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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
Data AnalysisPutting Data Capital to Work
Mohit MahendraAnalyst / StrategistOracle Corp
Views are a professional point of view, synthesized from multiple sources.
Its All About Decisions
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“…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
Both Intuition and Analytics Matter For Big Decisions
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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
What Are Big Decisions?
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Source: PwC’s Global Data & Analytics Survey 2014: Big Decisions
Top goals for big decisions, among Technology industry executives
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
New Expectations of an Analyst
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AG
ILE
Decisions
STRATEGIC
FUELS INNOVATION
CREATIVE
DA
TA S
CIE
NT
IST
Problem Solver
BUSINESS KNOWLEDGE
DATA WRANGLER
COMPETITIVEEDGE
VIS
UA
L &
AR
TIS
TIC
OPTIMIZATION
ANALYTIC SKILLS
RESEARCHER
ACADEMIC
COMMUNICATION
BIG PICTURE
NUMBERS
MATH
LEARNINGTOOLS
EXPERIMENTS
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
ProductiveValue
ANSWERS
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
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
Source: Business Intelligence: A Core Business Competency, Gartner 2014
• “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
