Purpose

An impact analysis looks at the outcome(s) of a project and its (their) potential effect on the environment around the project (business environment, physical environment, financial environment, political environment, and so on). At the very least, an impact analysis will highlight the differences in the environment between the status quo and the environment after the project has been implemented. At the extreme, the impact analysis may look at a host of gradients of project impact, as well as the impacts of alternative approaches.

Application

An impact analysis can be used to either heighten or allay concerns about a project’s outcome by focusing on postproject conditions. It can be applied either prior to or after the project has been implemented. If developed prior to the project, any assumptions used to determine postproject conditions should be clearly stipulated and any tools applied to ascertain the future state should be identified. If developed after the project has been implemented, the sources for any historical information regarding the preproject state should be acknowledged as well.

An impact analysis incorporates the following components:

•  Anticipated outcomes from project;

•  Preproject state;

•  Areas where the project is/was expected to impact the preproject state;

•  Assumptions;

•  Data sources;

•  Data presentation (preproject versus postproject);

•  Conclusions.

Content

The content for an impact analysis is most commonly quantitative in nature. Qualitative impact analyses are not unheard of, but they often have the air of a simple defense of a single point of view. In some instances, qualitative assessments will be converted to quantitative values through preordained metrics or surveys. In any case, the effort should be to keep the assessments as objective as possible.

As for the components of the analysis, the content comes from a variety of sources:

• Anticipated outcomes from project. This information may come from the project plan, the customer statement of work, requirements documentation, or feasibility studies done on the project. This is normally an objective statement (in a few paragraphs) that serves as an overview of project intent.

• Preproject state. This is an assessment (historical or present day) of the critical environment(s) as it(they) existed prior to project implementation. Any existing statistical data on performance or condition should be presented here.

• Areas where the project is/was expected to impact the preproject state. This identifies the environment(s) where an impact is expected to occur, in terms of changes in process, approach, outcome, or perception.

• Assumptions. Perhaps the single most critical element, the assumptions frequently determine the validity (or invalidity) of the impact analysis. Any assumptions regarding the preproject state, impact, postproject state, or the data should be defined in detail. Any efforts that were made to validate the assumptions should also be documented.

• Data sources. The data sources should be defined, as well as the time when the data were gathered and the methodology for gathering them. Any assumptions used to fill data gaps or to gather the data should be reflected under in the assumptions section (see previous entry).

• Data presentation (preproject versus postproject). Often in tabular format, data regarding the preproject and postproject states should be juxtaposed for easy review.

• Conclusions. Although the data should be presented in such a fashion that the conclusions are self-evident, any specific conclusions the reader is expected to draw should be affirmed at the end of the analysis. If some of the conclusions draw on assumptions, those assumptions should have been identified earlier in the document and should be reasserted here.

Approaches

Impact analysis can focus on a single issue or on multiple issues, although a multiple-issue impact analysis can become unwieldy. The challenge in dealing with multiple effects to a single environment (or more confusing, multiple environments) is that the data can become a sea of numbers with very little cogent insight to be drawn from them. If the data are largely qualitative, the methodology for its development will be crucial. The more insight that can be developed about where the numbers came from, the better.

Considerations

In conducting a review of an impact analysis, the first questions always relate to the data—too much? Too little? That is a major issue. Too much data will leave the reviewer believing that there is information hidden that she does not understand. Too little data will look like the impact analysis was incomplete. There should be sufficient data to support a single story or premise, without bludgeoning the reader with it.

Also, in establishing the assumptions, the author of the impact analysis should determine how the assumptions might be misconstrued or misunderstood. What if the reader does not agree with the assumptions? If those questions can be defined and clarified, their answers should be provided as a component of the impact analysis.