White Paper
The Essential Human
Role in Decision Making
for the Digital Enterprise
The user interface
for the enterprise.
The Essential Human Role in Decision Making For The Digital Enterprise
“Ninety percent of all the data in the world has been
generated over the last two years” (SINTEF, 2013).
According to IBM, the world’s population collectively
generates 2.5 quintillion bytes of data every day.
From the sensors in our homes to the GPS signals in
our phones to retail transaction records to photos
posted on Facebook, IoT sources are producing data
at an extraordinary rate through a growing number
of channels. Anything and everything “smart,” from
smart homes to smart grids, means greater data
volumes at exponentially accelerating speeds.
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The Essential Human Role in Decision Making For The Digital Enterprise
As data becomes bigger, people
become even more important.
Here’s why:
More data does not necessarily lead to better
decision making. In fact, it often multiplies missed
opportunities hidden under a deluge of numbers.
Even when you have the world’s best data, you
still need people to make it work. To survive the
data deluge and thrive in this new era of sensors,
interconnected devices and constant unstructured
informational streams, decision makers at all levels
need a new class of interoperable analytics solutions
that lets workers see and understand all of their
data where and when they need it. Empowered
workers with the right data collaborate effectively,
make smarter decisions and transform disembodied
data from bits and petabytes into the competitive
advantage it was always meant to be.
3
The Essential Human Role in Decision Making For The Digital Enterprise
4
Data is exponentially expanding.
Businesses must figure out how
to manage it.
Growth of Global data - Zettabytes
2010 Stored data* - Petabytes
40
4000
30
3000
20
2000
10
1000
0
0
2009
2011
2015
2020
rth
No rica
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Am
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Eu
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Ja
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Ind
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So rica
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Zettabyte=one million petabytes
Sources Nasscom-CRISIL GR&A analysis
*greater than / Sources Nasscom-CRISIL GR&A analysis
The pronouncements about just how much data from
how many sources vary far and wide, but they all agree
on two things—the data will be big and the sources will
be many. Gartner, argues that the Internet of Things (IoT)
installed base will grow to 26 billion by 2020 (Rivera &
Meulen, 2013). Wireless World Research predicts that
the number of wireless sensing devices will outnumber
people by a factor of 1000:1 by 2017 (Sørensen & Skouby,
2009). GE asserts that the “Industrial Internet” has the
potential to bolster global GDP by ten to fifteen trillion
over the next twenty years (GE Reports, 2013). Cisco
forecasts a nineteen trillion-dollar value creation by IoT
in 2020 (Chambers, 2014).
Consumers are becoming increasingly knowledgeable
about and comfortable with for-profit companies
aggregating and analyzing their data as well. Consumers
know and expect that companies will collect data about
them and appear to be just fine with this, as long as
the data collected is being put toward enhancing their
consumer experience. As Forrester argues, consumers’
rapid embrace of IoT technology brings with it a deluge
of data that retailers can and should use to better serve
their customers (Maple, 2015).
All of this data should be transformative—on businesses,
employees, operations, consumers and the bottom line.
In fact global management consulting firm A.T. Kearney
argues, developing an organization’s data capabilities
can and should greatly improve performance, while
simultaneously opening up the possibility to expand
offerings and services (Hagen, et al., 2013).
