208 • Supply Chain Risk Management: An Emerging Discipline
drivers without getting lost in the data. Scenarios are performed iteratively
to demonstrate value and self- fund subsequent improvement opportunities.
Many organizations start with spreadsheets as a proof- of- concept
(POC) and then migrate to some form of business intelligence tool to
perform more rigorous analysis. Why? According to the Hackett Group,
world- class procurement organizations on average spend less than 30%
of their time compiling data, compared with 60% for the bottom- quartile
companies. In other words, while typical companies still compile data,
world- class organizations spend more of their time analyzing the data and
making informed decisions. e CIO of a leading company argues that
75% of the eort and cost when managing data is process reengineer-
ing and data cleansing and creation, and the other 25% is the IT portion.
He further states that when people say their systems didn’t deliver, the
chances are they missed the 75% part they should have been working on.
An adoption process or continuum has emerged through observation of
big data and predictive analytic projects, or what IBM calls the Four E’s:
Educate, Explore, Engage, and Execute.” Figure11.1 depicts this emerging
adoption continuum. We’ll briey touch on the key elements of each stage.
Education is the rst stage in the continuum. Its primary focus is on
awareness and knowledge development. In this stage, most organizations
are studying the potential benets of big data technologies and analytics
and trying to better understand how big data can help address impor-
tant business opportunities. Also within the rst stage, the potential for
big data has oen not yet been fully recognized and embraced by busi-
ness executives. Exploration, the second stage, denes the business case
and roadmap. Almost 50% of respondents in an IBM study report formal,
ongoing discussions within their organizations about how to use big data
Focused on
knowledge
gathering and
market
observations
Developing
strategy and
roadmaps
based on
business
needs and
challenges
Piloting big
data initiatives
to validate
value and
requirements
Deploying two
or more big
data initiatives
and
continuing to
apply
advanced
analytics
Educate Explore Engage Execute
FIGURE 11.1
e Four E’s of big data adoption.
Using Big Data and Analytics to Manage Risk • 209
to solve important business challenges. Key objectives in this stage include
developing quantiable business cases and creating a big data blueprint or
roadmap. Most organizations in this stage are considering existing data,
technology, and skills and are contemplating where to start and how to
develop a plan aligned with their organization’s business strategy.
Engagement, the third stage, is about embracing the value of the data.
In this stage, organizations begin to prove the business value of big
data as well as performing assessments of their technologies and skills.
Companies in this stage usually have one or more proof- of- concept proj-
ects under way. ese companies are working within a dened scope to
understand and test the technologies and skills required to capitalize on
the new sources of big data.
Execute is the nal stage of the continuum. In this stage, big data and
analytics capabilities are more widely operational and implemented
within the organization. Many organizations here manage at least two or
more big data solutions at scale, which seems to be a threshold for advanc-
ing in this stage. e companies in the execute stage are leveraging big
data to transform their businesses and thus are deriving the greatest value
from their information assets.
Most organizations are in the early stages of big data development.
IBM’s research concludes that 24% of companies are focused on under-
standing the concept and have not begun the journey, while 47% are
planning big data projects and developing roadmaps. Another 28% of
companies are developing proofs of concept or have already implemented
full- scale solutions.
BARRIERS AND CHALLENGES MOVING FORWARD
e challenges to utilizing big data dier as organizations move through
each of the four stages as featured in Figure11.2. A consistent challenge,
regardless of stage, is the ability to articulate a compelling business case.
At each stage big data eorts rightfully come under scal scrutiny. e
current global economic climate and supply chain risk landscape has le
businesses with little appetite for new technology investments without
measurable benets. Aer companies begin their proof of concept, the
next biggest challenge is nding the right skill sets to operationalize big
data, including technical, analytical, and governance skills.
210 • Supply Chain Risk Management: An Emerging Discipline
As shown in Figure11.2, dierent obstacles surface as companies move
through the continuum
10
:
• Acquisition of data. Data are available from so many sources and
end users must constantly decide which will be useful.
• Choosing the right architecture. is involves balancing cost and
performance to obtain a platform based around programming tech-
niques far dierent from those of the normal desktop environment.
• Shaping the data to the architecture. is involves spending time
capturing, compiling, and uploading the data to be aligned with the
architecture. With all the new technology, transforming the data can
be a time- consuming process.
