This document discusses big data and how organizations can get the most value from it. It defines big data as large data sets that are difficult to process using traditional data management tools due to their size and complexity. The document outlines the characteristics of big data, including volume, velocity, and variety. It also discusses different sources of big data, challenges of big data, and how organizations can analyze big data to gain insights, make predictions, and gain competitive advantages. The document advocates for measuring the success and value of big data initiatives.

1. Getting The Most Out Of Big
Data
Associate Professor Paul Hawking

2. “Can the amount of hype about Big Data be considered Big Data?”

3. What is Big Data?

4. Big Data is Not New
Cox & Ellsworth
“Data sets are
generally quite large,
taxing the capacities
of main memory,
local disk and even
remote disk. We call
this the problem of
big data”
1997 1998 1999 2001
Masey
“Big Data… and
the Next Wave of
Infrastress.”
Bryson et al
“Very powerful
computers are a
blessing to many
fields of inquiry. They
are also a curse; fast
computations spew
out massive
amounts of data.”
Laney
3D Data
Management:
Controlling Data
Volume, Velocity,
and Variety

5. Characteristics – V’s
Volume Velocity Variety Voldemort
Big Data

6. Voldemort – The dark side of Big Data

7. Data Sources
Big
Data
Transactions
Machines
Humans

8. What is Big Data?
Danah Boyd & Kate Crawford (Microsoft)
Big data is “a cultural, technological, and scholarly phenomenon
that rests on the interplay of:
 Technology: maximizing computation power and algorithmic accuracy
to gather, analyze, link, and compare large data sets.
 Analysis: drawing on large data sets to identify patterns in order to
make economic, social, technical, and legal claims.
 Mythology: the widespread belief that large data sets offer a higher form
of intelligence and knowledge that can generate insights that were
previously impossible, with the aura of truth, objectivity, and accuracy.

10. Why the increased interest?

11. The vendors
Prediction: Customers will leverage existing vendors’ technologies

13. Business Intelligence Process
1
Identify
business
issue
2
Formulate
business
question
3
What
information
do I need
4
Where do I
find the
information
5
Retrieve
information
6
Analyse
Information
7
Report
answers
8
Take
actions

14. Goals of Big Data

15. Big Data Analysis
Let’s act on it
What is the best that can happen?
What will happen next?
Why is this happening?
What actions are needed?
Where exactly is the problem?
How many, how often, where?
What happened?
Reports
Ad Hoc
Reports
Query
Drilldown
Alerts
Statistical
Analysis
Forecasting
Predictive
Analysis
Optimisation
Degree of Intelligence Maturity
Competitive Advantage
Proactive
Decision
Making
Reactive
Decision
Making

16. Leading Companies
Treacy & Wiersema
The Discipline of market Leaders

17. Core/Context Framework
Core  Engage
Processes that create differentiation that wins customers
Context  Disengage
All other processes

18. Big Data Value = Analysis + Context
Wisdom
Intelligence
Knowledge
Information
Data
New business strategies, opportunities
Lifetime value of this customer and
strategies to deploy to create loyalty
What the company has purchased,
what other products they may
purchase
A contact associated to a
Company and all back
orders
A Contact

19. Measuring Success and Value
Overall Success
Implementation
Success
User
Success
Operational
Success
Business
Success
• Create a formal, continuous process for measuring
success and value generated
• Identify and measure results of each initiative
• Establish realistic goals and expectations based on
capability / maturity
• On-time,
• On-budget
• User adoption
• Usage tracking
• User satisfaction
• Productivity
improvements
• Process
efficiency and
effectiveness
• Return on investment
• Economic value add
• Revenue increases
• Cost Savings
• Customer / corporate
profits
• Enables Business
Strategy and
Completive Advantage
Value Created

20. Who?

21. Meta Data
Management
Master Data
Management
Data Quality
Data Integration
Beware
Big Data

22. Gartner Hype Cycle

23. Topic:
Organized by
Paul Hawking
Associate Professor
SAP Academic Programs Director
College of Business
Telephone: +61-3-99194031
Mobile: +61-419301628
Email Paul.Hawking@vu.edu.au
Speaker name:
Email ID:
UNICOM Trainings & Seminars Pvt. Ltd.
contact@unicomlearning.com
Paulhawking #SAPVU