{"id":7480,"date":"2025-04-06T09:00:00","date_gmt":"2025-04-06T09:00:00","guid":{"rendered":"https:\/\/dgen.net\/0\/?p=7480"},"modified":"2025-04-06T14:08:41","modified_gmt":"2025-04-06T14:08:41","slug":"fair-weather-foul-weather-navigating-to-positive-impact-in-turbulent-times","status":"publish","type":"post","link":"https:\/\/dgen.net\/0\/2025\/04\/06\/fair-weather-foul-weather-navigating-to-positive-impact-in-turbulent-times\/","title":{"rendered":"Fair weather, foul weather: navigating to positive impact in turbulent times"},"content":{"rendered":"\n

The National Data Library can deliver its vision,
but it must pivot to build trust in the age of today\u2019s web<\/a><\/h4>\n\n\n\n

This post builds on observations and conversations across government and industry (and internationally) since my previous post on \u201cHow can we find the Goldilocks Zone of our National Data Infrastructure?<\/a>\u201d.<\/p>\n\n\n\n

When we\u2019re building into a future where technology has such clear benefits it is often hard to be heard above the noise of<\/em> the possible<\/em>. And yet I believe we must also \u2018grasp the nettle\u2019 regarding equally possible negative or unintended consequences. While we must not \u201cstifle innovation and growth\u201d, we also must ensure we don\u2019t scuttle our ship in the process.<\/p>\n\n\n\n

The first misconception I want to address is that data is not a technology<\/em>.<\/p>\n\n\n\n

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

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.<\/p>\n\n\n\n

\n

Treating data as if it were software is comparing apples and carts.<\/p>\n<\/blockquote>\n\n\n\n

Context<\/h1>\n\n\n\n

Having been a tech entrepreneur for 30 years, I\u2019ve sat in meetings with Silicon Valley investors where the \u201cmove fast and break things, then ask for forgiveness\u201d mantra has been very present. Innovators love to build; building is fun and very rewarding \u2014 both intellectually and, sometimes, financially.<\/p>\n\n\n\n

It\u2019s common to worry about the consequences later, even when \u2018later\u2019 can be measured in decades. Those raising alarm bells are often badged as \u2018activists\u2019. This is a label I\u2019ve never felt helpful: we can draw parallels with the past when \u2018health risks of smoking\u2019, through to catastrophic climate change, risked people being boxed as \u2018alarmist\u2019 instead of \u2018anxious-with-cause\u2019.<\/p>\n\n\n\n

I\u2019ve 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.<\/p>\n\n\n\n

A lot has changed since my previous posts on data infrastructure<\/a>. We must now, right now<\/strong>, <\/strong>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.<\/p>\n\n\n\n

Trust isn\u2019t just a buzzword; it underpins our society and, in a digital age, this means addressing our data infrastructure, ensuring our <\/em>safety<\/em><\/strong>, de-risking <\/em>innovation<\/em><\/strong> and enabling <\/em>growth<\/em><\/strong> in turbulent times. Trust helps us build all of these, for the long term.<\/em><\/p>\n\n\n\n

Going from vision to implementation is hard<\/h1>\n\n\n\n
\"\"<\/a><\/figure>\n\n\n\n

There is a natural tendency towards overreach in most systems \u2014 this is a very human attribute. When trying to innovate, our bureaucratic systems can often feel frustrating (good entrepreneurs \u2014 both inside and outside of government \u2014 know how to navigate around, or sometimes \u2018through\u2019, such systems).<\/p>\n\n\n\n

Today, in 2025, with data and<\/em> the web and<\/em> 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<\/em>.<\/p>\n\n\n\n

To that end, I believe we must take steps to better understand<\/em> and balance<\/em> 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.<\/p>\n\n\n\n

The National Data Library (NDL) represents significant intent<\/strong> 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<\/strong>.<\/p>\n\n\n\n

Icebreaker One has already made a strong case<\/a> (selected for publication by the Wellcome Trust) for the National Data Library to:<\/p>\n\n\n\n

    \n
  1. Focus on defined\u00a0users<\/strong>\u00a0and\u00a0use cases<\/strong>\u00a0rather than datasets, just like any effective data infrastructure.<\/li>\n\n\n\n
  2. Begin life as a simple, decentralised version, that would curate and improve the\u00a0discovery<\/strong>\u00a0of, and\u00a0timely access<\/strong>\u00a0to, public sector data for research.<\/li>\n\n\n\n
  3. Experiment with more complex architectures for\u00a0harmonising<\/strong>\u00a0access<\/strong>\u00a0to data drawn from across multiple government departments.<\/li>\n<\/ol>\n\n\n\n

    Cutting across all of this, the NDL must<\/strong> be built, from its core, in ways that engender trust<\/strong>. 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.<\/p>\n\n\n\n

    The NDL can still maintain its grand vision to improve outcomes for all<\/strong> of us, connecting data, rapidly unlocking insights, and leading the way.<\/p>\n\n\n\n

    More than its technical architecture, the<\/strong> nature of its funding and staffing will shape what it can achieve<\/strong>. I\u2019d 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.<\/p>\n\n\n\n

    \n

    We can go far together if we each do one thing well.<\/p>\n<\/blockquote>\n\n\n\n

    We can bind our work, together, with common principles and practices<\/strong>. There will be no \u2018one hub\u2019 \u2014 if you are a hub in a collection of hubs, you\u2019re a node. Even a National Library is one node in a network of knowledge. There isn\u2019t only one search engine in the world, nor will there be one AI.<\/p>\n\n\n\n

    We must also approach, with a clear perspective, that one-time broad-based consent is just one<\/strong> limited use case<\/strong>. It may apply to certain, relatively static, data but a different approach is required for highly variable data: data is rarely static<\/strong>. The conditions on which that \u2018one-time\u2019 consent, as we have also seen, will also change based on lived experience (e.g. changes in corporate and political governance).<\/p>\n\n\n\n

    In a world of rapid change, we must pivot rapidly but
    never forget the foundations of trust beneath our feet<\/em><\/p>\n\n\n\n

    Building on the Smart Data<\/a> model, there are known ways to apply decentralised, user-controlled, real-time, granular consent. There are dynamic solutions in place today<\/em> which enable peer-to-peer data sharing via secure APIs<\/a>. 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.<\/p>\n\n\n\n

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

    This is where implementation matters. If we over-centralise and over-reach in our ambition we run the risk of the litany of failed \u2018IT projects\u2019 which have cost the taxpayer billions.<\/p>\n\n\n\n

    I see many projects, even some with the word \u2018governance\u2019 in their titles, take a technology-led approach rather than a governance-led approach: data is not a technology\u2014it enables insights about its subjects and those subjects have rights.<\/p>\n\n\n\n

    Recommendations<\/h1>\n\n\n\n
    \n

    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.<\/strong><\/p>\n<\/blockquote>\n\n\n\n

    The good news is that we can run, at pace, fast but not foolhardy<\/em>, 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.<\/p>\n\n\n\n

    Phase 1: Immediate reorientation on purpose and language (0\u20136 Months)<\/strong><\/p>\n\n\n\n

    Step 1: Transition the \u201cLibrary Card\u201d model to a Trust-based, scalable model<\/strong><\/p>\n\n\n\n