# How to Use Decision Analytics

Decision analysis can help you make better decisions. This
business decision-making methodology enables companies to model the real-world
consequences of a decision, calculate the best possible outcome, and then take
appropriate action.

#### What Exactly Is Decision Analysis?

Decision analysis formalizes the decision-making process by
dissecting each component of a business decision and evaluating all of its
potential outcomes. Risk analysis, operations research, new product launches,
and optimization strategies all rely on decision analysis. It ensures that a
company considers all pertinent information before making a critical decision.
Businesses, for example, may use decision analysis when weighing a large
investment decision.

Several decision-making tools facilitate the decision-making
process by visualizing every aspect of the decision in question. Decision
trees, influence diagrams, and expected value equations assist businesses in
making strategic decisions by comparing all potential outcomes and their
associated risks, opportunities, trade-offs, and utility functions.
Decision-making tools can be simple or involve extremely complex algorithms.

#### How Decision Analysis Works

A quantitative and systematic approach to making optimal
decisions is known as decision analysis. The specifics of the decision analysis
process vary depending on the problem being solved, but every decision analysis
process includes the following steps.

**Determine the issue.** When faced with a difficult decision,
the first step is to identify the factors that make the decision difficult so
that you can seek appropriate decision support. For example, a company changing
its pricing strategy must consider all potential consequences and whether the
change will help them achieve a desired outcome.

**Perform data analysis.** Businesses can benefit from
descriptive and predictive analytics to make more informed decisions. These
business intelligence metrics provide historical context and forecast future
trends or opportunities. Analysts collect and model this data using a decision
analysis tool, which allows their company to plan its next steps.

**Make use of decision modeling.** Throughout the
decision-making process, influence diagrams and decision trees can assist you
in visualizing the problem and identifying potential solutions. Influence
diagrams can help you get a high-level view of a decision problem. They entail
labeling each decision variable with a box or circle. Each decision node is
linked by an arrow to a possible outcome, uncertainty, or alternative. Decision
trees are trees that represent a decision problem. Each "branch"
represents a possible decision outcome. Because of their simplicity, these
models are the most common. They enable decision analysts to model and evaluate
every variable in a decision problem in order to find the best possible
solution.

**Determine the expected value.** Calculating the expected value
(EV) of each business decision aids in determining the average outcome and
avoiding uncertain outcomes. To determine the expected value of a decision, you
must assess the likelihood of each outcome and assign a numerical or monetary
value to it. If you're familiar with Bayes' theorem, you could use it to
calculate the probability. Alternatively, once you've gathered and modeled all
relevant data, plug it into the following equation to calculate the expected
value: EV = (Probability A multiplied by Expected Profit A) + (Probability B x
Expected Profit A).

#### Decision Analysis Example

Consider the following real-world decision analysis example:
A restaurant is thinking about expanding to a second location in Chicago or New
York. Opening in either city will have different costs and success rates. The
project management team hires a risk management firm to collect data and input
it into a decision tree for risk assessment purposes. The decision tree depicts
the monetary values that will be used to calculate the expected value.

In terms of Chicago, the data indicates a 20% chance of
success and an 80% chance of failure. With regard to New York, there is a 40%
chance of success and a 60% chance of failure. The firm estimates that opening
in Chicago will cost $1 million and opening in New York will cost $3 million.
Furthermore, they anticipate a $15 million profit in Chicago and a possible $2
million loss. In New York, they anticipate a $30 million profit and a possible
$9 million loss.

The firm then runs these figures through the expected value
equation:

**EV (Chicago)** = (0.2 x $15,000,000) + (0.8 x -$2,000,000) =
$1,400,000

**EV (New York)** = (0.4 x $30,000,000) + (0.6 x -$9,000,000) =
$6,600,000

The firm must subtract the expected value from the upfront
costs to calculate the net gain and net loss for each location:

**Chicago:** $1,400,000 - $1,000,000 = $400,000

**New York:** $6,600,000 - $3,000,000 = $3,600,000

These calculations show that New York will produce the best
results, implying that the restaurant's stakeholders should open their second
location in New York rather than Chicago.