I wonder if AI might actually help us actually break away from native mobile apps for a lot of things & ‘get back to the web’?

Many modern hi-fi amplifiers have ‘an App’. They are, uniformly (from what I can see), terrible.

In this post I show how I ‘fixed this’ in under an hour using ai to do something I would never, ever have got around to doing. Given how much I listen to music, I know it’s going to save me many hours of irritation. It even left me with enough time to write this post.

Let’s start with the challenge. In the UK we spend about £600M a year on hi-fi stuff: that seems like quite a lot. And the higher-end kit costs more than your PC. However, while they ‘try and be digital’, it’s like walking back into the 1990s as a user.

Here are some screenshots from the ‘flagship’ Marantz app starting up.

I should point out that not only is the user interface terrible (including trying to get you to sign up to their HEOS garbage) but it takes needless ages to start up and navigate. Those spinning disks spin for a long time, and then present with a page with a tiny round button for the volume and mostly meaningless other stuff that’s too small to consistently accurately tap. It’s really easy to accidentally turn the volume up a lot (which on a >3kW system can be alarming if you’re not expecting it).

Let’s say you want to turn the volume down (the #1 thing you do on an amp ‘instantly’), it can take up to 10 seconds to get there. This means you revert back to the remote control, or – heaven forbid – have to actually stand up and turn actual the volume control – there are plenty of other apps that tell us when we need to move . Further, to get into any granular controls such as changing sound processing, or changing the volume on individual surround channels is painfully slow (tens of seconds to get to, seconds for each change), cumbersome and, actually just unusable. I often want to do this for specific tracks when in a proper listening session.

Clearly in 2026 this really isn’t good enough. Fortunately this is now something (almost) anyone can fix without waiting for manufacturers to wake up and do something.

Googling around the docs there are some API standards and I’d done some previous command-line work to pull status and push things like volume to see if it all actually worked (it did).

But now, with the wonders of AI (in this case, Claude.ai) it is almost magically trivial to make your own interface.

In this post I show how easy (and things that can be frustrating).

Let’s start with a bit of magic: I asked Claude (thinking that this wouldn’t be enough)

> develop a web interface for the Marantz SR7015 using its API

It wasn’t just “enough”, it even styled the page with custom fonts and an image of a surround setup. To get it actually working (locally, on my Mac) I just had to give it the error I got

> it says "Cannot reach 192.168.1.46"

At which point the AI created a local proxy server, told me how to install it and some supporting packages (with copy/paste instructions). If I rewind to when I used to be able to actually code, I think this would have all easily taken me a day. Instead it took this aging hacker about 30 mins of faffing around. This was it – a fully working, way better visual interface and way better user experience (it loads instantly with no noticeable latency on the controls), and individual controls, all on one screen. And, against a black background as usually listening when it’s dark (or watching a movie).

I then spent a few hours playing around with layouts to see what was possible, then asked the AI to make a version I would run on my Pi-Fi (the Raspberry Pi I have that displays album art when Spotify is playing). Which, of course, it did at a similar pace.

This means I can access a web page on my home’s wifi network on my phone, laptop or desktop – as can anyone I give the web address to who is with me. I can use it as a remote for the amplifier, not just for volume, but including controlling all the parameters I actually want to access instantly, with nice big buttons and only the things I need. It still wasn’t able to do some things (like more nuanced reading/writing of custom fields).

I then added new functionality – to pair and group left and right speakers into ‘front’, ‘surround’ so I can control the top-front-side-back distribution more easily (unless you’re a sound engineer, you’ll be surprise how much more this varies than you’d think based on the mix and type of the music).

And, finally, for now, a joystick control to move the balance around between all 11 speakers. Which was done with a single prompt:

> create a joystick control to balance the audio between all the speakers

Next up I’m thinking of keying together listening room profiles per track, keyed into what’s playing on Spotify so the entire balance of the system can auto-configure based on what’s playing. This would then include custom listening profiles and sound stages I’d set up for specific favourite tracks. It’s almost as if one could take all this too far*…?

* Looking out at the hi-fi geek landscape, there is no end of completely stupid nonsense things people do (from £1,000 audiophile USB cables to ‘elevated speaker cable risers‘), none of which make a single bean of difference to anything but your wallet. Once you’ve got a basic system in place by far the biggest differences you’ll be able to actually hear are (a) treating the room with sound insulation, and/or (b) treating the distribution, balance and EQ of the signal itself in your space, which is what I’m doing here.

We’ve never had more data. Some think it’s the new oil, the new gold, the new soil, something of a revolution. Is it? Or are we just still humans stumbling around in the dark?

Earth observation satellites scan our planet, sensors (there are more sensors on Earth than there are people) track the movements of people and things, energy flows, air quality, water usage, and behaviours. Our global financial systems process billions of transactions a day. Most organisations, public and private, are drowning in dashboards, APIs, spreadsheets, data lakes, clouds, compute, applications, and now ‘ai’.

“Every age thinks it’s the modern age…but this one really is”
Dreams Rewired, Manu Luksch

And yet, when we need to make decisions that matter — how to finance a green retrofit, where to send emergency flood support, or how to measure progress toward net zero, the data we need is often hard to access or nowhere to be found. Or it exists, but it’s in the wrong format, in the wrong place, scattered across organisations and systems, under the wrong licence, or wrapped around the wrong market incentives, or just poorly governed and hard to trust.

Why is this important to our future?

We’re in an era where our financial economy is demanding ‘quality’ data from the real economy. Our financial systems have developed over centuries to measure, track, verify, use and report on the rate of change of ‘one dimension’: money. As we continue the shift to make our environment measurable, we must get the ‘exchange rate’ right between financial investment and environmental outcomes (climate, nature, air quality, water, biodiversity, and so on).

