At first glance, nothing at all! This comparison is perhaps a bit exaggerated, but the question of what a physics degree with a focus on gamma and neutrino astronomy has to do with IT consulting in quality management (QM) is more than justified. If you take a closer look at these two areas of fundamental research and software development life cycle (SDLC), then it becomes very clear that a lot of the processes overlap.
An astrophysicist's work
An experimental astrophysicist generally deals with data derived from the measurement methods for a particular experiment. The data from cosmic events can be measured by analyzing the physical phenomena. At first glance, this data seems useless, but through complex methods of statistical data analysis, it can shed light on the nature of many objects we know little about in our universe. This requires the research project and the corresponding implementation to be well planned. The insight from other scientists is almost always indispensable here and most research papers use it as a base. This results in a cycle, which, much like software development (SD), is run over and over again, improving quality (Q) with each iteration. This means that QM also plays a major role in this area.
A quality manager's work
QM in SD consists of Q planning, control, assurance (QA) and improvement activities. As such, the Q-manager has a finger in almost every pie when supporting a project, as they use QA measures in every phase of the SDLC from the very beginning. These include preventive measures to bring to light any sources of error as early as possible and thus minimize the risk of errors throughout the project. Appropriate control measures ensure that Q targets are still achieved should there be any deviations from what was planned. Furthermore, operational measures in the form of static and dynamic tests are run in order to make the software quality measurable in some form. Finally, a certain learning effect occurs during the course of a project, whereby certain measures can be applied in later project phases via the so-called lessons learned. More about that later.
About the similarities
The connection with scientific techniques here is not as far-fetched as might be first assumed. Firstly, a research topic is nothing more than a project, as is the case with SD. At the beginning, research projects, as with SD projects, have to be planned. Similar to a QA plan, the goals are first set, then the measures for achieving these goals are established. There also has to be a preparation stage, such as installing tools and environments, and setting up storage locations.
After the planning phase, it is time to take action. But, as they say, planning replaces coincidence by error. No project in the world runs perfectly according to plan without contingencies that could be foreseen by pre-established assumptions. As with Q control, this requires a certain degree of flexibility in terms of how the planned activities are carried out. The important thing here is not to lose sight of the goals set at the beginning.
Let us now turn to QA. Here we're focusing on analytical QA, since planning and control already cover the technical and organizational measures. So that leaves the analyzing and testing measures for ensuring QA in some provable manner. In science, for example, this takes the form of regular lectures and discussion groups where interim results can be discussed with colleagues. In principle, this is a quality-improving measure in every conceivable area, since collective intelligence has the ability to systematically lead to solutions for higher-level problems.
In a research paper's conclusion, the results are always evaluated in some way. This means that the results must be discussed and a conclusion drawn from them, as is the case with a final test report. It also ensures that all activities are completed, or stipulates that any outstanding items need to be deferred to a future project. Sometimes, it also explains that a certain approach is not goal-oriented. An important point that unites SD cycles with research cycles are the lessons learned: how can things that work well be improved even more? How will you avoid things that are not so good in the future? What unforeseen events occurred? Could they have been avoided? These are all questions whose answers promote quality.
Is lateral entry recommended?
The advantage of QM is that it can be applied in almost any area, as soon as it is even remotely related to a project. If you're interested in achieving high-quality work in a project and, most importantly, leaving a positive footprint through provable Q improvement, it is not difficult to approach SD from an obscure field such as astroparticle physics. Just because QM is not mentioned by name does not mean that quality assurance measures are not applied, even if this is only subconsciously. For this reason, lateral entry from an initially obscure field is a convenient entry point for anyone with an affinity for quality.
If you would like to learn more about how insurance software helps insurance companies position themselves for the future, feel free to contact our expert Karsten Schmitt, head of Business Development.
