The statistical production process heavily relies on (statistical) computing throughout all process steps. Historically, ‘official’ software tools were developed and maintained by development units within IT departments, while much processing was automated ‘under the radar’ by users using for example the programmability of office tools and batch scripts. This user-driven automation took off further with the introduction of capable workstation statistic software packages such as SAS and SPSS and is now changing even more rapidly with the usage of powerful open-source such as R or Python.

Nowadays, these open-source tools are not limited to basic statistical tasks. They live in a rich ecosystem with professional software engineering features including libraries, (unit) testing and documentation standards, CI facilities, and so on. Current (open source) statistical tools have capabilities that go far beyond simple, stand-alone scripts and currently offer the ability to build services, web applications, or standalone applications that integrate with a modern IT infrastructure and thereby also can play an important role to make production systems more cost efficient.
As a consequence, decentralized script writing that has typically been permitted rather than sanctioned and supported in statistical organizations is rapidly transforming into professional software engineering. However, we still see that Statistical Organizations have to come to terms with this new reality. Decentralized software engineering means that allowing script writing is not enough: support is needed in the areas of HR management, IT infrastructure, and planning and organization of statistical production processes.

In this session, we cover the topic of computing in the statistical office from four different perspectives, each presented in a 10 minute presentation. The topic is introduced (5min) by the discussant and after the presentation a panel discussion is chaired by the discussant with input from the discussant and the audience.