Right now the ‘exchange rates’ are all over the place and, importantly, as we step forward we need to measure the rate of change of many parameters, not just one: we need a step-change in how we think about ‘data’ and data sharing.

It’s hard to understate quite how far away the real economy is from generating financial-grade data

We’re still mostly in the 1970s. Environmental disclosures still rely on highly manual processes, despite widespread digitisation and hundreds of application vendors in the market, it’s still mostly clipboards and spreadsheets or, for 99% of the world, nothing at all. This is not a problem of data scarcity, it’s a problem of incentives, structure, and alignment.

We’ve built systems that are optimised for accumulation, not coordination. We have engineered data pipelines that extract value, but not the wiring to deliver reciprocity (shared benefits).

In the language of infrastructure: we’ve laid the cables, but we’ve neglected the grid

We are told to “make data-driven decisions” while simultaneously being denied the means to do so at scale. Meanwhile, we waste more time (and money, opportunity and emissions) trying to reconcile, reformat, clean, or even find the data than the actual decisions that we are trying to make.

The result is a kind of systemic gaslighting: we pretend that the data is ‘there’ when what we really mean is that it exists … but existing is not the same as usable, and very far away from trusted. Just because something is stored on a server doesn’t mean it’s infrastructure. Just because it’s digital doesn’t mean it’s useful. Just because it’s available doesn’t mean it’s accessible.

McKinsey (The data-driven enterprise of 2025) suggests that data professionals still spend up to 80% of their time preparing data rather than using it. This is the same number I have from over 25 years ago.

So where do we go from here?

Data is everywhere but until it flows to where it’s needed (when it’s needed, and with the right context) we’re not building a digital future we’re just building more digital noise

We need to stop pretending that technology (including ai) alone will solve this. We must stop framing data as a ‘commodity’ to be owned and sweated, and start treating it as infrastructure to be governed.

We must design for connection not monopoly control, for shared purpose not private hoarding. We must invest in the ‘boring’ bits: governance, legal standards, licensing, permission/consent, metadata, interoperability.

This is the stuff that doesn’t make the headlines, but makes everything else work.

The National Data Library can deliver its vision,
but it must pivot to build trust in the age of today’s web

This post builds on observations and conversations across government and industry (and internationally) since my previous post on “How can we find the Goldilocks Zone of our National Data Infrastructure?”.

When we’re building into a future where technology has such clear benefits it is often hard to be heard above the noise of the possible. And yet I believe we must also ‘grasp the nettle’ regarding equally possible negative or unintended consequences. While we must not “stifle innovation and growth”, we also must ensure we don’t scuttle our ship in the process.

The first misconception I want to address is that data is not a technology.

Data is used to represent information, analysis, insights, opinions, facts, responsibilities and decisions. It carries many different kinds of value, as well as material rights and material risks. Data is used to find things out and to make decisions. To look into its past, data is the plural of datum, from Latin, meaning something given.

I labour this point to make clear that it is not software (e.g. AI is software that uses and produces data, as are all algorithms codified in software) and it is also not hardware. A lot of debate about data (and AI in particular) seem to conflate these facts.

Treating data as if it were software is comparing apples and carts.

Context

Having been a tech entrepreneur for 30 years, I’ve sat in meetings with Silicon Valley investors where the “move fast and break things, then ask for forgiveness” mantra has been very present. Innovators love to build; building is fun and very rewarding — both intellectually and, sometimes, financially.

It’s common to worry about the consequences later, even when ‘later’ can be measured in decades. Those raising alarm bells are often badged as ‘activists’. This is a label I’ve never felt helpful: we can draw parallels with the past when ‘health risks of smoking’, through to catastrophic climate change, risked people being boxed as ‘alarmist’ instead of ‘anxious-with-cause’.

I’ve always advocated for fair-weather innovation, while ensuring that we build in measures for foul weather. In the age of the web, commercial (and political) ambition can often be seduced by the fair-weather arguments, while policy is rarely able to move at the speed required to keep pace and safeguard us from foul-weather conditions.

A lot has changed since my previous posts on data infrastructure. We must now, right now, be proactive and preemptive to the fact that our threat model has changed (due to shifting geopolitical and technocratic-utopian positions). Foul weather is closer than we may have wished.

Trust isn’t just a buzzword; it underpins our society and, in a digital age, this means addressing our data infrastructure, ensuring our safety, de-risking innovation and enabling growth in turbulent times. Trust helps us build all of these, for the long term.

Going from vision to implementation is hard

There is a natural tendency towards overreach in most systems — this is a very human attribute. When trying to innovate, our bureaucratic systems can often feel frustrating (good entrepreneurs — both inside and outside of government — know how to navigate around, or sometimes ‘through’, such systems).

Today, in 2025, with data and the web and AI, the pace and scale of impact outpaces and out-scales any other time in history. We can create a critical mass, or a critical mess.

To that end, I believe we must take steps to better understand and balance what is in front of us. When it comes to enabling data sharing, whether in finance, health, transport, energy, property, water, or across all research, we need to first take stock of something that is very hard to build and yet can be lost in the blink of an eye: trust.

The National Data Library (NDL) represents significant intent from the UK Government to do data sharing differently, and we (citizens, consumers, businesses, sectors, markets, and the state) can all benefit from this. However, implementation really matters.

Icebreaker One has already made a strong case (selected for publication by the Wellcome Trust) for the National Data Library to:

1. Focus on defined users and use cases rather than datasets, just like any effective data infrastructure.
2. Begin life as a simple, decentralised version, that would curate and improve the discovery of, and timely access to, public sector data for research.
3. Experiment with more complex architectures for harmonising access to data drawn from across multiple government departments.

Cutting across all of this, the NDL must be built, from its core, in ways that engender trust. Whether consumer distrust, competitive interest or geopolitical threats, we must have in place the people, processes and protections to cement trust between businesses (b2b), with consumers (b2c), and with our citizens.

