Data, Big Data, Data Science, Analytics, and Visualization are necessary competencies, but they are not sufficient. Without bridging the last mile, these competencies are like heating water to 211 degrees Fahrenheit. Not bad, but not good enough. What's the extra 1 degree to get the water to really boil and steam? Decision-making! You have to bridge that very last mile - bringing the result of all the data competencies to help companies decide and solve problems. You have to consider judgment under uncertainty, biases, and heuristics in decision-making. Without that, it's just a lot of hot air!

1. Decision-Making: The Last Mile
of Analytics & Visualization
The problem that just won’t go away!
Kiran Garimella, Ph.D.
Principal Consultant, XBITALIGN
Excellence in Business & IT Alignment
© XBITALIGN

2. Theme
The history of “New” things
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"The lifecycle of data to decision begins with data, moves on to
information, analysis, visualization, and finally action or decision. The path
is fraught with multiple challenges where each challenge has become the
rallying cry for a new technology, acronym, or concept. Examples are
metadata, taxonomies, master data, data quality, data ownership, business
intelligence, predictive analysis, big data, visualization, data science, etc.
The topic that is significantly underserved in all this is the actual usage of
the end result in decision-making. What are the challenges in the last mile
of the lifecycle? Without a good solution to meeting the challenge of
decision-making, the rest of the phases are analogous to producing a
Ferrari without giving the user any drivers' education."
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3. Many “New, Shiny Objects”
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Fads and fashions?
Data, BI, metadata, master data, Big
Data, analytics, data science, etc.
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4. The main thing
It isn’t about technology, but what’s in it for the decision-makers.
My stakeholder – Vice Chairman of GE – said to me:
“20 years ago, my MIS department would put in front of
me, every morning, a reliable report about revenue and
other metrics from various regions based on products
and services. It looks like that’s not possible anymore.”
If you can’t help decision-makers make better decisions faster while
minimizing risk, you have done nothing.
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5. What we give them: CTAs
(Collage of Terrifying Acronyms)
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WSDL
SOA
OWL
BAM
BI
CAF Portals
ESB
BPMN
BPEL
JSR-168
XPDL
AJAX
WYMIWYR
CMS
EAI
Web 2.0
BPEL
Cloud
Social
Complex Event Processing
ICE
PLM
PPM
SAAS
Agile
Big Data
jquery
Analytics
Hadoop
python
scala
BPM
d3js
wsdl
Mobile
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6. WHAT THEY - YOUR USERS - CARE ABOUT
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Process Cycle Time
Throughput Yield
Bottlenecks
Wait-times
Defects per million opportunities
Latency Process Variance
Inventory Turns
SLA Violations
False Demand Triggers
Return Rate
Percentage Rework
Cost of Poor QualityUnnecessary Motion
Excess Processing
Time to Completion
Economic Value Add
Transportation Waste
Process Variance
Process Capability
Process Capacity
Excess Transactions
Root Cause
Voice of the Customer
Run Chart
Critical-to-Quality
Reduction of Waste
Overall Equipment EffectivenessKey Performance Indicators
Baseline Conditions
Compliance
Citizen Satisfaction
Tax Dollar Efficiency
Customer Satisfaction
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7. The lifecycle of data
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Raw data
generation
Extraction
Collection
Cleansing
Analyzing
Packaging/
(Information)
Consuming
Decisioning
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8. The main thing – the root
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Data shows errors due to decision-making have remained
flat, contributing to an increasing number of accidents.
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9. The main thing’s root: thinking & deciding
Technology in data gathering, storing, analysis, and
visualization had made tremendous progress.
But what about the human’s ability to think and decide?
Would you put a kid in a Ferrari?
9© XBITALIGN

10. The Analytic Landscape
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11. Common mistakes – “Abuse of statistics” - Samples
Issue Data analysis techniques
Example of abuse Correct technique
To study factors that “influence” visitors to
come to a recreation site
Likert scaling based on
interviews
Data tabulation based on
open-ended questionnaire
survey
Measure the “influence” of a variable on
another
Using partial correlation
(e.g. Spearman coeff.)
Using a regression
parameter
Finding the “relationship” between one
variable with another
Multi-dimensional scaling,
Likert scaling
Simple regression
coefficient
To evaluate whether a model fits data
better than the other
Using R2 Many – a.o.t. Box-Cox 2
test for model equivalence
To evaluate accuracy of “prediction” Using R2 and/or F-value of a
model
Hold-out sample’s MAPE
“Compare” whether a group is different
from another
Multi-dimensional scaling,
Likert scaling
Many – a.o.t. two-way
anova, 2, Z test
To determine whether a group of factors
“significantly influence” the observed
phenomenon
Multi-dimensional scaling,
Likert scaling
Many – a.o.t. manova,
regression
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12. Analytic heuristics
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Representativeness
Insensitivity to prior probability
of outcomes
Insensitivity to sample size
Misconceptions of chance
Insensitivity to predictability
Illusion of validity
Misconceptions of regression
Availability
Biases due to retrievability of
instances
Biases due to effectiveness of
search set
Biases of imaginability
Illusory correlation
Anchoring / adjustment
Insufficient adjustment
Biases in the evaluation of
conjunctive & disjunctive events
Anchoring in the assessment of
subjective probability
distributions
Source: “Judgment under uncertainty: heuristics and biases”, Kahneman, Slovik, and Tversky,
© XBITALIGN

13. The role of heuristics
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Biases in judgmental heuristics are universal
They have nothing to do with wishful thinking
Payoffs don’t influence them
Laypeople and experts are equal victims
Correction of biases in critical
- Biases do not practice discrimination!
- They are unconscious or subconscious!
- Beating people up or paying them won’t eliminate biases!
- Have a Ph.D. or a Nobel Laureate? Sorry, it doesn’t make you immune!
- Not addressing the problem is not a choice!
© XBITALIGN

14. How to bridge the last mile
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Users must be trained to be aware of:
 limitations of analytical models
 tendency towards logical fallacies
 biases in judgmental heuristics
 The experts must be more diligent in applying the right
analytical models
 They need to present the caveats with the results
 They need to interpret the results in business-speak
 They need to present choices
 They need to explain the risks
© XBITALIGN