Project metrics are a suite of parameters that we choose during the planning phase and then monitor and analyze during execution to help us make course corrections. They inform us of our performance against the plan and trigger us to take actions when unfavorable variances are detected. Project success is dependent on the relevancy, accuracy, and timeliness of those metrics.

In this blog, I’m going to provide a different kind of perspective on project management metrics than you may have seen before, and I’ll offer some new ideas for metrics that I believe will improve the probability of project success.

Projects are executed by people who perform a logic-linked network of knowledge transactions. Throughout the transaction network, interim sub-products are created at network nodes, and ultimately at the end of the network they are combined into final deliverable products that are essentially the artifacts of the aggregated project knowledge transactions. Typically, in defining the key metrics for a project, we select objective, or “explicit” parameters and measure them at the output of particular logic nodes in the workflow. Examples of these include: Requirements Completeness vs. Plan; Design Models Completed vs. Plan; Lines of Code Completed vs. Plan; Tests Objectives Met vs. Plan; Technical Performance vs. Requirement etc.

Because of the time required to collect, process, and analyze the data, every explicit metric looks back in time and gives us information, which lags behind current reality. In the case of Earned Value Management metrics for example, by the time the Project Manager sees a variance of concern, the information may be more than a month old. On a fast-moving project, operating in a dynamic environment, this lag can allow small problems to become big ones very quickly. This doesn’t mean that explicit metrics are without value. A good set of explicit metrics, collected and acted upon, in a timely manner is a must for every project.

But what if, in addition to these important explicit metrics, there were other metrics that could really look forward, and be predictive of unfavorable performance or at least advise us of an emerging execution performance risk? I believe that there are such metrics. Since projects are executed by people, it makes sense to me that we ought to include metrics that focus on people and the project environment within they are required to operate.  I think of these as “Implicit Metrics.” Implicit metrics are all about nature and the health of the project team operating environment and the ways by which it affects the interactions and knowledge transactions between the people. They are implicit because although everyone seems to understand that the quality of the work environment is important to project success, we rarely see it discussed in project plans or incorporated into on-going project health monitoring efforts.

My idea is to pay more direct and sustained attention to these implicit metrics and integrate them with the explicit metrics into a more holistic evaluation of project performance.  While Project Managers are selecting the suite of metrics that they will use to monitor and control the execution of their project, they should look beyond the traditional explicit metrics and add some implicit work environment and team interaction metrics which I believe have a profound effect on overall project performance. It’s been my experience, in the investigation of many troubled projects, that the attitudes, confidence, and morale of the project team are always significant, but often hidden as contributing causes of problems.  Project environment and interaction health issues are predictive of future problems and ultimately the potential failure of the project.   Implicit metrics offer the opportunity for the only real forward-looking parameters project managers may have to forecast future performance. Project Managers using observation, interviews and perhaps surveys, can gather important information and insight into the health of their project environment and its impact on people by looking at subjective metrics that address topics such as:

• The degree to which team members understand, respect and support each-others objectives. We want them to care about each-others success. This is especially important in matrix organizations.
• The degree to which team members communicate openly with each other. Infrequent communication and communications of contrived formality often mask hidden agendas and conflicts that impair performance.  We want people to want to communicate effectively and efficiently with each-other.
• How effective they are in collaborating to solve problems and make decisions.
• The degree to which the work environment is viewed as “enjoyable.” The work may be complex and difficult, but the environment doesn’t have to be oppressive and fear-based.
• The degree to which people perceive fairness in making assignments, and offering recognition.

The “science” of project management provides us with many explicit methods and metrics that help us monitor and control execution. What I’m suggesting here is that success may be further enhanced by using the “art” of project management to build a complementary set of implicit metrics that focus on the work environment and its effect on the interactions of the people doing the work. I’m convinced that an artful project manager can monitor, shape and improve the work environment and the health of the interactions between team members to enhance the probability of success of the project.