The Essential Human Role in Decision Making For The Digital Enterprise
5
2011 and 2017 Big Data
Analytics Adoption Rates Forecast
2011 and 2017 Big Data Analytics Adoption Rates Forecast
70%
2011
60%
2017*
50%
40%
30%
20%
10%
0%
t
en g
stm kin
e
n
Inv Ba
s
e
om
nc
a
ur
Ins
Source: Wikibon 2015
c
ele
T
* Estimated
il
&
le
re
ta
rt
sa e
ca
Re ing
po tics
lth
ole rad
s
k
a
h
s
n
i
n
T
a
W
He
Ba
Tr Log
il &
ta
e
R
l
ra
nt t
Ce men
n
er
ov
r
he
Ot ties
i
tiv
Ac
G
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na
ing
y&
ur
sio ices
rg ties
t
s
e
c
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fa
En tili
of rv
U
nu
Pr Se
Ma
Big Data Market Forecast, 2011-2026 ($US B)
$90.00
$80.00
$81.78
$84.69
$78.53
$70.00
$74.88
$70.76
$66.11
$60.00
$60.91
$50.00
$55.22
$49.28
$40.00
$43,40
$37.97
$30.00
$33.31
$27.36
$20.00
$19.60
$10.00
$12.25
0
$7.60
2011
2012
Source: Wikibon 2015
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
The Essential Human Role in Decision Making For The Digital Enterprise
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The Evolution of Data
Data size
and complexity
Data driven
3
Very
Complex,
Unstructured
Structured data
Unstructured data
Multimedia
Relational+
2000s and beyond
Data utilization
2
Complex
Relational
Data generation
and storage
Primitive
and
Structured
1
Relational databases
Data-intensive
applications
Relational
1980s and 1990s
Mainframes
Basic data
storage
Pre-relational
1970s and before
Computing timeline
Source: A.T. Kearney Analysis
Exponential growth
in data volume
The Essential Human Role in Decision Making For The Digital Enterprise
7
Gleaning insights from vast arrays
of data will be a key business differentiator
in the coming decades.
How important is Big Data to your organization?
Overall
More than $10B
$5B-$10B
$1B-$5B
$500M-$1B
$250M-$500M
59%
34%
67%
28%
61%
36%
58%
36%
59%
34%
43%
Extremely Important
4%
43%
Important
12%
Moderately Important
Non Very Important
Source: http://smartdatacollective.com/bigdatastartups/201286/why-ups-spend-over-1-bilion-big-data-annually
Source: Accenture Big Success with Big Data Survey, April 2014
However, as companies continue to rapidly advance data
collection, most of them have not altered how they work
with and in this data rich environment. The International
Data Corporation estimates that approximately 33% of
all data contains useful information, awaiting for proper
analysis (Mearian, 2012). Organizations face myriad
and multiplying issues analyzing the data, though. In
an Avanade survey, more than sixty percent of C-level
executives said their employees need to develop new
skills in order to translate data into business value
(Avanade, 2012). Moreover, McKinsey believes the only
remedy for this dearth will be creating hundreds of
thousands of new data analysis experts (Lohr, 2012).
As the Harvard Business Review indicates, the growth
in data means that analyzing large, messy and
unstructured data will become a fundamental aspect
of how most of us work (Harris, 2012). Organizations
that are effective and efficient at analyzing large data
sets will form a foundation of organizational growth
and innovation (Manyika, 2011). Yet, while nearly every
private and public sector organization—from retail
to healthcare—has been impacted by these changes,
finding a common playbook on how to address these
data dynamics remains elusive.
The Essential Human Role in Decision Making For The Digital Enterprise
8
Data growth = New ways
of working
As things currently stand, many organizations don’t
know where to begin in dealing with such large amounts
of data. To many, it simply doesn’t seem possible that
a typical office worker, still working with bar charts
and Excel graphs, will ever have the tools necessary to
actually exploit the purported power in all of this data.
Because of this, artificial intelligence could be a catalyst to
harness data’s power. As Babson professor and analytics
expert Tom Davenport says in the Harvard Business
Review, machines will soon go from mere support tools
to actually replacing humans as decision makers, “Now
companies have needs for greater productivity than
human quants can address or fathom. They have models
with 50,000 variables. These [AI] systems are moving
from augmenting humans to automating decisions”
(Power, 2015).
