Types of Metrics

By Serhiy Kharytonov  |  Posted 2010-02-01 Print this article Print

Types of metrics

There are three basic types of metrics: quantitative data (which collect the "raw" project data of the meaningful metrics), qualitative indicator (which provides the derived and calculated dependencies and correlations) and prognosed trends (which allows us to build and see the required extrapolations that permit us to make the correct decisions).

Moreover, metrics can be derived from observable information or "raw" data. For instance, the amount of source lines of code (SLOC) in a particular program or the number of documentation pages, staff hours, tests or requirements are examples of raw data. Conversely, an example of a "derived" metric can include the number of SLOC produced per day per worker, the defects per one thousand SLOC or a cost performance index. Additionally, often there are also six criteria that are used to illustrate the definition of metrics. These items are as follows:

1. Time: How the project is going against schedule?

2. Cost: How the project is going against budget?

3. Resources: How much time is being spent on this project?

4. Scope: Is the project's scope kept in line with expectations?

5. Quality: Are quality problems being reviewed and fixed?

6. Actions: Are there any outstanding action items on the project?

Metrics can explain what exactly is going to be measured, how the item will be assessed, and the units of measurement that will be used (such as percentages, number, ratio or index).

Some individuals also believe that a metric should include how that particular metric should be used and by which person in the organization, as this act encourages better decision making and better end products.

Of course, metrics must have another figure with which to compare figures; this number can be an industry benchmark, regulatory guidelines or other similar figure. It is important to remember, though, that sometimes metrics are only applicable to a particular company and thus, there could be no industry benchmarks altogether in this situation.

Serhiy Kharytonov is the Executive VP of Consulting Services at SoftServe. Serhiy has been with SoftServe since 2000, previously in the positions of VP of Technology and VP of R&D Services. Serhiy holds a MasterÔÇÖs degree in Computer Science from the National Polytechnic University, Lviv, Ukraine, and is currently finishing his PhD. He can be reached at skhar@softserveinc.com.

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