Are some ways of achieving transparency better than others?

There are many ways to prove transparency once your evidence is in the public domain. But what about during the analytical process and afterwards? Many analytics solutions come in black-box form. You can’t ‘take the lid off’ and see inside in order to report on how exactly your ‘evidence’ has been calculated.

We recommend an open system, that allows you to see and record any changes made by data scientists and therefore to audit and report on them. This level of ‘in production’ transparency means that as data scientists join and leave your organisation there is a bread crumb trail others can follow – and this mitigates risk for project continuity and the efficacy of your evidence.

An open system like this, eradicates many of the challenges public sector organisations are now experiencing with open source analytics. While appearing less expensive in capital costs, these up-front savings are often eroded because of the lack of scalability, quality processes and continuity provided. As different data scientists deploy their own particular way of doing things cost, compliance and efficiency issues are introduced. The result is that with open source and some black-box proprietary solutions you will never be able to completely understand the analytic approaches being used. Therefore, an essential component of your evidence base will be missing – and that’s not great for delivering meaningful visibility.

It is possible to acquire the best of both worlds. Take a look at SAS ViyaTM to see how we can support the advanced levels of visibility government departments must deliver – whether you work with open source analytics or not.