Some even go so far as to argue that it is “impossible”
for humans to understand the data. Technology
executive Mark Jaffe maintains, “It’s simply impossible
for humans to review and understand all of this dataand doing so with traditional methods, even if you
cut down the sample size, simply takes too much
time.” In light of all of this, we need new ways to
work. Those advocating for a full-fledged shift to AI
decision making make very important points in this
regard. Most organizations do not have systems in place
to handle all of these data streams, and AI certainly
can process information with a velocity humans simply
cannot match.
These conclusions depend on two assumptions: that
the tools people use to understand data, and the
organizations that utilize them, remain as they are.
Which Artificial Intelligence Categories Are Seeing The Most Innovation?
179 Machine Learning (Applications)
127
95
90
38
0
Computer Vision (General)
Machine Learning (General)
71
Computer Vision (Applications)
70
Virtual Personal Assistants
65
50
Speech Recognition
Recommendation Engines
Smart Robots
30
Gesture Control
28
Context Aware Computing
15
Speech to Speech Translation
14
Video Content Recognition
20
by Venture Scanner
40
Natural Language Processing
60
80
100
120
140
160
180
200
Company Counted
The Essential Human Role in Decision Making For The Digital Enterprise
Is artificial intelligence our
only means to harness data’s power?
Humans are and will continue to be vital to organizations
because of two key factors that machines cannot ever
replicate or supersede: judgment and accountability.
No matter the number of correlations one discovers–
and machines can certainly detect a great many–
judgment will ultimately determine the meaning of data
points and how to act on them. Machines won’t tell us
which correlations are meaningful (Marcus & Davis,
2014). Moreover, accountability gives decisions weight
and value. Without an accountable party, organizational
chaos would ensue. What we need is a way to integrate
data into daily decision-making processes (Shah, Horne,
& Capella, 2012).
Many businesses fail not because their data was
necessarily faulty or incomplete, but because they
failed in judgment. Take Blockbuster, now widely seen
as having missed out on the changes wrought by
competitors like Netflix. Blockbuster went down not for
lack of data, but rather a lack of judgment over what
to do with the data. To Blockbuster executives, the data
suggested the company’s core customers were middleaged parents who had little interest in streaming or
the niche movies. As a result, Blockbuster focused on
the issue of providing parents something quick for the
kids. So this was the experience Blockbuster tried to
improve on—ultimately mistaking the trees for the forest.
It was impaired judgement that led them to focus on the
wrong data.
Data solutions should give users the power to make
better decisions. More data certainly would not have hurt
the Blockbuster executive team. However, no solution
can ever make the decisions for them.
An ideal data solution manages data as well as an AI platform, but acknowledges the necessity of human judgment
and accountability. In essence, the ideal data platform should include:
The ability to bring all data sources, no matter the
format or type, together into a single view
Allow for quick sifting and sorting
The ability to rapidly iterate with large amounts of data in order
to develop multiple perspectives, models and hypotheses quickly
Allow for quick collaboration and sharing across an organization
Provide for application of judgment and accountability
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The Essential Human Role in Decision Making For The Digital Enterprise
Human judgment and
accountability are vital to getting
the most out of data.
Only one solution accomplishes all of these objectives:
Conduce. Conduce offers the first immersive operational
data visualization platform. Leaders and teams can see
and interact with all their data instantly using a single,
intuitive interface.
With Conduce, decision makers see internal and external
data from any source, explore it fully and unlock real value.
No other solution combines secure, transactional business
tools with advanced gaming technology to activate the
power of data across any enterprise, no matter how global
or complex.
Conduce technology traces its origins to the unforgiving
environments of defense and intelligence. This sector has
critical specialized requirements to provide immediate,
comprehensive situational awareness necessary to make
vital life and death decisions. Conduce architected its first
technology to provide decision makers with informational
depth and agile analysis on a single pane of glass to allow
military and intelligence professionals to make smarter
decisions by exercising better informed judgment.