• Coding. is includes selecting a programming language, designing
the system, deciding on an interface, and being prepared for a rap-
idly changing environment.
• Debugging and iteration. is is the process of looking for errors in
code, architecture, and making modications quickly.
TOOLS, TECHNIQUES, AND METHODOLOGIES
SUPPORTING BIG DATA
Let’s prole the techniques that are being utilized by organizations running
big data projects. Figure11.3 gives us a sense of the tools and techniques
that are being leveraged. More than 75% of companies report using core
Educate Explore Engage Execute
Articulating a compelling business case
Understanding how
to use big data
Management focus and support
Data quality
Analytic skills
Technical skills
FIGURE 11.2
Big data primary obstacles. (Source: Adapted from IBM 2013 Big Data Executive Report.)
Using Big Data and Analytics to Manage Risk • 211
analytics capabilities, such as querying and reporting and data mining to
analyze big data, while more than 67% report using predictive modeling.
ese foundational methods are a pragmatic way to start interpreting
and analyzing big data. e need for more advanced visualization tech-
niques increases with the introduction of big data because datasets are
oen too large for business or data analysts to view. e next highest usage
of techniques and tool sets are optimization models and advanced analyt-
ics to better understand how to transform key business processes. Many
organizations are embracing simulation and pattern recognition to ana-
lyze the many multivariate, nonlinear relationships within big data.
As you can see from Figure 11.3, more and more organizations are
focusing on unstructured data to analyze text in its natural state, such
as transcripts from call center conversations. ese tools and techniques
include the ability to interpret and understand the nuances of language,
such as sentiment, slang, and intention. And with the tools emerging to
analyze these new and unstructured forms of data, it’s no surprise that the
skills to manage these techniques are in short supply. It seems apparent
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
Voice analytics
Video analytics
Streaming analytics
Geospatial analytics
Natural language text
Simulation
Optimization
Predictive modeling
Data visualization
Data mining
Query & reporting
IBM Big Data Executive Report 2013
Percentages %
FIGURE 11.3
Big data analytics capabilities and tools.
212 • Supply Chain Risk Management: An Emerging Discipline
that there are a host of tools and techniques emerging to support the big
data eort. Techniques such as standard reporting, ad hoc reporting,
query drill- down, cloud- based analysis, classical deterministic forecasting
techniques, predictive modeling, simulation, optimization, pattern recog-
nition, and articial intelligence are all coming on board at an accelerat-
ing rate.
Changing gears a bit, but still remaining in the tools, techniques, and
methodologies arena of big data, it appears that organizations that are
embracing Soware- as- a-Service (SaaS), or cloud- based technology, are
utilizing those tools much more pervasively throughout their organiza-
tions, partly because they are able to make better use of scarce IT skills.
11
We
mentioned earlier the lack of technical skills, in house, as an obstacle. Also,
it appears that organizations that leverage outsourced IT tools and consult-
ing skills are experiencing a much richer and more complete solution when
compared with organizations that are not using the SaaS approach.
Here is a quick- hit denition of the SaaS concept: With SaaS or cloud-
based business intelligence (BI), the soware itself is not licensed, owned,
or installed by the organization. Instead, the soware resides in a remote
third- party data center and the functionality provided by the soware is
accessed over the Internet and rented. is service is typically paid for as
a monthly subscription.
One research group has concluded that the use of SaaS to drive big data
analytics oers advantages across many dimensions.
12
More than 60% of
organizations using a SaaS solution were satised or very satised with ease-
of- use of this approach as opposed to only 41% of companies not using SaaS.
Just over 80% of SaaS BI users have access to drill- down to detail capability
as opposed to 58% for non- SaaS users. And just over 60% of SaaS BI users
are able to tailor their solution quickly as opposed to only 41% of non- SaaS
users. Companies that utilize SaaS BI tools say that they can nd informa-
tion they need in time to support their decisions within one hour of raw data
being captured, or what is called time- to- decision and time- to- value, 84% of
the time as opposed to only 70% of the time with organizations not using
SaaS BI. Finally, organizations that use the SaaS approach are 40% more
likely than others to exchange data openly and easily across business units.
Other ndings not mentioned here also reveal the value of Saas.
e idea of augmenting what you already have in house with third- party
companies is gaining traction, especially with analytics. Many companies
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