value insights

Agile Corporate Decision Making- Valutrics

Most information systems in use today are opaque and hard to change.
The use of programming languages—code—to specify their behavior
makes them opaque to any but the most technically adept. This opacity,
and the difficulties of confirming that changes made to the code do what
they are expected to do, make for long change cycles and a lack of
responsiveness. The combination means that extensive information tech-
nology projects must be planned, budgeted, and executed to make
changes to the behavior of a system.
These characteristics are unacceptable in a Decision Management
System. Opacity is unacceptable because many decisions must demon-
strate that they are compliant with policies or regulations. If the code is
opaque, then it will not be possible to see how decisions have been made
and it will not be possible to verify that these decisions were compliant.
Decision Management Systems also make decisions that are based on
detailed business know-how and experience. If the code is so opaque that
it cannot be understood by those who have this know-how or experience,
then it is unlikely to be correct.
Organizational decision-making changes constantly, so agility is also
essential. As regulations change, the behavior of any Decision
Management System that implements that regulation must also change.
Organizations also want Decision Management Systems to make good
decisions—effective ones. Effective decisions based on the expectations
of customers must be competitive, yet the behavior of competitors and
customer expectations change constantly. And moreover, customers and
competitors are not obliged to tell organizations when their expectations
or plans change.

An increase in transparency is likely to result in an increase in busi-
ness agility—if it is easier to see how something works, it will be easier
to change how it works when this is needed. A faster response to a
needed change improves overall business agility. Transparency is neces-
sary for agility but not sufficient. Once a change is identified and its
design impact assessed, it must be possible to make the change quickly
and reliably. Decision Management Systems can require real-time
changes to their behavior in extreme cases. Daily or weekly changes are
very common. When sudden market changes occur, such as major bank-
ruptcies or an outbreak of hostilities, the resulting need for changes to
Decision Management Systems can be extreme. Money—and perhaps
lives—will be lost every minute until the change is made.


In recent years, organizations have spent heavily on technology for
managing and using data. Beginning with Database Management
Systems and moving through Information Management, Data Quality
and Data Integration to Reporting, and Dashboards, these investments
are now mostly classified as Business Intelligence and Performance
Management. These investments have taken data that was once hidden
in transactional systems and made it accessible and usable by people
making decisions in the organization.
These investments have been focused on analyzing the past and pre-
senting this analysis to human users. They have relied, reasonably
enough, on their human users to make extrapolations about the future.
Users of these systems are making decisions based on this data, using what has happened in the past to guide how they will act in the future.
Many of these systems can also bring users’ attention to changes in data
quickly to prompt decision-making. The value of this investment in
terms of improved human decision-making is clear.
These approaches will not work for Decision Management Systems.
When a decision is being automated in a Decision Management System,
there is no human to do the extrapolation. Passing only historical data
into a Decision Management System would be like driving with only the
rear view mirror—every decision being made would be based on out-of-
date and backward-looking data. It fact it would be worse, as a human
driver can make guesses as to what’s in front of her based on what she
sees in a rear view mirror. She will be reasonably accurate too, unless the
road is changing direction quickly. Systems are not that smart—without
people to make extrapolations from data, Decision Management Systems
need to be given those extrapolations explicitly. Without some view of
the future and the likely impacts of different decision alternatives, a
Decision Management System will fail to spot opportunities or threats
in time to do anything about them.
Predicting likely future behavior is at the core of using predictions in
Decision Management Systems. You need to predict individual customer
behavior such as how likely they are to default on a loan or respond to a
particular offer. You need to predict if their behavior will be negative or
positive in response to each possible action you could take, predicting
how much additional revenue a customer might generate for each possi-
ble action. You want to know how likely it is that a transaction repre-
sents risky or fraudulent behavior. Ultimately you want to be able to
predict the best possible action to take based on everything you know by
considering the likely future behavior of a whole group of customers.

Most repeatable decisions do not have a huge economic impact indi-
vidually. Despite their limited scope, many do have a significant gap
between good and bad decisions. The value of the decision varies signifi-
cantly with how well they are made. This gap arises when there is a risk
of a real loss if a decision is made poorly. For instance, a well-judged
loan offer to someone who will pay it back as agreed might net a bank a
few tens of dollars in profit. A poorly judged offer will result in the loss
of the loan principal—perhaps thousands of dollars. This mismatch
between upside and downside is characteristic of risk-based decisions.
Similarly, a poorly made decision in detecting fraud can result in large
sums being transferred to an imposter or large purchases being made
using stolen credit cards.

In a static world, one round of experimentation might be enough to
find the best approach. A set of experiments could be conducted and the
most effective approach selected. As long as nothing changes, this
approach will continue to be most effective. However, the effectiveness
of a decision-making approach can vary over time for many reasons, and
you have little or no control over this. The old “best” approach may
degrade suddenly or gradually, and when it does, you will need to have
alternatives. Even when experimentation finds a clear winner, a Decision
Management System needs to keep experimenting to see whether any of
the alternative approaches have begun to outperform the previous win-
ner. Alternatives approaches could be those rejected as inferior initially
or new ones developed specifically to see whether a new approach would
be superior in the changing circumstances. The effect of this continuous
and never-ending experimentation is to optimize results over time by
continually refining and improving decision-making approaches.

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