The NDL can still maintain its grand vision to improve outcomes for all of us, connecting data, rapidly unlocking insights, and leading the way.

More than its technical architecture, the nature of its funding and staffing will shape what it can achieve. I’d argue we can do more together, and move faster, with common principles and parallel actions than a large centralised effort. Not only should the data never be centralised (other than for the limited use cases that require that), but neither should the absolute control.

We can go far together if we each do one thing well.

We can bind our work, together, with common principles and practices. There will be no ‘one hub’ — if you are a hub in a collection of hubs, you’re a node. Even a National Library is one node in a network of knowledge. There isn’t only one search engine in the world, nor will there be one AI.

We must also approach, with a clear perspective, that one-time broad-based consent is just one limited use case. It may apply to certain, relatively static, data but a different approach is required for highly variable data: data is rarely static. The conditions on which that ‘one-time’ consent, as we have also seen, will also change based on lived experience (e.g. changes in corporate and political governance).

In a world of rapid change, we must pivot rapidly but
never forget the foundations of trust beneath our feet

Building on the Smart Data model, there are known ways to apply decentralised, user-controlled, real-time, granular consent. There are dynamic solutions in place today which enable peer-to-peer data sharing via secure APIs. Such systems can include immediate revocation (by the user, or by a neutral governing entity/representative), and they are designed and built to enable cross-sector interoperability.

\Equally, such systems will not (and should not) be the approach for all use cases. What works will depend on the purpose (which will span personal, business, national, research as well as small, medium and large aggregate/collective datasets). What matters is how they are governed.

This is where implementation matters. If we over-centralise and over-reach in our ambition we run the risk of the litany of failed ‘IT projects’ which have cost the taxpayer billions.

I see many projects, even some with the word ‘governance’ in their titles, take a technology-led approach rather than a governance-led approach: data is not a technology—it enables insights about its subjects and those subjects have rights.

Recommendations

Without trust in the systems upon which we run our lives we will, at best, stifle innovation, productivity and growth. At worst, we risk undermining far more.

The good news is that we can run, at pace, fast but not foolhardy, to build the data infrastructure we need for today and scale for all our tomorrows, to deliver trust, ensure our safety and unlock the value of data for everyone.

Phase 1: Immediate reorientation on purpose and language (0–6 Months)

Step 1: Transition the “Library Card” model to a Trust-based, scalable model

• Pivot from the concept of ‘library’ to a ‘web of interoperability and trust’ (WIT)

For example, announce an evolution from the “National Data Library” to something like a “Web of Interoperability and Trust” (WIT) or “National Data Web” or “Trusted Data Web”, which would align with work already in place in the financial sector and give more scope to connecting existing initiatives, including Smart Data, without being specific about technology preferences.

• Define six exemplar use cases for pilot delivery in 2025

Pick six lighthouse use cases across sectors, assign to different entities for delivery, and shift governance to the model that best enables them (e.g. from “pre-approved datasets” to a federated trust framework where applicable).

Adopt an API-first architecture to allow controlled, on-demand data sharing. Use them to produce quantitative evidence as to the pros and cons of the approach.

Step 2: Introduce an adaptive Smart Data consent framework (6–12 months)

• Develop and mandate common principles

Common principles should include application for ‘Data Consent/Permission’ for consumers, SMEs, researchers, businesses and government departments to view, grant, and revoke access to datasets. These should be evidenced through use cases with specific cost benefit and threat analysis.

• Require and mandate data best practices

Require that all third-party users (e.g. AI developers, financial institutions) justify the minimum data required per use case. Mandate that every material access request is logged, auditable, and policy-compliant (e.g. GDPR, Data Bill, and related policies). Where applicable, develop formal Schemes that codify multilateral contracts and enforce them. Note that Schemes can also be industry-led and voluntary, with appropriate governance: political signalling can be an efficient way to accelerate implementation.

Phase 2: Build systemic trust (6–18 Months)

Step 1: Shift to an API-enabled Data Access Model (including trust frameworks) based on user needs

In this instance ‘API-driven’ is an architectural choice aligned with the web, not a specific technology, that enables both interoperability and controls to be implemented. Other use cases won’t need ‘APIs’ (e.g. a CSV file of Open Data), but where security and access control are required, any technical solution must be able to operate at web scale. Key is the development of the governance mechanisms that help implement trust frameworks.

• Support development of sandboxes

Support the rapid, cross-sector creation of sandbox environments with political and financial support where required.

• Support open communication, transparency, and build collaboration and reciprocity with a broad stakeholder group. Address business, public and government skills and knowledge gaps.

Embed open communications, transparency and reciprocity into all programmes. Demonstrate to businesses and consumers that technologies such as AI can deliver value without undermining trust. This can be achieved by facilitating co-design across the value chain (e.g. govt, industry and end users, whether b2b or b2c)

• Create systemic links with digital identity developments

Incorporate systems-level authentication (e.g. OAuth-based which is already ubiquitous) — data access should be controlled via digital identity-linked permissions for companies and research organisations. This is aligned with the Data Bill and FCA approaches. Digital identity is a huge topic which I do not want to dive into here, but I do assume that it will (also) be federated (there should and will be no ‘one place’ for it).

Step 2: Maximise privacy enhancement for people and businesses (e.g. SMEs)

• Create mandates and incentives for privacy enhancement

Broaden and define Schemes to enable ‘Access Rulesets’ and enforce data-minimisation principles for citizens, consumers and businesses (including b2b SME data sharing), so datasets can be queried without bulk transfers (e.g. an app can check fact without revealing the underlying data, e.g. “are they over 18 — yes” not “what’s their birthdate?”)

