Lessons from the Pandemic: Recommendations for regulators

Traditional drug development is excruciatingly slow. From 2010–2020 the average time from the first submission to regulator sign-off was over 9 years, and that’s after potentially decades of R&D to deliver the drug candidate. Yet in response to COVID-19 the pharmaceutical industry, their regulators and governments around the world overcame decades of status quo processes to deliver highly effective vaccines within a single year.

In a society crying out for new infrastructure solutions responding to energy security, climate change and shifting demographics we must learn from this harrowing experience to accelerate the design and delivery of critical infrastructure projects.

Across this series we’ve outlined key lessons learnt from the development of the COVID-19 vaccines:

How multiple drug candidates were developed concurrently enabling teams to learn from each other and optimize their processes far faster than ever before in ‘Multiple paths, multiple opportunities’.

How in the midst of a global pandemic, the largest clinical trial in history took place facilitated by remote working and high levels of trust between teams spread across the globe in ‘Remote first trials’.

And how regulators radically sped up how they accepted and validated clinical trial results by taking a data-first approach, learning to collaborate with clinical teams in real-time as new data was generated and insights discovered in ‘Iterative Data, Iterative Learning’.

Recommendations for regulators

In this final piece, we’ll take a step back and consider how regulators could get started in learning from the COVID-19 story and adapting the lessons learnt to the built environment.

These policies and capabilities are not mutually exclusive but would make great initial steps if you're just starting your journey into data-driven decision-making. These are not new ideas! From the UK Environment Agency to the US Federal Digital Service, regulators have an incredible history of innovation which you can learn from.

Read on to discover three top policies you’ll need to get started in Data-Driven Decision-Making in the built environment and three core capabilities you’ll need to deliver them.

Policies

A. Open by default

The key to successful data-driven decision-making is trust. Trust in the data, trust in the processing of that data and trust in the interpretation of that data.

Whilst there are no shortcuts to creating trust between stakeholders, especially in a subject as diverse as infrastructure, building a culture of decision-making in the open is a great way to start.

From early outreach to stakeholders on forthcoming changes, to external blogs on core technology and policy decisions, a significant amount of goodwill can be gained by ‘saying what you’ll do, then doing what you say’.

As your data-driven decision-making matures, ‘openness’ will take on a new meaning. This could include publishing your data schema, the source code used to interpret the data or up-to-date statistics on trends over time.

Teams who assume the light of public interest is always shining on them will make better decisions and build a stronger communications culture in articulating the why, what and how of decisions which might affect thousands of people in the real world.

B. Enabling data iteration

Rapid iteration is at the core of data-driven decision-making generally, and the COVID-19 response specifically.

Building the systems is the easy part, building the culture is far harder. Traditionally, admitting a finding or a data point was wrong was seen as a failure much like a parent's response to their child receiving a C grade whilst expecting an A* in a maths exam.

People and processes must become capable and comfortable with accepting and considering new data as it becomes available. At the most extreme you might like them to celebrate when old data is found to be inaccurate as a harbinger of a better decision than otherwise would have been made.

This is a simple concept, yet dastardly difficult to implement, driven perhaps by the memory most of us have as pink-faced 11-year-olds fearful of parental response to a poor grade.

C. Submitting multiple concepts

COVID-19 was effectively vanquished for the majority by competition between vaccine candidates to quickly identify the most effective, safest and economical candidate.

The construction industry can deliver similarly radical outcomes by facilitating and enabling the submission of multiple concepts simultaneously. Shifting the debate from binary to bi-model could radically transform stakeholder engagement as decisions move from a simple yes/no on a proposal to inclusive debates on the strengths and weaknesses across multiple concepts.

As new data is collected and assumptions proved or disproved competition will drive decision-making towards the best solution, learning from each other to iteratively improve with the active input of all stakeholders.

Capabilities

A. Accepting, validating and storing your data

With your decision-making becoming more data-centric you’ll need the data infrastructure to accept, validate and ultimately store the order of magnitude increase in data tumbling down virtual pipes, spigots and reservoirs.

But before your CFO has a mild meltdown at the size of the consultancy invoice, you can start small and simple. Software as a Service (SaaS) solutions now support a broad range of construction use cases, from concept designs to environmental assessment to public engagement. Such solutions are inherently scalable and bring with them best-practice talent from hundreds of past projects.

Above all; keep it simple. Avoid restricting yourself to the detail of an in-house process, but rather adapt to the best practice vendors in the market bring with them. Whilst custom software development projects can bring the satisfaction of seeing your end-to-end vision captured in a tangible form, they also bring significant financial, cyber-security and reputational risks compared to buying off-the-shelf solutions.

B. Visualising and benchmarking your data

Once you have a steady stream of data spilling down your pipes from early adopting partners, visualisation becomes the priority. Again, with off-the-shelf technology consider simplicity above all else. SaaS solutions which include both data management and visualisation tools are increasingly commonplace; creating secure, scalable and user-friendly working environments to deep dive into your data.

Whichever solution you choose, follow the best data governance practice of ensuring you have ongoing ownership of your data with direct access to export it on your terms, even better selecting products which support secure sharing by default enabling collaboration from day one.

Benchmarking is an inherently political act, which datasets to include or exclude, their relative weighting and choice of time series are all innately policy questions over technological. Share your policies and examples early and often with early adopter partners and other regulatory bodies with a vested interest, and work in the open wherever practical.

C. Publishing your data schema

You have your data in hand, a logical structure has been produced and the key metrics have been decided and benchmarked, so what’s next?

To build broad trust in data-driven decision making it's critical to publish the basis of those decisions: the data schema.

There is a risk here. For every stakeholder who gains from a decision, there will be those who lose and a published schema will provide ammunition to challenge. Yet without publishing the schema your decision-making process will remain a black box, open to challenge and misinterpretation. Transparency of decision-making requires transparency of the decision-making mechanism.

About Continuum Industries

Continuum Industries is a provider of an AI-powered infrastructure development platform, Optioneer, that enables power, utility and renewables companies to instantly visualise, analyse and comprehensively assess routing options for power lines, cables and pipelines.

By incorporating all environmental constraints into the development process from the very beginning and considering them together with social, engineering and cost criteria, Optioneer bridges the gap between existing routing procedures and the pace at which project development needs to happen to meet Net Zero targets.