Conduce’s original military and intelligence platform served
three fundamental contexts: operational monitoring and
analysis, rapid data synthesis of all available sources to
enable better planning and decision making, and spur-ofthe-moment tactical management for critical tasks amid
the fog of war. Recognizing that these contexts were
inextricably interwoven, Conduce built fluid movement
between these three modes into the system, given that
contexts and situations are always shifting.
Although civilian organizations may not face the same
types of life and death decisions, their requirements are
no less complex or critical. As this paper has emphasized
throughout, businesses of all types and sizes face similar
requirements and informational needs, from bringing
siloed data together into a single platform to enabling
quick and effective decision making and collaboration.
Conduce transferred its proven performance in synthesizing
operational, strategic and tactical contexts to a wide variety
of enterprises outside defense and intelligence including
retail, CPG, energy, travel, manufacturing and more.
Conduce technology unifies data from any internal or external source in a single, intuitive operational interface.
Some of the key features of Conduce include:
Collaborate instantly and securely. Any and all team members are able to view
and act on the same data at the same time — all securely in the cloud.
Drive measurable business outcomes by viewing the entire picture in real-time
— assets in motion, changing KPIs, IoT telemetry — to learn what is happening
locally or across the enterprise.
Explore many data sources simultaneously by moving freely through time and
space with infinite zoom.
Realize value from previous technology investments by using existing tools to
view data in a new way.
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The Essential Human Role in Decision Making For The Digital Enterprise
Conduce is battle management for
business that increases worker and AI
productivity alike.
Current decision making complexity demands the same
level of battle management control for any organization,
no matter the sector. General McChrystal makes this point
in his book on organizational strategy, Team of Teams,
wherein he argues that all organizations, not merely the
military, must move beyond siloed data and hierarchical
decision-making structures, to interoperable data systems
and empowered decision making throughout. Conduce
readies any organization to make any decision, whether on
the battlefield or elsewhere.
As McKinsey argues, while IoT opens up the potential for
new business models that can fundamentally alter the
competitive landscape, companies cannot succeed without
the tools to take advantage of it. Tools like Conduce
make them battle ready. According to McKinsey, a full
forty percent of IoT’s value lies in the interoperability that
tools like Conduce provide. Moreover, McKinsey believes
that an even greater piece of value resides in enabling an
organization to make decisions and ultimately act on IoT
data, which is exactly what Conduce is built for (Manyika &
Chui, 2015).
Conduce allows businesses to comprehend data in ways
that have never been previously possible. With Conduce,
organizations are able to harness the full power of AI
platforms, while multiplying the capabilities and talents of
their human workforce. IoT elements are made interoperable
with one another; AI resources are able to perform their
sifting, sorting and calculation; and people are ultimately
empowered to make the necessary decisions. With data
reporting diversity, decision makers will find themselves
favoring information that comes from fancier machines.
Conduce levels the playing field by accessing all the data
and presenting it uniformly. Less bias = smarter decisions.
Data’s value will continue to increase into the future. But
the even bigger opportunity is to improve the effectiveness
and impact that people will have with access to the right
resources and tools. The real question is not the false
dichotomy of human or machine, but how best to create a
structure that can undergird the inherent powers found in
each. The creative judgment that only people can exercise
in concert with the calculation speed of computers creates a
mutually reinforcing system between information, machine,
and human. A system animated through tools like Conduce.
How people maximize the value of data:
Understanding
Creativity
People develop shared awareness
through an ongoing process of
collaborating to solve ever-shifting
objectives.
People have the ability to ask
questions of data sets in completely
new and unexpected ways, which
maximizes data’s value.
Judgment
Accountability
The human ability to select from
alternatives based on the context of
an organization and environment is
irreplaceable.
Only people can be held accountable
for the decisions made by and within
an organization.