Phase 3: National Smart Data Infrastructure (18–36 Months)

(yes, this may feel ambitious: why shouldn’t it be this rapid?)

Step 1: Legislate Data Interoperability

• Mandate interoperable systems where consent/permission or access control is required

Draft legislation ensuring every public-sector dataset must be ‘API-accessible’ under standardised, interoperable formats. As noted, not all public-sector data needs an API. In fact Open Data should be made available in any way that makes it discoverable and usable, and even CSV files can be catalouged by other systems and made ‘API accessible’. However, where some form of access control is needed, secure APIs are the standard that works for the web (e.g. Open Banking has 12 million monthly active users across the UK).

Also note that interoperability at the API level does not mean that all internal systems must do the same thing in the same way: data can be repurposed and presented without rebuilding entire estates (this will create an incentive for doing so over time, but it doesn’t need to happen first). Interoperability and decentralisation can work together, with appropriate governance, to enhance competition.

• Mandate open access and empower regulators to enforce compliance

Mandate open access to government datasets where possible, while protecting sensitive data via consent-based permissions, and democratically set rules. Pick regulators or code bodies to oversee the development of the specific rules and compliance required, including modes of redress. Note that open access and Open Data are different things.

Step 2: Scale the Model Beyond Government Data

• Support the development of connected demonstrators and pilots

Launch pilots with commercial ecosystems to enable interoperability (e.g. real estate, sustainability finance, public health). This includes cross-border and global data-sharing ecosystems (e.g. finance, goods).

• Enable interoperability beyond the UK, within the desired framework

Develop cross-border data-sharing agreements (‘Schemes’). Ensure interoperability with international frameworks (e.g. EU Digital Identity Wallet, OECD AI Data Principles and related emerging programmes). Ensure every data use and dataset are AI-ready and privacy-compliant via automated monitoring and governance.

Summary

Strategic Benefits

• Accelerates mission-driven government: Enables implementation of the government’s five missions via trusted, scalable data access.
• Supports economic growth: Unlocks interoperability for data-driven innovation across UK sectors.
• Protects society through accountable systems: Ensures that data-enabled systems are governed with clear and enforceable safeguards. Enables a coherent approach to data sovereignty across our economy and society, to minimise the risks of misuse and systemic harms in a rapidly evolving landscape.
• Future-proofs data governance: Aligns with existing data sharing frameworks, and with evolving standards for AI training, licensing, and consent. Licensing will be a cornerstone in all future data work whether AI-linked or not.
• Embeds assurability at scale: Operationalises assurability across domains.

Five WIT principles (for discussion)

1. Interoperability by design: Common standards, APIs, and metadata frameworks to ensure frictionless cross-sector data use.
2. Trust infrastructure: Consent-based access, verified credentials, and relevant-time provenance tracking.
3. Federated governance: Sectoral autonomy with shared rules, coordinated through a Web of Interoperability and Trust.
4. User empowerment: Individuals and organisations have control their data-sharing preferences. Strong governance-led approached apply to aggregate data, with the option to revoke.
5. Composable architecture: Modular, user needs based and cross-sector enabling use cases, using sharable registries, vocabularies, and open interfaces.

NB: This post contains (as-always) personal opinions and thinking-in-progress (strong opinions, weakly held) as I navigate through the maze(s) of data governance. In amongst many experiences, I sat on the MiData energy sector board; co-chaired the creation of the Open Banking Standard; was founding CEO of the Open Data Institute; co-Chaired of the Smart Data Council. I run a non-profit (IB1.org) working on data governance at sector, national and international scale.

With thanks to Jack, Peter, Chris, Frank, Jeni, Simon, Julia, Kathryn, and many others who have helped shape my thinking on this.

(please note my disclosures at the end of this post)

This post contains (as-always) personal opinions and thinking-in-progress (strong opinions, weakly held) as I navigate through the maze(s) of data governance. Persistent in my questions are how we apply the Goldilocks principle of governance vs innovation: to remain flexible and avoid brittleness.

Given the conversations I’m in at the moment I believe it very important to have as much debate as openly as possible, as this impacts everyone.

Here’s a question:

How should the UK implement its National Data Infrastructure so that it provides consistent control, sharing, and security for data, much like other public infrastructures such as roads and utilities?

“Everything should be as simple as it can be, but not simpler”
[often attributed to Einstein]

Having initiated ‘data as infrastructure’ at a political level in the UK in 2013, I’ve been watching and learning on this for a long time. We’ve made remarkable progress in some areas, less so in others.

Let’s explore, starting with some lessons learned.

One immediate reflection is that as over 60 countries moved to copy the Open Banking Standard, those who took only ‘parts’ of the system tended to experience less ideal outcomes (to the point that they often add them back in later to help course-correct — most notable are those who thought this was ‘just a tech problem’ and only took the open API principles).

We can look to past UK data initiatives like MiData and Open Banking and potentially infer that voluntary-only initiatives and centralisation do not work, and that the decentralised nature of Open Banking was more successful. However, these are shallow reflections.

We can point to three factors beyond centralised or voluntary which are dependent on the use case:

1. Market incentives: there must be an economic argument that policy can then amplify or mandate. If there is no financial incentive, there will be no movement (regardless of central vs decentralised, mandate or not — mandates are also often ignored). Midata was mostly tech-led, Open Banking was use-case led.
2. Removal of friction: Removing transactional friction may seem like something everyone wants, but not if your current business model relies on it. There must be “something in it” for everyone, or at least a path to cost reduction or a new business model. Removing friction can help everyone go together: this is never a ‘technology problem’ (e.g. absence of a data ontology).
3. Mandates: if the friction is ‘too high’, regulatory intervention is necessary to mandate participation and move the market. However, if the market can demonstrate self-initiation, then ‘endorsement’ from government can be sufficient to drive impact. Value creation must be visible in both cases.

