de cis io n a na lys i s
Bridging the Gap between Data
and Decisions
Indeed, these three decision support
features – stakeholder group collaboration, risk preferences and multiple objective analysis – are just some of the
techniques that help to bridge the gap
between information (i.e., data) and informed decision-making. A quick survey
of recent literature indicates that the need
to do so is readily apparent. According to
research by the Economist Intelligence
Unit (EIU) and PricewaterhouseCoopers (PWC), “experience and intuition,
and data and analysis, are not mutually
exclusive. The challenge for business
is how best to marry the two…even the
largest data set cannot be relied upon
to make an effective big decision without human involvement.” [3] The same
study also found that executives were
skeptical of how data and analytics can
assist big decisions, especially with regard to emerging markets.
In fact, these “big decisions” (i.e.,
more strategic level problems) are
where decision-makers themselves are
often unclear of their risk preferences
and where data insights alone may not
lead to a clear choice of alternatives for
meeting their objectives. As opposed to
operational level decisions that can be
informed more directly by descriptive
types of data analytics, these strategic
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problems often require decision professionals to meet their customers halfway
between the data and the decision. They
require an analyst not only to accurately
interpret the data available, but to also
demonstrate how it can illuminate a customer’s understanding of their own preferences and objectives, which until that
point were not readily apparent.
So what is the decision analysis community to do in challenging environments
of strategic decision-making? This survey’s list of software products provides a
great starting point. Ultimately, however,
software cannot do it alone – decision
professionals bear the ultimate responsibility. As strategic consultant Dhiraj
Rajaram explained in his October 2013
article in Analytics magazine:
“Leveraging data effectively to
enable better decisions requires
more than just data sciences...
In the real world, however, not all
business problems are clearly defined. Many of these problems start
off muddy. To help solve them, one
needs to understand and appreciate the business context. It requires
an interdisciplinary approach consisting of several different skills:
business, applied math, technology and behavioral sciences.” [4]
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