Why is it important for us to understand what different terms mean? Well, most importantly, so that we understand each other when we use these terms. But just as importantly, so that we understand how and when to use, not only the term, but also the principle and operation behind the term.
Let us say, for example, there are two seminars that you are thinking of attending – one is on “Measures and Metrics” and the other is on “Analytics”. How do you know which one is best suited to your needs? Let’s examine these terms.
What is a Measure?
Measure – To ascertain or appraise by comparing to a standard . A standard or unit of measurement; the extent, dimensions, capacity, etc., of anything, especially as determined by a standard; an act or process of measuring; a result of measurement . A related term is Measurement – The act or process of measuring. A figure, extent, or amount obtained by measuring . The act or process of measuring something. Also a result, such as a figure expressing the extent or value that is obtained by measuring .
An example measure might be five centimeters. The centimeter is the standard, and five identifies how many multiples or fractions of the standard are being appraised. With the centimeter, someone measuring something in the United States is going to get the same measure as someone in Europe.
Let’s relate this to IT, such as lines of code (LoC). Currently, there really isn’t a universal standard for lines of code. Someone measuring a program’s lines of code in one office will probably not get the same count as someone measuring the same program in a different office. Therefore, each organization needs to determine a standard for what is meant by a line of code and ensure that everyone in the organization understands and uses that standard. A measure may therefore be a universal standard or a local standard, but it needs to be a standard. For instance, 1200 LoC per day – this is the standard.
Without a trend to follow or an expected value to compare against, a measure gives little or no information. It especially does not provide enough information to make meaningful decisions.
What is a Metric?
Metric – A quantitative measure of the degree to which a system, component, or process possesses a given attribute. A calculated or composite indicator based upon two or more measures. A quantified measure of the degree to which a system, component, or process possesses a given attribute.
An example of a metric would be that there were only two user-discovered errors in the first 18 months of operation of a particular system. This provides more meaningful information that defines the quality of the delivered system.
A metric is a comparison of two or more measures–in our example this is errors over time–or defects per thousand source lines of code. This starts to provide better information, but not quite enough to make meaningful decisions.
What is an Indicator?
Indicator – A device or variable that can be set to a prescribed state based on the results of a process or the occurrence of a specified condition. For example, a flag or semaphore. A metric that provides insight into software development processes and software process improvement activities concerning goal attainment.
An indicator generally compares a metric with a baseline or expected result. This allows the decision makers to make a quick comparison that can provide a perspective as to the “health” of a particular aspect of the project. In the case of our example, being able to compare the system errors over time with a measure (standard) for system errors makes a big difference in determining what kind of action, if any, may be needed.
An Indicator, therefore, provides good information about the past. So the decisions that can be made using Indicators are “corrective” rather than “planning” decisions.
What is Analytics?
Analytics – leverage data in a particular functional process (or application) to enable context-specific insight that is actionable.
Analytics, as the term is used today, is the application of computer technology, operational research, and statistics to solve problems in business and industry. Mathematics and statistical analysis underpin the algorithms used in analytics to extract useful properties of data using computable functions. So analytics is the process of developing optimal or realistic decision recommendations based on insights derived through the application of statistical models and analysis against existing and/or simulated future data.
And therein lies the rub! Common applications of analytics include the study of business data using statistical analysis in order to discover and understand historical patterns with an eye to predicting and improving business performance in the future.
Business managers may choose to make decisions based on past experiences or rules of thumb, or there might be other qualitative aspects to decision making; but unless there are data involved in the process, it would not be considered analytics.
Analytics therefore provides more comprehensive and complete information for both “corrective” and “planning” decisions.
So, besides an understanding of terms to benefit communication, the terms Measures, Metrics, and Indicators refer largely to historical “facts”. Analytics, on the other hand, may use these “facts” and related data, to predict the future. So if you are invited to attend a seminar on Measures and Metrics, or a seminar on Analytics – if you are wanting to use the past to predict the future for improved planning and decision-making, which would you choose?
To understand how Measures, Metrics and Analytics can be applied to drive performance improvement through YOUR workforce, contact us.
- Measure, Metric, or Indicator: What’s the Difference? Bryce Ragland, Software Technology Support Center
- IEEE Standard Glossary of Software Engineering Terminology, IEEE Std 729 1983.
- Engineering an Effective Measurement Program Course Notes, 1994.