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SATYENDRA RANA – LOVEN SYSTEMS
Toward an Opportunity
Driven Enterprise: Why &
How?
Satyendra Rana
CTO - Loven Systems
March 24, 2016
3rd Big Data & Business Analytics Symposium – March 24, 2016
1
SATYENDRA RANA – LOVEN SYSTEMS
Achieving Business Excellence
Sustain ------------------ Grow
3rd Big Data & Business Analytics Symposium – March 24, 2016 2
Big
Data
People Processes
Technology
Value
People, Process, Technology
Synergy
Big Data
Phenomenon
Quantified Enterprise
Opportunity Driven
Enterprise
SATYENDRA RANA – LOVEN SYSTEMS
Opportunity Driven Enterprise
What’s different?
3rd Big Data & Business Analytics Symposium – March 24, 2016 3
Data Driven
(Naïve Approach)
• Starts with Data
• First Milestone - Data Lake
• Embellish by Data Visualization
• User must understand Data
• User reaches out to System (Discovery)
• Produce Analytics
• Develop Data Strategy (CDO)
Opportunity Driven
(Cognitive Approach)
• Starts with Value
• First Milestone – Opportunities Flow
• Embellish by Natural Language Interaction
• System understands Data
• System reaches out to User (Guidance)
• Consume Analytics
• Develop Opportunity Management Strategy
(COO)
SATYENDRA RANA – LOVEN SYSTEMS
Path to Excellence
Enterprise Risk & Change Management
3rd Big Data & Business Analytics Symposium – March 24, 2016 4
“You cannot
steer, what
you cannot
quantify”
Foundation of quantification is measurement
“What Gets
Measured
Gets Done”
Quantification Reflection Adaptation
SATYENDRA RANA – LOVEN SYSTEMS
Quantification
What should we measure?
3rd Big Data & Business Analytics Symposium – March 24, 2016 5
Quantified
Workforce
“We don’t know how to measure
what we care about, so we care
about what we measure”
- Richard Tapia
“Not everything that counts can
be counted, and not everything
that can be counted counts”
- Albert Einstein
Quantified
Consumer
Data Trail
IOT
Quantified
Process
You need to measure what is meaningful. You need to measure to find out what you need to measure that is meaningful.
SATYENDRA RANA – LOVEN SYSTEMS
Reflection
Reflection is a cognitive process
3rd Big Data & Business Analytics Symposium – March 24, 2016 6
Data Science is still an Art
1. An Opinion formed after a Careful Thought. 2. Learning from Experience
SATYENDRA RANA – LOVEN SYSTEMS
Adaptation
Adaptation is a Cognitive Process
Shameification
Why do we resist change?
Change is hard
 Fear of loss
• something of personal value
 Uncertainty
• about personal ability to cope
 Switch to survival instinct
• rationality goes out the door
 Not believing
• plans perceived as unrealistic Whatistheroleoftechnology?
SATYENDRA RANA – LOVEN SYSTEMS
Quantified Self-Movement
What can we learn?
3rd Big Data & Business Analytics Symposium – March 24, 2016 8
The quantified self-movement (QS) is about
acquiring self-knowledge for the end goal of gaining deeper insight about personal habits.
Taking a leap from quantified self to quantified enterprise is quite tempting.
Interesting Motivating Changing
 Is that how I am?
 I wonder!
 What if?
 Tracking reality vs
how I think about
reality?
 Non-judgmental
 Private
 It is in my interest
 I am in charge
 It really works
 Internal motivators
 Simplest rewards can
trigger dopamine
 Gamification
 Making aware of what needs
to change
 Help set incremental &
realizable goals
 Providing meaningful &
contextual recommendations
 Self-determination
 Empowerment
SATYENDRA RANA – LOVEN SYSTEMS
DIWO®
Cognitive Analytics Solutions Platform by Loven Systems
3rd Big Data & Business Analytics Symposium – March 24, 2016 9
DIWO’ism
 Capture Data with a Purpose (Opportunity Driven)
“Ask not what data can do for you,
ask what you can do if you have the data”
 Don’t wait for perfect data (Evolutionary)
 Real world is messy, so is its data trail. Let it not be an excuse
for inaction.
 One can start with partial and messy data & improve over time
 No need to re-learn what is known to work well (Knowledge-
Based & Contextual)
 (Semantics + Analytics) is much larger than the sum of its
parts.