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The Essential Human Role in Decision Making For The Digital Enterprise
12
Works Cited
Avanade . (2012, June). Global Survey: Is Big Data Producing Big Returns? Retrieved from http://www.avanade.com/~/media/documents/research%20and%20insights/avanade-big-data-executive-summary-2012.pdf
Chambers, J. (2014, January 15). World Economic
Forum. Retrieved from Are you ready for the Internet of
everything?: https://agenda.weforum.org/2014/01/areyou-ready-for-the-internet-of-everything/
GE Reports. (2013, October 7). Retrieved from Analyze
This: The Industrial Internet by the Numbers & Outcomes: http://www.gereports.com/post/74545267912/
analyze-this-the-industrial-internet-by-the
Hagen, C., Ciobo, M., Wall, D., Yadav, A., Khan, K.,
Miller, J., et al. (2013). Big Data and the Creative
Destruction of Today’s Business Models. Retrieved
from A.T. Kearney: https://www.atkearney.com/
documents/10192/698536/Big+Data+and+the+Creative+Destruction+of+Todays+Business+Models.pdf/
f05aed38-6c26-431d-8500-d75a2c384919
Harris, J. (2012, September 13). Data Is Useless Without
the Skills to Analyze It. Retrieved from Harvard Business Review: https://hbr.org/2012/09/data-is-uselesswithout-the-skills
IBM. (n.d.). What is big data? Retrieved June 23, 2015,
from http://www-01.ibm.com/software/data/bigdata/
what-is-big-data.html
Maple, T. (2015, June 4). Internet Retailer. Retrieved from The Internet of Things: It’s more about
services than things: https://www.internetretailer.
com/2015/06/04/internet-things-its-more-about-services-things
Marcus, G., & Davis, E. (2014, April 6). Eight (No, Nine!)
Problems With Big Data. Retrieved from New York
Times: http://www.nytimes.com/2014/04/07/opinion/
eight-no-nine-problems-with-big-data.html
Mearian, L. (2012, December 11). By 2020, there will be
5,200 GB of data for every person on Earth. Retrieved
from Computer World: http://www.computerworld.
com/article/2493701/data-center/by-2020--there-willbe-5-200-gb-of-data-for-every-person-on-earth.html
Power, B. (2015, March 19). Artificial Intelligence Is
Almost Ready for Business. Retrieved from Harvard
Business Review: https://hbr.org/2015/03/artificial-intelligence-is-almost-ready-for-business
Rivera, J., & Meulen, R. v. (2013, December 12). Gartner
Says the Internet of Things Installed Base Will Grow
to 26 Billion Units By 2020. Retrieved from Gartner:
http://www.gartner.com/newsroom/id/2636073
Rivera, J., & Meulen, R. v. (2014, March 19). Gartner Says
the Internet of Things Will Transform the Data Center.
Retrieved from Gartner: http://www.gartner.com/newsroom/id/2684616
Lohr, S. (2012, February 11). The Age of Big Data.
Retrieved from New York Times: http://www.nytimes.
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Sørensen, L., & Skouby, K. E. (2009, July). Outlook:
Visions and Resaerch Directions for the Wireless World.
Retrieved from World Wireless Research Forum: http://
www.wwrf.ch/files/wwrf/content/files/publications/
outlook/Outlook4.pdf
Manyika, J. (2011, May). Big data: The next frontier for
innovation, competition, and productivity. Retrieved
from McKinsey: http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
Shah, S., Horne, A., & Capella, J. (2012, April). Good
Data Won’t Guarantee Good Decisions. Retrieved from
Harvard Business Review: https://hbr.org/2012/04/
good-data-wont-guarantee-good-decisions
Manyika, J., & Chui, M. (2015, July 22). By 2025, Internet
of things applications could have $11 trillion impact. Retrieved from Mckinsey: http://www.mckinsey.com/insights/mgi/in_the_news/by_2025_internet_of_things_
applications_could_have_11_trillion_impact
SINTEF. (2013, May 13). Big Data, for better or worse:
90% of world’s data generated over last two years.
Retrieved from Science Daily: www.sciencedaily.com/
releases/2013/05/130522085217.htm
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The user interface
for the enterprise.