input to a decision process as suggested by the popu-
lar press. For example, we recently studied a major
North American financial institution as it consid-
ered a proposal to change its enterprise e-mail
platform from one technology to another. The orga-
nization had conducted two prior reviews of e-mail
systems from major vendors and had twice recom-
mended remaining with the existing supplier.
However, the head of a small but influential and
profitable division of the company advocated switch-
ing platforms in order to provide better integration
with a specialized tool used only within his division.
When asking staff to conduct the third major analy-
sis, a director of the company’s information
technology group recommended that the project
manager produce a report that would support a
change of vendor. A project team member told us,
“The executives have already made up their minds….
We are being told that this is the way that we are going,
we need to get on board — be team players — and
make the decision work out to be [the new choice].”
Clearly, the ideal evidence-based decision pro-
cess was subverted in this case by the perceived
requirement to marshal facts and analysis to sup-
port a decision that had already been made elsewhere
in the organization. We call this practice decision-
based evidence making and argue that it is more
widespread than many managers acknowledge. The
purpose of this article is to examine the practice and
to answer three fundamental questions:
■ Why does decision-based evidence making occur
in organizations?
■ Is decision-based evidence making necessarily bad?
■ And, if decision-based evidence making is inevitable in organizations, what can be done to lessen
its negative impacts?
Why Does Decision-Based
Evidence Making Occur?
Managers use different approaches when making
decisions, ranging from highly analytical and algorithmic to ad hoc and intuitive. Rather than
converging on a single best approach, most normative models of decision making emphasize context
and adaptability. The models suggest that a decision-making approach should be tailored to fit the
particular characteristics of the decision problem.
Thus, an algorithmic approach is well suited to
highly structured decision problems in which the
ends and means are well understood. Intuition and
bargaining are more appropriate for poorly structured decision problems with multiple, conflicting
ends and uncertain means.
The problem with the flexible, contextual approach is that the role of evidence is unclear. In some
cases, hard evidence is critical in determining a decision outcome. In other cases, evidence is merely
symbolic; it is used to lend legitimacy to the decision
and signal the rationality of the decision makers. In
this article, we impose structure on this loose continuum by identifying three distinct roles for evidence in
decision-making practice, depending on whether it is
being used to make, inform or support a decision. (See
“The Role of Evidence in Decision Making.”)
Make a Decision
Evidence is used to make a decision whenever the
decision follows directly from the evidence. For
example, consider the choice of the optimal location of a new distribution facility. The objective is
to minimize the cost of the facility, where cost is a
function of several quantifiable factors such as
route lengths, demand patterns, land availability
and local tax incentives. Qualitative and noneconomic factors do not fit well into this mode of
decision making and must be either ignored or
transformed into quantitative evidence through
“pricing out” or similar techniques. The objective
facts regarding each of the decision alternatives (the
potential facility locations) are then used as inputs
into an optimization algorithm, and the location
with the overall minimum cost is provided as output. The combination of data, a cost model and an
optimization algorithm make the decision with
minimal human intervention.
The success of evidence-based decision making
in highly structured environments such as location
planning and supply chain management is universally acknowledged. The recent push toward
evidence-based decision making in medicine suggests that even incomplete or provisional evidence
(expressed as probabilities) can be valuable in less-structured, ambiguous decision environments.
Indeed, while research shows that many managers
have yet to adopt analytic approaches, head-to-head
comparisons in which algorithmic, evidence-based