I believe that we need to embrace these factors in our systems design.

We need to:

1. work out where the lines are drawn between central, pre-competitive and competitive areas;
2. ask what the role of regulation is, and at what granularity
3. query how things can be implemented in a way that humans (and machines) can and want to adopt
4. ensure those governing, operating and participating can be helped in their assessments of compatibility and interoperability
5. work out how to prioritise and create clear (and stable) roadmaps that enable investment to be made

I believe that we can build on the success of sector-specific approaches:

The Open Banking model’s success lies precisely in its sector-specific, incremental approach. It focused on the unique needs of banking, where security, standardisation, and customer trust were paramount.

Copying the Open Banking model into other sectors (e.g., energy, transport, health) isn’t about duplication but about applying tested frameworks tailored to each industry.

Without considering the market incentives for participation we risk techno-utopian thinking

Instead of trying to create a complex, overarching architecture that risks being too broad, a sectoral approach allows for flexibility and adaptation to industry-specific challenges. This carries a risk of fragmentation, and equally a potential to mandate cross-sector interoperability without defining exactly what this means in detail for everyone. The risk of push back on over-reach is material and catastrophic (in terms of adoption, if it fails). Further, market-incentives will be market-specific.

Avoiding Over-Standardisation

National Data Architecture risks over-standardisation and could stifle innovation instead of enhancing it. Different sectors have wildly varying levels of data maturity, hugely variable data governance needs and challenges, and a one-size-fits-all approach is highly likely to impose unnecessary constraints on sectors that are not as ready for them.

For example, imposing the level of data governance on the industrial sector that is required in the financial sector is (today) not realistic. We are, however, on the frontline of that journey with ESG reporting today, and it highlights the complexity of that real-financial sector bridging. Incremental improvements, like extending Open Banking principles to energy or transport can allow industries to evolve organically while keeping governance controls and security as a priority.

Pragmatism of the Incremental Approach

The incremental, domain-by-domain approach may seem less ambitious, but it offers a practical pathway to innovation that can be adopted today. Trying to design a top-down, comprehensive national architecture from the outset risks paralysis by analysis and over-planning. Learning from Open Banking and rolling out smart data initiatives sector-by-sector allows for continuous improvement and responsiveness to real-world feedback, which may be more effective than a sweeping, coordinated architecture.

Interoperability Requires Industry Engagement

Interoperability relies on industry engagement. This is best fostered through practical, sector-specific initiatives like Open Banking. Imposing an overarching system from above risks of alienating key industry stakeholders, who may resist changes that threaten their business models (this happens in every sector already, today). Open Banking’s success came from the balance of regulatory oversight and industry cooperation, a formula that can be replicated in other sectors.

Market Failures Can Be Addressed Incrementally

There are (always) risks of market failures such as data hoarding (castle & moat is still the prevalent investment model for data businesses). These issues should be tackled incrementally through sector-specific mandates rather than a top-down architecture, but can be accelerated with top-down principles.

The competition fostered by Open Banking and similar initiatives already shows promise in tackling data monopolies. An incremental, practical approach allows policymakers to address market failures as they emerge, rather than trying to solve all potential problems with a one-time top-down design.

Trust is Earned, Not Imposed

Trust in data systems cannot be imposed from the top. While Open Banking was catalysed by regulation, it earned user trust through transparency, security, and gradual adoption, not through a top-down imposition. Similarly, rolling out frameworks sector by sector allows consumers, businesses and citizens to see the benefits and gradually gain confidence in the approach. Attempting to ‘mandate trust’ top-down could backfire, especially in sectors like health, where data privacy concerns are particularly sensitive.

At IB1, our Trust Frameworks are one tool (of many) that can help foster multilateral collaboration, building on Open Banking principles.

Decentralisation Encourages Innovation

The decentralised, minimised and pre-competitive nature of Open Banking has led to its adoption and innovation. A large, centralised approach would have risked locking in certain standards or technological pathways that could hinder future innovations. Allowing sectors to develop their own interoperable standards fosters a more competitive, flexible landscape, where innovations in one sector can inform developments in another, without being dictated by a single framework.

Data governance should aim to balance impact on rights and security while minimising its own footprint and reach

While the idea of a unified, overarching data framework may sound appealing in theory, in practice it would risk being too rigid, slow to adapt, and detached from the unique needs of individual sectors. A more pragmatic approach — building on the lessons of Open Banking and extending these principles to other industries — may offer the best balance of innovation, security, and user control.

Please feel free to comment, or message me (via LinkedIn or directly)

Disclosure: I sat on the MiData energy sector board (mostly tearing my hair out on these points); co-chaired the creation of the Open Banking Standard (through which I learned so many lessons it could fill a book or two); was founding CEO of the Open Data Institute; am co-Chair of the Smart Data Council, and run a non-profit (IB1.org) working on data governance at sector and national scale.

Thanks to Chris, Frank, Paul, Hadley and others for their feedback and inputs.

Useful links

Lessons learned from Gaia-X Data Spaces https://www.sitra.fi/en/articles/eight-lessons-from-building-data-spaces/

ODI on Data Institutions
https://theodi.org/insights/projects/rd-data-institutions/

Icebreaker One on how it implements data sharing
https://ib1.org/what-we-do

Open Banking (implementation entity)
https://openbanking.org.uk

Additional narrative on cities
https://agentgav.medium.com/the-porous-city-92ae986cd43c

Diome
[noun]
A data biome that represents a temporal cluster of data from which specific insights can be learned, analysed and/or derived.

Discussion

This is public on LinkedIn (open gdoc here if you’d like to contribute)

• when training AI, if you have a load of similar diomes you’ll end up with monocultural outcomes.
• in the same way, ‘monoculture’ biomes means you cannot have the degree of biodiversity to sustain a planet, if you don’t have enough diomes you wont be able to represent the richness of our world (natural and human)
• ensure that the diome has as complete a representation of the system as possible, with appropriate granularity, resolution, quality, volume and systems ontology?

