International Journal of Business Intelligence Research, 1(1), 1-12, January-March 2010 1
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Keywords: Business Intelligence, Decision Automation, Decision-Making, Decision Support
introduCtion
For most of the five-decade period begin-
ning in the mid-1950s, the primary focus of
information systems has been on automating
core business processes. The era began with
custom-developed narrow-purpose applications
and concluded with broad enterprise system
packages provided by external vendors, but
the purpose was the same: develop control
and efficiency over processes by automating
and capturing information from key business
transactions. Whether a general ledger entry, a
customer order captured, or a vacation balance
debited, the transaction has been the primary
unit around which this world revolved.
By now, however, many organizations
have mastered basic transactions, and are at-
tempting to use the accumulated information
from transaction systems to optimize decisions
about the management of the business. From
the early 1970s (Gorry & Scott-Morton, 1971),
the idea was that better information would lead
to better decisions and better ways of managing
organizational processes. Whether this idea
was called decision support, executive sup-
port, online analytical processing, or business
intelligence (Power, 2007), there was always
another goal waiting to be achieved. Because
organizations’ efforts and attentions were be-
ing spent on automation, to improve decision-
making was never the primary focus. Today it
has taken center stage. Business intelligence
applications have become the top spending
Business intelligence and
organizational decisions
Thomas H. Davenport, Babson College, USA
aBstraCt
The focus on transactional systems in the earlier decades of information management is beginning to shift
toward decisions. In order to study the relationship between information and decisions, the author interviewed
32 managers in 27 organizations where an attempt to use information to support decision-making had been
made. A framework involving three different relationships between information and decisions is introduced:
loosely-coupled, structured human, and automated. It is suggested that loosely-coupled information and
decision environments, while productive for information providers, may require too much knowledge on the
part of information users to be effective. A four-step process for bringing information and decisions in closer
alignment is also advanced.
DOI: 10.4018/jbir.2010071701
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