For labs we will therefore update this drawing at every design stage and use for quick reference..

However, certain problems remain.The first is that someone might not be sure whether the data they’re looking for even exists, and the second involves the requirements under which the data was gathered.

It’s important to understand any inconsistencies there might be in the collection of data, because without understanding those types of quirks and failings, it’s hard to know how useful the data might be.Additionally, the further the data gets from its intended use, the less useful it is.This is why digitising planning makes quite a good use case because, in theory, it’s a linear process..

The next issue with data is governance, and the question of whether someone is allowed to share particular data.Here we see issues like cybersecurity and GDPR coming into play, presenting a list of complex and expensive regulations to navigate.

Finally, there’s the issue of transacting data.

While on the one hand someone might worry about opening themselves up to liability by providing data, they might also be wary of someone else using their data to achieve financial gain, and missing out on the opportunity themselves.The focus must be on broader value.

In terms of implementing policy decisions, we need to provide more evidence, more data, more measurement.It’s vital that we’re able to demonstrate clearly that these new ways of operating will deliver the outcomes we seek.

It’s not enough just to measure cost and time.We need to make a strong case for the other value outcomes as well, including productive growth and social value..