The climate and data agendas are deeply linked.
Decarbonising our society will require systems that are automated, that balance resources (across energy, water, agriculture, transport and the built world) to maximise efficiency and reduce waste. Balancing our infrastructure means it will have to ‘self-heal’ — for example in a decentralised energy grid, if a local renewable source goes offline that’s powering a hospital (or, given vehicles will be electric, drive away), the lights need to stay on.
To enable this, we need to start sharing asset-level data (e.g. from sensors), ‘digital twins’, environmental and geospatial data (including earth observation data)…
Some reflections on climate action based on dozens of meetings I’ve been in recently.
‘Decision-makers’ (who are, believe it or not, just regular humans) ignore systemic and complex risks. I suspect this is a mix of denial anchored in climate grief — on top of our basic desires to have things ‘not change’ so we can try and live a normal life.
I wonder if our collective experience of Covid will enable us to feel that change is more possible, or whether we’ll end up more resistant to change as we try and hold on to a sense of control…
We take for granted that we can search a billion websites from around the world in under a second. The fact that we can do this is the result of a very specific architectural approach, one that is based on standards that have been adopted globally. The outcome is that connections can be made by any system that follows the rules, enabling billions of connections to be made on a continuous basis, unlocking vastly diverse applications.
This is a free-market, open, democratic approach that enables both commercial and non-commercial innovation.
Things like search engines exist as a direct consequence of…
Why don’t we pay for value in the way it is generated rather than using a legacy system that isn’t fit for purpose?
There must be a raft of research on this [citations welcome], but I’m constantly bewildered by the insistence of certain organisations that continue to ‘require’ hourly timesheets.
This is something I’ve felt for a long time but have been prompted to write after yet another ask from an organisation who is introducing hourly timesheet reporting as a process — in an innovation programme.
Hour-based timesheets are a legacy of the industrial revolution when we were trying…
Data must be usable by machines, not just humans. Policies must mandate that data be machine-readable in order that it may be collected and used in an efficient manner.
As important is the ability to discover that the data exists, what it is, where it is from, and how it may be used. This ‘metadata’ is a priority to make available so that data may be found and information about it accessed. Policies must mandate the production of meta-data that will aid discovery.
This first priority is independent of the specifics of any taxonomy, ontology or other structural design. …
There is a lot of confusion about what the word ‘open’ means in relation to data, software, research, modelling (e.g. AI), analysis, markets and standards.
On this slide we show a number of different initiatives that are open, we detail some of the roles that they operate around, and how open applies to their approach.
For example, Cambridge Zero, as a research institution, aggregates data, creates and uses software and models to carry out research and analysis. Commercial vendor, Planet, produces data from its satellites on earth observation and makes both data and analysis available to the market on an…
To bring more clarity to our journey towards a Shared Data infrastructure, we need to bring together a range of skills and expertise.
We can learn from previous decades of web development that ‘move fast and break things’ often leads us to … broken things.
Policy, regulation and governance is [often] slow to play catch up, but rather than beat up the regulators, we can look at how to best shorten the path between business and societal needs.
To help understand how the data value chain works we can begin with a focus on how data is used — the decision-flow of how people and systems use it.
This is independent of technologies, organisational boundaries and/or governance structures.
How can we enable teams to move quickly to deliver solutions and address good data governance in the process? Start by taking in the whole picture.
I’ve been receiving a lot of calls this year from people asking how they can use data to ‘help’ in various situations (from climate to COVID).
To help frame a potential response that isn’t just “we can build an app for that” (that’s very rarely the answer) or “we should build a portal” (that’s almost never the first-order answer), I’ve been trying to encourage people to first look at user needs, examine prior work…