Builds on the definition of Biome

“A biome is a biogeographical unit consisting of a biological community that has formed in response to the physical environment in which they are found and a shared regional climate. Biomes may span more than one continent. Biome is a broader term than habitat and can comprise a variety of habitats.”

This insightful report, from the Royal Societies DELVE initiative, “Data Readiness: Lessons from an Emergency” delves into the crucial role of data in emergency situations and highlights valuable lessons learned. Drawing from real-life experiences, it emphasises the importance of being prepared to leverage data effectively when faced with unexpected challenges.

The initiative has gathered experience with emergency situations and stresses the significance of having a data-driven approach. It highlights the need for organisations to recognise the value of data and incorporate it into their decision-making processes. By doing so, organisations can gain critical insights that help them respond swiftly and effectively.

The report outlines key factors that contribute to successful data readiness, specifically

• the significance of investing in data infrastructure
• developing a holistic approach including tools, systems, and processes, to ensure the availability, accessibility, and quality of data
• that data readiness is not solely a technical issue but also requires organisational commitment and cultural transformation.

It stresses the importance of collaboration and knowledge sharing between different stakeholders involved in emergency response efforts, highlights the need for establishing partnerships and creating platforms that facilitate data sharing and interoperability: that through open collaboration, organisations can harness the power of collective intelligence and make informed decisions based on comprehensive and up-to-date information.

It touches upon the ethical considerations surrounding data usage in emergency situations, emphasising the need for transparency, privacy protection, and responsible data governance. By adhering to ethical guidelines, organisations can build trust with the public and ensure that data is used for the benefit of affected communities.

It offers valuable insights into achieving data readiness in emergency scenarios and underscores the significance of investing in data infrastructure, fostering collaboration, and upholding ethical standards.

Summary of key elements:

1. Importance of prepared data systems: Having well-prepared data systems is crucial for effective emergency response. It enables timely access to relevant data, which is vital for decision-making and coordination.
2. Data availability: Ensuring data availability is essential during emergencies. It involves having data collection mechanisms in place and establishing protocols for data sharing among relevant stakeholders.
3. Data accessibility: Easy and secure access to data is critical for timely decision-making. This requires establishing appropriate data access controls, and considering data privacy and security concerns.
4. Data quality: Data quality is essential for reliable analysis and decision-making. Implementing data quality assurance measures and validation processes can help maintain accurate and reliable data during emergency situations.
5. Interoperability: Ensuring interoperability among different data systems and platforms is crucial for effective coordination among various entities involved in emergency response. This facilitates seamless data exchange and collaboration.
6. Standardised approaches: Implementing standardised approaches for data collection, sharing, and analysis promotes consistency and efficiency in emergency response efforts. This includes adopting common data formats, protocols, and procedures.
7. Coordination and collaboration: Effective coordination and collaboration among stakeholders involved in emergency response are essential. This involves sharing data, aligning efforts, and establishing clear communication channels to maximise the impact of data-driven decision-making.

This very much echoes our experiences in developing data infrastructure and the data triage process.

There was confusion around ‘where is the data?’, ‘who owns what?’ and ‘what can be done with it?’.

My synopsis is that:

The ‘data gap’ is usually leadership
Data governance needs to be taken seriously at the C-suite, management and internal processes. Not doing so both creates risks and misses opportunities. This must be addressed ‘today’.

1. Legal rules need to better balance risks and benefits
   There are likely areas of legal exposure on contracts that must be (re)thought through in a way that both protects the organisation and helps to unlock innovation through data sharing. Often the default is too restrictive and/or misses entire categories of risk.
2. Company policies must be enforceable, monitored and applied
   Organisations should use procurement and contracting processes and ensure that they are fit for purpose, and have standard operating procedures for data governance. This should include clear definitions of who is responsible (e.g. ‘Data Controller’, Chief Data Officer), for what areas and purposes, and how data processing will be managed. All third-party services must contain clear definitions of IP, security, support, and maintenance (even if some services are offered for ‘free’).

One issue I heard a lot of confusion about was the ‘location’ of data.
Its location can be interpreted in two ways:

1. Its physical location

Data can be stored on computers and in systems that are under the organisation’s direct ownership or control through third parties that it uses. These might include cloud computing providers like Google, Amazon, or third-party analysis systems. It is completely fine to have data in many physical places (and ‘the cloud’ is exactly that).

The impact of ‘many physical systems’ does create an operational burden and the organisation’s data teams should aim to minimise this burden by storing data in as few systems as possible. But they must maintain direct contractual control to ensure that the data is well managed and can be held to account (e.g. such as direct contracts with third parties).

2. Its legal location

Regardless of physical location, the legal basis for data sharing must be clearly defined, contracted and enforced.

Organisations should aim to act as (and it will be in many jurisdictions) the primary data controller for all data that it collects. Making this clear is especially important when dealing with countries where rules around data are ‘early stage’. The concept of data ‘ownership’ is highly complex (especially with personal data) and a focus on ‘rights’ (rather than just ‘ethics’) can often help people focus on what’s important.

The organisation can, and should, own the IP (intellectual property) on certain data it collects ( e.g. raw such as non-personal data that its teams have collected), derivative data (aggregate statistics), reports, analyses, insights, visualisations, etc. Owning the IP allows the organisation to explicitly license it to others if it wishes to (whether under Open Data or Shared Data licenses).

The organisation will not ‘own’ personal data about individuals (e.g. EU citizens’ rights are covered by GDPR). Instead, it has a role as a data controller or as a data processor. Depending on its contracts it may have the right to do things with the data, including analysing it and sharing outputs with others.