 Trust can trump Quality (Empowering)
 A system with high quality may not be adapted for lack of trust
in its working
 Building trust is usually cheaper than perfection
 Let machine wake you up, then vice versa (Preventive)
 When in doubt, ask the machine. When you doubt the machine,
ask for evidence (Conversational)
SATYENDRA RANA – LOVEN SYSTEMS
3rd Big Data & Business Analytics Symposium – March 24, 2016 10

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Opportunity driven enterprise

  • 1. SATYENDRA RANA – LOVEN SYSTEMS Toward an Opportunity Driven Enterprise: Why & How? Satyendra Rana CTO - Loven Systems March 24, 2016 3rd Big Data & Business Analytics Symposium – March 24, 2016 1
  • 2. SATYENDRA RANA – LOVEN SYSTEMS Achieving Business Excellence Sustain ------------------ Grow 3rd Big Data & Business Analytics Symposium – March 24, 2016 2 Big Data People Processes Technology Value People, Process, Technology Synergy Big Data Phenomenon Quantified Enterprise Opportunity Driven Enterprise
  • 3. SATYENDRA RANA – LOVEN SYSTEMS Opportunity Driven Enterprise What’s different? 3rd Big Data & Business Analytics Symposium – March 24, 2016 3 Data Driven (Naïve Approach) • Starts with Data • First Milestone - Data Lake • Embellish by Data Visualization • User must understand Data • User reaches out to System (Discovery) • Produce Analytics • Develop Data Strategy (CDO) Opportunity Driven (Cognitive Approach) • Starts with Value • First Milestone – Opportunities Flow • Embellish by Natural Language Interaction • System understands Data • System reaches out to User (Guidance) • Consume Analytics • Develop Opportunity Management Strategy (COO)
  • 4. SATYENDRA RANA – LOVEN SYSTEMS Path to Excellence Enterprise Risk & Change Management 3rd Big Data & Business Analytics Symposium – March 24, 2016 4 “You cannot steer, what you cannot quantify” Foundation of quantification is measurement “What Gets Measured Gets Done” Quantification Reflection Adaptation
  • 5. SATYENDRA RANA – LOVEN SYSTEMS Quantification What should we measure? 3rd Big Data & Business Analytics Symposium – March 24, 2016 5 Quantified Workforce “We don’t know how to measure what we care about, so we care about what we measure” - Richard Tapia “Not everything that counts can be counted, and not everything that can be counted counts” - Albert Einstein Quantified Consumer Data Trail IOT Quantified Process You need to measure what is meaningful. You need to measure to find out what you need to measure that is meaningful.
  • 6. SATYENDRA RANA – LOVEN SYSTEMS Reflection Reflection is a cognitive process 3rd Big Data & Business Analytics Symposium – March 24, 2016 6 Data Science is still an Art 1. An Opinion formed after a Careful Thought. 2. Learning from Experience
  • 7. SATYENDRA RANA – LOVEN SYSTEMS Adaptation Adaptation is a Cognitive Process Shameification Why do we resist change? Change is hard  Fear of loss • something of personal value  Uncertainty • about personal ability to cope  Switch to survival instinct • rationality goes out the door  Not believing • plans perceived as unrealistic Whatistheroleoftechnology?
  • 8. SATYENDRA RANA – LOVEN SYSTEMS Quantified Self-Movement What can we learn? 3rd Big Data & Business Analytics Symposium – March 24, 2016 8 The quantified self-movement (QS) is about acquiring self-knowledge for the end goal of gaining deeper insight about personal habits. Taking a leap from quantified self to quantified enterprise is quite tempting. Interesting Motivating Changing  Is that how I am?  I wonder!  What if?  Tracking reality vs how I think about reality?  Non-judgmental  Private  It is in my interest  I am in charge  It really works  Internal motivators  Simplest rewards can trigger dopamine  Gamification  Making aware of what needs to change  Help set incremental & realizable goals  Providing meaningful & contextual recommendations  Self-determination  Empowerment
  • 9. SATYENDRA RANA – LOVEN SYSTEMS DIWO® Cognitive Analytics Solutions Platform by Loven Systems 3rd Big Data & Business Analytics Symposium – March 24, 2016 9 DIWO’ism  Capture Data with a Purpose (Opportunity Driven) “Ask not what data can do for you, ask what you can do if you have the data”  Don’t wait for perfect data (Evolutionary)  Real world is messy, so is its data trail. Let it not be an excuse for inaction.  One can start with partial and messy data & improve over time  No need to re-learn what is known to work well (Knowledge- Based & Contextual)  (Semantics + Analytics) is much larger than the sum of its parts.  Trust can trump Quality (Empowering)  A system with high quality may not be adapted for lack of trust in its working  Building trust is usually cheaper than perfection  Let machine wake you up, then vice versa (Preventive)  When in doubt, ask the machine. When you doubt the machine, ask for evidence (Conversational)
  • 10. SATYENDRA RANA – LOVEN SYSTEMS 3rd Big Data & Business Analytics Symposium – March 24, 2016 10