This short deck explains the work of a modern data analyst. Putting data capital to work in the business enables decision quality & velocity, operating speed and growth

1. 1
Know
Data
Capital?
What gets
measured, gets
managed
- Peter Drucker
The price of light is
less than the cost of
darkness
- Arthur C. Nielsen
War is ninety
percent
information
- Napoleon
In God we Trust.
All others must
bring Data
- Edward Deming
Building Oracle is
like doing math
puzzles as a kid
- Larry Ellison
He who would
search for pearls
must dive below
- John Dryden, poet,
17th century
Data Scientist:
Sexiest job of the
21st Century
- HBR
Consulting on the
Cusp of Disruption
- Clayton Christensen
Data is now a kind of
capital, on par with
financial capital
- Oracle
Data analysis is a
creative process
- Tableau
The world is one
big data problem
- Andrew McAfee

2. Data Analysis
Putting Data Capital to Work
Mohit Mahendra
Analyst / Strategist
Oracle Corp
Views are a professional point of view, synthesized from multiple sources.

3. Its All About Decisions
3
“…It is possible for executives—and companies—
to significantly improve their chances of success
by making one straightforward (albeit not simple)
change: expanding their tool kit of decision support
tools and understanding which tools work best for
which decisions.”
- Deciding How to Decide, HBR
Reports
Scorecards
Dashboards
Drill-down
Decisions

4. Both Intuition and Analytics Matter For Big Decisions
4
Average value of future
profitability of big
decisions made by
technology industry
executives
Tech executives relied
on Data & Analytics
Inputs the most for
their last big decision
63%
53%
Tech executives expect to make
a big decision at least once per
month. Just as many plan to
revisit their most important
decisions every 3-6 months.
Tech executives have changed
the way they approach big
decision making as a result of
Data & Analytics, over the last
24 months.
Source: PwC’s Global Data & Analytics Survey 2014: Big Decisions
$320M
43%
29%
28%
Own
Experience
& Intuition
Experience
of others

5. What Are Big Decisions?
5
Source: PwC’s Global Data & Analytics Survey 2014: Big Decisions
Top goals for big decisions, among Technology industry executives

6. What Does An Analyst Do?
Oracle Confidential – Internal
6
1. Perspective – Data discovery
and strategic business analysis
2. Impact – How effectively did a
strategy work? Why or why
not?
3. Projection – What will a
strategy look like?
4. Recommendation - What
strategy will work best?
Analytics to Innovate, Differentiate and Compete

7. New Expectations of an Analyst
7
AGILE
Decisions
STRATEGIC
FUELS INNOVATION
CREATIVE
DATASCIENTIST
Problem Solver
BUSINESS KNOWLEDGE
DATA WRANGLER
COMPETITIVE
EDGE
VISUAL&ARTISTIC
OPTIMIZATION
ANALYTIC
SKILLS
RESEARCHER
ACADEMIC
COMMUNICATION
BIG PICTURE
NUMBERS
MATH
LEARNING
TOOLS
EXPERIMENTS

8. Functional Tracks of Decision Science
DATA
TOOLS & SKILLS
ANALYSIS & MODELS
QUESTIONS
DECISIONS
STRATEGY
SYSTEMS & INFRASTRUCTURE
FASTER, BETTER SKILLS
SCALE w/ LOWER COST SKILLS
BETTER TOOLING
CUSTOM TOOLING
REUSABLE IP
ANALYTICS
SPEED / SCALE
Business
Value
Productive
Value
ANSWERS

9. Business Intelligence has evolved
Old Model New Model
Reporting & Dashboards
Back-Office / Tactical
BI Systems of Record
Low Adoption among Users
Decisions needing Consistency
IT-Centric Producers vs Consumers
Waterfall Development
One-stop Data Warehouse
HIPPO
Discovery, Exploration, Storytelling
Business Competency / Strategic
BI Systems of Innovation
Ad-hoc, Self-serve BI
Decisions needing Agility
Business-Centric ‘Prosumers’
Rapid Time to Insight
Data lakes / Fail fast, fail cheap analytic models
Wisdom of Crowds

10. Key Competencies
Product, Market
& Industry
Knowledge
Data
Modeling &
Tool Skills
Quant,
Financial &
Analytic
Skills
Market/
Fin Analyst
BI
Analyst
Ops/
Business
Analyst
The Decision Science Venn Diagram,
inspired by The Data Science Venn Diagram (2010)
DECISION
SCIENCE

11. Source: Business Intelligence: A Core Business Competency, Gartner 2014

12. • “Although art and analytics may seem different, there is a common thread: both are on a mission to reveal truth and impart
meaning, both challenge their viewers to look at the world through a different lens, both rely on observation and curiosity and
encourage creative problem-solving.” – Christian Chabot, Tableau Keynote
• “Somebody will always have to open the can.”
VS.
Keeping Ahead: Man vs Machine
• Computers
• Dispassionate analysis
• Data and statistics
• Discipline and rigor
• People
• Passionate advocacy
• Intuition
• Creativity and insight
Tableau Keynote

13. Thanks 
Email: mohit.mahendra@oracle.com
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