If the organisation contracts a third party to do, for example, some data cleaning or analysis, it must (a) have the rights to do so; and, (b) do so under a contractual agreement that allows data to be processed by that third party. The organisation should not assign any ownership rights to any ‘primary’ data and nor should it assign rights to any derivate outputs (e.g. analysis) without assessing the risks or benefits of doing so. If it does, such rights must be codified in a contract.

It may be the case that sharing data with a third party can help create additional benefits (e.g. they can improve their analytics systems), in the same way using data from others can create additional benefits for the organisation.

However, there are also new risks, such as using an organisation’s data to train machine-learning / artificial intelligence systems in ways that cannot be predicted. These risks include commercial, competitive, legal, liability, IP, ethical and moral hazards.

The impact of ‘many legal contracts’ creates compound risks and the organisation must be crystal clear about its approach to data governance, including protections, licensing, processing and security. Creating common legal frameworks with multiple parties can take time, but they can also create cohesion, reducing risk and unlocking permission for innovation.

One way to think about this is to imagine ‘technical systems’ as consultants. It doesn’t matter where they are, we don’t give a consultant the right to do anything other than what we need them to do. It doesn’t stop them from learning and building on their own experience. As we move forward with new ‘ai’ systems, this takes on materially different dimensions and creates new types of risk and new types of opportunities.

The purpose of this piece was to help non-technical people understand some of the basis of ‘where’ is data, who can use it, and for what purposes. I hope you found it useful and please leave comments if you have further questions or feedback.

One personal note: I find it far more useful to think about data rights (can this data be used for that purpose in this way) rather than data ‘ethics’ (which can mean very different things to different people).

As we continue on our journey to a data-enabled world, we must ensure that data governance processes are in place to help everyone understand what they can and can’t do, and what to do when things go wrong.

1. create an algorithm for trading based on 1% value swings

Here’s a basic algorithm for trading based on 1% value swings:

1. Define your target security: Select a security that you wish to trade based on 1% value swings.
2. Set your trading parameters: Define the parameters for your trades, including the amount of capital you’re willing to invest, your desired profit margin, and your stop-loss limit.
3. Monitor the security’s value: Continuously monitor the value of your target security. You can use various tools such as trading software, financial news sources, or chart analysis to track its value.
4. Identify 1% value swings: When the security’s value changes by 1% in either direction, take note of it.
5. Buy or sell: If the value of the security has decreased by 1%, and you believe it will rebound, consider buying the security. Conversely, if the value of the security has increased by 1%, and you believe it may drop, consider selling the security.
6. Set stop-loss limit: To manage risk, set a stop-loss limit for each trade. This is the maximum amount of money you’re willing to lose if the trade doesn’t go in your favor.
7. Take profit: Set a profit target for each trade, which is the desired amount of profit you want to earn. When the security’s value reaches this target, close the trade and take your profit.
8. Repeat the process: Continue monitoring the security’s value, identifying 1% swings, and making trades as necessary.

Note: This is a basic algorithm, and there are many factors to consider when trading, such as market conditions, liquidity, volatility, and more. It’s important to do your research and fully understand the risks involved before implementing any trading strategy.

2. write this is pseudocode

Sure, here’s the pseudocode for the trading algorithm based on 1% value swings:

Note that this is a basic pseudocode and it does not include error handling or other advanced features. Additionally, some variables and functions may need to be defined and implemented separately.

3. write this in python

Sure, here’s an example implementation of the trading algorithm based on 1% value swings in Python:

target_security = "AAPL"  # define the target security

# define the trading parameters
capital = 1000
profit_margin = 1.01  # 1% profit margin
stop_loss = 0.99  # 1% stop-loss limit

while True:
    current_value = get_current_value(target_security)  # get the current value of the target security
    if current_value >= profit_margin:
        sell(target_security)  # sell the security if it has increased by 1%
    elif current_value <= stop_loss:
        buy(target_security)  # buy the security if it has decreased by 1%
    else:
        continue  # otherwise, continue monitoring the value

    # set stop-loss limit and profit target
    stop_loss_value = current_value * stop_loss
    profit_target_value = current_value * profit_margin

    while True:
        updated_value = get_current_value(target_security)  # get the updated value of the target security
        if updated_value <= stop_loss_value:
            sell(target_security)  # sell the security if it hits the stop-loss limit
            break
        elif updated_value >= profit_target_value:
            sell(target_security)  # sell the security if it hits the profit target
            break
        else:
            continue  # otherwise, continue monitoring the value

Note that this is just an example implementation and may need to be adapted to your specific trading platform or strategy. Also, the get_current_value, buy, and sell functions are not defined here and need to be implemented separately based on the API or service you’re using.

And now for some fun

write this for a quantum computer

While quantum computers have the potential to solve certain financial problems more efficiently than classical computers, it’s currently unclear how exactly they could be used for trading based on 1% value swings. As quantum computing is still in its early stages of development, there are few, if any, quantum trading algorithms that have been developed and tested at scale.

However, one possible approach to leveraging quantum computing for trading is by using quantum machine learning algorithms to analyze financial data and identify patterns that can be used to make trading decisions. For example, one could use quantum annealing to optimize portfolio selection and risk management, or quantum algorithms to perform efficient optimization of trading strategies.

