Günümüz dünyasında, tüm insanlığın 20 yüz yıl boyunca ürettiği veri miktarı kadar veri üretiliyor! Heyecan verici ve neredeyse bağımlılık yaratan bir Büyük Veri çağına giriyoruz. Peki, veriyi iş hayatında daha iyi karar verebilmek için “yeterince” kullanabiliyor muyuz? Dr. Milo Jones, Büyük Veri’nin vaatlerini ve tehlikelerini, veri işleyen ilk kurumlar olan istihbarat bakış açısıyla keşfediyor. Bu keşifle birlikte ortaya çıkardığı 4 temel soruyla, Büyük Veri’nin özellikle iş insanları ve firmalar için ne anlama geldiğini cevaplamaya çalışıyor.

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Big Data: Four Problems to Consider
Prof. Milo Jones
IE Business School

2. © 2017 Milo Jones
Who am I?
» I am:
 American; grew up in New York, Wisconsin & Virginia
 BA Northwestern, Art History
 US Marines; then archaeology, then broker at Morgan Stanley in NY
 London Business School MBA
 Accenture, London: Organizational Strategy and Change
 Dot.com startup; Masters & PhD in International Relations
 PhD focused on intelligence analysis, strategic surprise and internal culture
of the CIA
 Now: I live in Warsaw; I run a consulting company called Inveniam Strategy;
I am a non-Executive Director of US FMCG company; I teach at IE about
eight weeks a year.

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Who am I?

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Big Data = Wisdom?

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The Role of Theory

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Big Data = Wisdom?
» A story from 2012…

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What do we mean by “Big Data”?
» Not just everyone, but everything is generating (or will be
generating) recordable data
» We may be at another moment when we can “see the invisible”

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Problem One
We have the wrong definition of “smart”

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Problem One
» We have the wrong definition of “smart”
» Students are taught to give good answers, but not to ask good
questions!

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Problem Two
“Collection” of data does not prevent strategic
surprises

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A little reminder

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The Lessons of Pearl Harbor

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Is collection of data the problem?
» Hypotheses are used to sort “Signal” from “Noise”

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The “normal” intelligence cycle
Tasking
Collection
Analysis
Production
Dissemination
The Intelligence Cycle
This is almost
never the
problem!

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W. Edwards Deming, revisited
» Deming: “Without data you’re just a person with an opinion.”
» When Big Data is everywhere: “Without an opinion you are just
another person with data”!
» You need hypotheses (i.e. questions) to sort “signals” from “noise”
» Surprises come from “Failures of imagination”

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“Failures of imagination” – no one to blame?

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Problem Three
Hypotheses and “Failures of Imagination” do not
come from nowhere

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But does that explain enough?
» What can we learn from “Cassandras”?

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The identity and culture of the CIA

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Tasking
Collection
Analysis
Production
Dissemination
A New Intelligence Cycle
Hypotheses
Your Identity
and culture
shape these
Identity and Culture shape Hypotheses (and Surprises)

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A sad example

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Who is a Cave Man?
» Implication: diversity is a practical concern!

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Problem Four
“Centaurs” are spreading

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1997
©2015 Inveniam Strategy

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2011
©2015 Inveniam Strategy

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2016

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Problem 4: The Rise of Centaurs

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Problem 4: The Rise of Centaurs

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» “A weak human + a machine + a good process is superior to…
» To a strong computer alone and…
» (more remarkably)…
» To a strong human + a machine + an inferior process.”
» Big Data + AI = Centaurs in every field of business
» But the challenge is more complex than “analytics”
Garry Kasparov
“The Chess Master and the
Computer”
Problem 4: The Rise of Centaurs

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Practical Implications

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Practical Implications
» Job One: remove friction in the interface between your team and
their machine/Big Data assistants!
» Job Two: make sure you design processes to create data sets
worthy of intelligent machines: polluted or suspect data wastes
everyone’s time and people debate data, not strategy!

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» Attack exceptions/ interrogate anomalies
» Get good at structuring rigorous discussion of soft data
» Constantly refine the soft skills - inspiring your associates,
empathizing with customers, developing talent, etc.
» Giving context to small-scale machine-made decisions is likely to
be vital to maintain team morale
Practical Implications

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Summary

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Summary – The Challenge of Big Data
»Problem One: Being smart used to mean giving “good
answers”; increasingly, it will mean asking good
questions
»Problem Two: Collecting data does not prevent
surprises
»Data rarely “speaks for itself”, it needs hypotheses
»Problem Three: Both hypotheses and “Failures of
Imagination” come from your culture and identity
»That means that diversity is a practical issue for
identifying both opportunities and risks

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Summary – The Challenge of Big Data
»Problem Four: Meanwhile, “Centaurs” (teams of
humans and computers), are using Big Data and moving
into every field of management
»Management needs to focus the processes of human
and machine interaction – this is more than “analytics”
»Once you have clean data…
»Soft skills will be increasingly important for leaders!

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Thank you!
Please Connect: mjones@faculty.ie.edu
Linkedin: https://www.linkedin.com/in/inveniam
Twitter: https://twitter.com/Inveniam