Today organizations strive to work according to the Agile mindset. They use Scrum or Kanban or DevOps or SAFe® or any other way of IT delivery that we refer to as “High-performance IT delivery”.
In high-performance IT delivery, people are organized in cross-functional teams that together take responsibility to deliver the right IT support for business processes, with the right quality level to deliver business value to their customers.
A team member working in a cross-functional team requires additional skills and capabilities compared to working in traditional “siloed” teams.
In this series of blogs, we will elaborate on a wide variety of aspects related to training of people in cross-functional teams. In this first one, we will focus on the changing needs for skills and capabilities.
As a first example there is much more emphasis on collaboration between people. They need to be able and willing to pick up different kinds of tasks, instead of focusing on what relates to a specific role/expertise. The sum is greater than the parts in a cross-functional team. Everybody contributes with their specific knowledge, experience and skills. At the same time, to be able to collaborate efficiently, each team member needs to know about the other team members expertise’s.
Collaboration is very important, this can for example be implemented using pairing, which means working on one task with two people (e.g. pair-programming, pair-testing, pair-debugging). Collaboration can even be implemented as mobbing, which means working on one task with a group such as the whole team (e.g. in refinements, mob programming or mob testing).
Broader knowledge base
People need a broader knowledge base. That doesn’t mean they must be experts on all areas but at least they need to be interested in all different knowledge-areas that are relevant for their team and they need to be interested in various professions. Every team member needs to know a bit about all the different IT disciplines needed to deliver value by the team and together with other teams.
In a cross-functional team, everybody takes responsibility for quality. This means that all team members have at least a basic need for expertise in quality and testing. And of course, each team needs a few team members with deep knowledge of quality assurance and testing that can guide and coach everybody in the team, as well as explaining to stakeholders what the quality situation looks like. So, in general team members need more knowledge and skills in quality engineering.
Changing training need
Because working in a cross-functional team demands additional skills and capabilities, it’s important to look at the training needs for the team members. Most of them could be handled with on-the-job training, but some need other forms of training. In a later blog, we’ll explore different options to fulfil the different training needs. We’ll also elaborate on a wide variety of aspects related to training of people in cross-functional teams.
If you have any ideas or requests, please let us know, this series is a work in progress, so your contribution is highly valued!
This blog has been co-authored by Rik Marselis, Principal Quality Consultant at Sogeti in the Netherlands
About Eva Holmquist
Eva Holmquist has more than twenty-eight years of professional IT experience, working as a programmer, project manager and at every level of the testing hierarchy from a tester through test manager. She has also worked with test process improvements and in test education as a teacher and with the development of courses including a Swedish ISTQB Foundation certification course. Author of the book ”Praktisk mjukvarutestning” (Software Testing in Practice) as well as science fiction and fantasy novels. Eva works as a Senior Test Specialist at Sogeti helping clients improve their testing practices using her broad experience in system development, process improvements, and education. She is a frequent speaker and has during the last year held presentations about agile testing, DevOps and quality assurance, cognitive quality assurance and bias in artificial intelligence.
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