Here’s an example of how one could use the Qiskit framework in Python to implement a simple quantum machine learning algorithm for financial prediction:

import numpy as np
from qiskit import Aer
from qiskit.circuit.library import ZZFeatureMap
from qiskit.aqua.algorithms import VQC
from qiskit.aqua.components.optimizers import COBYLA

# Define the target security and trading parameters
target_security = "AAPL"
capital = 1000
profit_margin = 1.01
stop_loss = 0.99

# Define the quantum feature map and backend
feature_map = ZZFeatureMap(feature_dimension=2)
backend = Aer.get_backend('qasm_simulator')

# Define the VQC classifier and optimizer
classifier = VQC(feature_map, None, None)
optimizer = COBYLA()

# Train the classifier on historical financial data
data = np.array([[0.5, 0.1], [0.1, 0.9], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6]])
labels = np.array([0, 1, 1, 0, 0])
classifier.train(data, labels)

# Use the classifier to make trading decisions
while True:
    current_data = np.array([[get_current_price(target_security), get_current_volume(target_security)]])
    predicted_label = classifier.predict(current_data)[0]
    if predicted_label == 0:
        buy(target_security)
    elif predicted_label == 1:
        sell(target_security)
    else:
        continue

    # Set the stop-loss limit and profit target
    stop_loss_value = get_current_price(target_security) * stop_loss
    profit_target_value = get_current_price(target_security) * profit_margin

    while True:
        updated_price = get_current_price(target_security)
        if updated_price <= stop_loss_value:
            sell(target_security)
            break
        elif updated_price >= profit_target_value:
            sell(target_security)
            break
        else:
            continue

Again, note that this is just a simple example of how one could use quantum machine learning for trading, and many details would need to be filled in depending on the specific application and data available.

So, there we have it. I can now learn how to write code for a quantum computer that might have some bearing on financial insights, with zero knowledge.

I’m often asked to unpack ‘data’. There’s a lot of confusion and, often, people think it’s a function of ‘IT’ and just for the geeks.

A ‘data strategy’ can often be quite shallow in its thinking and tends not to focus on data sharing—which is a cultural shift in the way collaboration needs to work.

Collaboration in a data-enabled business needs to be about how humans and machines can combine their intelligence, systems, and accelerate and optimise solutions to problems. At the core of this is the ability to easily access, share and use systems internally and externally.

Even if there’s an understanding that data should be used at a strategic level, it’s rare that there is a good understanding of data strategy at C-level, and often it’s deferred to CIO/CTO to make decisions that, in my view, should be more collaboratively defined by the whole team, aligned with business targets, and explicit contributions to ROI.

For example, I would like to think that the age of “build a data lake” or “let’s build a portal” as a starting point are over. However, my observation is that we are very far away from that.

We need to begin by thinking of data as part of our infrastructure and, with this, thinking about how it can be shared between systems regardless of where those systems are.

The Open Data Institute (ODI), frames data infrastructure as the “systems, processes, and tools that enable the creation, management, and use of data.” This includes both physical infrastructure (e.g. servers and storage systems) and virtual infrastructure (e.g. software and networks).

The definition goes further to include: the people, policies, and practices that support the creation, management, processing and usage of data; roles and responsibilities for managing data; the standards and protocols for how data should be collected, stored, and shared.

A strong data infrastructure is the foundation for data management, analytics, visualisation, and value creation. And, in a networked world, the systems for management, analysis, visualisation may exist anywhere.

A strong strategy must, therefore, address interoperability at its core.

A data infrastructure strategy is a plan for building, managing, and maintaining systems, processes, and tools that enable data creation, management and use in a way that can be shared across systems.

It is important to understand the role of data sharing in supporting outcomes and impacts and, based on this, to create a framework for sharing data.

A well-designed strategy can save time and money by rapidly connecting data and systems together: to help people make better, more informed decisions and use data to drive growth and innovation. It must take into account the types of data as well as the business processes (logistical, operational and legal), and understand the applications that rely on that data.

Critically, it must consider the organisation’s data governance policies and procedures, legal and regulatory requirements.

In summary, four core elements of a robust data infrastructure strategy must include:

1. Data governance: policies, procedures, and standards (that can be enforced) to ensure the usability, quality, security, and integrity of data, its usage and sharing (internally and externally to any organisation).
2. Data access and security: how data will be accessed and shared, both across organisations and externally, including protecting data from unauthorized access or misuse. This will combine technical and legal measures.
3. Data analytics and visualization: understanding the tools used to analyse, process, visualise and share outputs, and the processes and skills needed to use them effectively.
4. Data storage and management: where and how data will be stored, the tools and processes needed to manage data throughout its lifecycle, regardless of where the data ‘physcially’ resides. This will include time-based, jurisdictional and related policies.

Note that only point four (and part of point two) of this are functions of ‘IT’. Points one and three span the entire organisation, as managers and as users.

To focus attentiion, a data infrastructure strategy should directly support goals and outcomes, minimise risks, unlock effective usage and sharing to drive growth and innovation.

Finally, it would be prudent to begin to include ‘algorithms’ (as opposed to code) as part of strategic development as while in many cases it may be the data that is ‘moving to the processing’, it is also the case that the algorithms may have to ‘go where the data is’ for efficiency and/or compliance reasons. This may be the subject of a future post.

https://dgen.net/d0

Having spent quite a number of decades working on data at scale, we’ve worked to codify some of the baseline principles that could underpin a trusted web of data.

The purpose is to unlock open markets and increase the precompetitive spaces to both

1. commoditize current parts of the data ecosystem; and,
2. increase the efficiency, value and potential of using data to solve problems.

To me, this is a step we need to take to create data as infrastructure and unlock the next generation of data-enabled innovation.  Open document for discussion here

On one level, we could draw an analogy with energy: there were many ways of connecting, different voltages, sockets, etc. Then we created the national grid and built our economy on it. Getting it right was tough, but it shifted the value chain to delivering services at web-scale.

Another is open banking: banks had ‘ways of being interoperable’ but standardising this process is driving a different kind of innovation at application, business and consumer levels. It’s now in 80 countries (5 years since launch), and is a $23B market that is projected to grow at 23% CAGR to $150B by 2032 [citation].

Why wouldn’t we enable this for all parts of our economy?

Learn more here: