,000 15,000 15,000 15,000 15,000
In-House Setup 15,000 3,000 3,000 3,000 3,000 3,000
Annual Capital Expenses (CAPEX) 18,000 18,000 18,000 18,000 18,000
Total 5 year CAPEX 90,000
OPERATING EXPENSES
In-House Personnel 75,000 78,750 82,688 86,822 91,163
In-House ICT Maintenance 5,400 5,670 5,954 6,251 6,564
Cloud Computing Service 210,000 220,500 231,525 243,101 255,256
Annual Operating Expenses (OPEX) 290,400 304,920 320,166 336,174 352,983
Total 5 Year OPEX 1,604,643
EXPENSES TO THE PROFIT AND LOSS STATEMENT
Annual Expenses 308,400 322,920 338,166 354,174 370,983
Total Five Year Expenses --> 1,694,643
Innovation
Central Connecticut State University School of Business
CAPEX/OPEX Summary
6/4/2014 © 2013-2014 Michael Gendron 27
SUMMARY
Scenario One In
House
Scenario Two
External Cloud
Total 5 Year CAPEX 215,000 90,000
Total 5 Year OPEX 1,061,261 1,314,243
Total 5 Year Expenses to P&L 1,510,761 1,694,643
Year One Cash Needs 649,500 380,400
Five Year Expenses 1,510,761 1,694,643
Innovation
Central Connecticut State University School of Business
Business/Innovation Cycle New
Innovation
Business Growth Causing
Increased Assets
Builds Small Infrastructure
Uses Cloud Providers
Greater Emphasis On OPEX
Moves to Hybrid
Infrastructure
Greater Emphasis On CAPEX
Change OPEX/CAPEX
Over Time
6/4/2014 © 2013-2014 Michael Gendron 28
Innovation
Central Connecticut State University School of Business
Zynga’s Business Cycle and IT Deployment
6/4/2014 © 2013-2014 Michael Gendron 29
Hyb
rid
Clo
ud
P
riva
te C
lou
d (
No
rmal
Tra
ffic
)
AW
S (P
eak
Load
s)
Level Out
Pu
blic
Clo
ud
M
ove
d O
per
atio
ns
to A
WS
Initial Growth
Bu
ilt In
Ho
use
Infr
astr
uct
ure
Startup
Innovation
Central Connecticut State University School of Business
PUTTING IT ALL TOGETHER
6/4/2014 © 2013-2014 Michael Gendron 30
Central Connecticut State University School of Business
An Example: TripAdvisor
• Monetized via division that sells BI to the travel industry
• Mobile to connect users
• Cloud for processing and access
• Big Data to customer sentiments, reservation data from trips, social networking, etc.
6/4/2014 © 2013-2014 Michael Gendron 31
Putting It All Together
Central Connecticut State University School of Business
How Implemented • Applications
– Wisdom of Friends – integration with Facebook
– City Guides – mobile application
– Maps – cloud geo-locater service
– Check Rates – cloud, big data
– TripWatch – email alerts
• All of these come together to create BI and the Sweet Spot for TripAdvisor
6/4/2014 © 2013-2014 Michael Gendron 32
Putting It All Together
Central Connecticut State University School of Business
Trip Advisor and the BI Sweet Spot
6/4/2014 © 2013-2014 Michael Gendron 33
Customer and Industry Access Self Service BI
SAAS Services Consumers and Travel Industry
Customer Reviews Hotel and Other
Travel Data
Putting It All Together
Central Connecticut State University School of Business
Contact • Personal Webpage: www.gendron.info • LinkedIn • Twitter: @BIAGendron • Email:
– gendronm@ccsu.edu and dr.g@gendron.info
• Amazon Author Page – http://amazon.com/author/gendron
• Blog www.allanalytics.com • Current Book: Business Intelligence and the Cloud • I will be at @BigDataExpo in June 2014
6/4/2014 © 2013-2014 Michael Gendron 34
Central Connecticut State University School of Business
• Raffle off two copies of my latest book
• Forming a workgroup
– Build business value in cloud and native applications
– Limited to 10 people and One year
– Develop best practices
– Please contact me if interested
6/4/2014 © 2013-2014 Michael Gendron 35
top related