Measuring the Productivity of Business Analysts (Part 1: Why )
By Adriana Beal
If you are a business analyst, you are probably feeling very tempted to click away from this page after reading the title of this article. After all, unless you belong to a small subset of very lucky BAs, at some point in your career you must have had a bad experience with performance measurement in general, and productivity metrics in particular.
Why “productivity” has such a bad reputation among knowledge workers
We all are aware of the distortions caused by attempts to bring the traditional view of productivity, borrowed from the context of the industrial era, to a world now defined by information, services, and intellectual capital. Productivity measurements are commonly based on output or throughput (Drucker, 1999). When confronted with this type of performance measurement, BAs rightfully ask, “why would anyone care how many use cases or requirements statements I write per week? Isn’t more important to focus on delivering value to our stakeholders?”
No sane person would argue that counting the number of use cases, requirements documents, or other artifacts a BA produces per given period would provide any insight into their performance. Not only this type of measure fails to say anything about the quality of the artifacts produced, or, more importantly, about the value delivered to the business, it is as irrelevant and misleading as it would be to measure the performance of a programmer by the number of lines of code produced per week. Consider, for example, a BA who identifies areas of a solution that have become unnecessarily complex and simplifies the design, reducing costs with coding and testing and increasing the value delivered to end-users by improving the user experience. A metric such as “number of requirements produced by week” would show a terribly wrong picture of the BA’s performance, and depending on how the measure was used, completely inhibit future creativity and progress.
We shouldn’t forget, though, that productivity metrics, even when applied to manual labor, typically include an element of quality (for example, output per hour, based on the quality of goods and services produced per hour of labor input–Huang et al., 2003). The inadequacy observed in most productivity measures is not an intrinsic property of this type of metric, but rather the consequence of common mistakes made by managers trying to measure the performance of knowledge workers, such as forgetting to look at the big picture and focusing on what is easy to count (e.g., number of hours required to complete a deliverable), while ignoring the relevance and quality aspects of the work.
Gil Gordon (1997) uses the broader term “effectiveness” to describe the performance of knowledge workers (KW), while acknowledging the similarity to what could be described as “productivity”. To him, “KW effectiveness” is a basket that includes:
- quantity (how much gets done);
- quality (how well it gets done);
- timeline (when it gets done); and
- multiple priorities (how many things can be done at once).
Gordon’s model rightfully recognizes that even though quality and quantity represent important aspects of how effective (or productive) a knowledge worker is, they aren’t the only dimensions that matter:
Here’s an example: consider an employee who turns out a lot of work with good quality, but is consistently late and has trouble juggling multiple priorities. Would you say this person is a good worker just because of the high-volume, high-quality output? Of course not – you’d appreciate those strengths, but when you look at the four factors together you see the total picture has some serious deficiencies.
Undoubtedly, delivering value to the business should always be the top priority of any business analyst. If the wrong productivity measures are adopted, an effective BA who chooses to tackle the most difficult problems, or spends time helping other team members and improving overall project results, could be perceived as less productive than another BA who focuses exclusively on producing as many documents as possible, without any consideration of the quality of the deliverables, or more important outcomes of the analytical work.
Competent management, however, is only interested in producing more products or services when those reach the established standards of excellence, and reflect the business priorities. While no one should expect the “number of units produced per day” from the factory floor to make sense for measuring the business analysis work, BAs should recognize that how much gets done is an important part of performance (always considered concurrently with quality, timeliness, and ability to handle multiple priorities, as Gordon points out).
The benefits of measuring BA productivity
One of the best reasons for measuring productivity of business analysts and other knowledge workers is to understand and reduce the obstacles impeding high performance. Low productivity is typically a symptom of poor process quality; by collecting relevant productivity data, managers can identify process-related barriers that are preventing their BAs from being more efficient and effective.
Armed with a better understanding of the constraints, decision-makers can allocate time and resources to what really matters to increase the productivity of their BAs, and consequently elevate the value these professionals are capable of delivering to the organization. For example, by comparing the productivity between two groups of BAs, a manager may learn that the lower productivity of a group was the result of the higher amount of time the group spends with activities outside the critical path of the analysis work (e.g., trying to schedule meetings with stakeholders).
By identifying and removing the obstacles (for instance, assigning support activities to project management assistants) organizations can achieve significant performance gains, bringing the actual productivity levels of the BA group closer to its potential productivity levels.
We are going to discuss in more detail the benefits of performance measures, and how productivity measures can be used for improving performance, in the third part of this article. First, in part 2, we will take a look at how the productivity of BAs can be measured in a meaningful way.
References
Drucker, P. (1999), “Knowledge-worker productivity: the biggest challenge”, California Management Review, Vol. 41 No.2, pp.79-85.
Gordon, G.E. (1997), “The last word on productivity and telecommuting”, available at: www.gilgordon.com/downloads/productivity.txt.
Huang, S., Dismukes, J., Shi, J., Su, Q., Razzak, M., Bodhale, R., Robinson, D.E. (2003), “Manufacturing productivity improvement using effectiveness metrics and simulation analysis”, International Journal of Production Research, Vol. 41 No.3, pp.513.
How to quote this article
Beal, Adriana (March 4, 2011). Measuring the Productivity of Business Analysts (Part 1: Why). Retrieved from BealProjects.com: http://bealprojects.com/measuring/2011/03/measuring-the-productivity-1/