Last week they held a workshop looking at data governance in the civil society sector. The ODI were invited to help out, and I chaired a session looking at collaborative maintenance of data. I believe the Royal Society will be publishing a longer write-up of the workshop over the coming weeks.
This blog post is a written version of a short ten minute talk I gave during the workshop. The slides are public.
You might already be familiar with terms like “crowd-sourcing” or “citizen science”. Both of those are examples of collaborative maintenance. But it can take other forms too. At the ODI we use collaborative maintenance of data to refer to any scenario where organisations and communities are sharing the work of collecting and maintaining data.
It might be helpful to position collaborative maintenance alongside other approaches that are part of “open culture”. These include open standards, open source, and open data. Let’s look at each of them in turn.
Open standards for data are reusable, shared agreements that shape how we collect, share, govern and use data. There are different types of open standards. Some are technical, and describe file formats and methods of exchanging data. Others are higher-level and capture codes of practices and protocols for collecting data. Open standards are best developed collaboratively, so that everyone impacted by or benefiting from the standard can help shape it.
Open source involves collaborating to create reusable, openly licensed code and applications. Some open source projects are run by individuals or small communities. Others are backed by larger commercial organisations. This collaborative work is different to that of open standards. For example, it involves identifying and agreeing features, writing and testing code and producing documentation to allow others to use it.
For example, the open government movement originally focused on open data as a means to increase transparency of governments. More recently there is a shift towards using open data to help address a variety of social, economic and environmental challenges. In contrast, as part of the open science movement, there is a different role for open data. Recent attention has been on the use of open data to address the reproducibility crisis around research. Or to help respond to emerging health issues, like Coronavirus.
With a few exceptions, the main approach to open data has been a single organisation (or researcher) publishing data that they have already collected. There may be some collaboration around use of that data, but not in its collection or maintenance.
We can think of collaborative maintenance as about taking the approach used in open source and applying it to data. Collaborative maintenance involves collaboration across the full lifecycle of a dataset.
Some examples might be helpful.
OpenStreetMap is a collaboratively produced spatial database of the entire world. While it was originally produced by individuals and communities, it is now contributed to by large organisations like Facebook, Microsoft and Apple. The Humanitarian OpenStreetMap community focuses on the collection and use of data to support humanitarian activities. The community are involved in deciding what data to collect, prioritising maintenance of data following disasters, and mapping activities either on the ground or remotely. The community works across the lifecycle and is self-directing.
Common Voice is a Mozilla project. It aims to build an open dataset to support voice recognition applications. By asking others to contribute to the dataset, they hope to make it more comprehensive and inclusive. Mozilla have defined what data will be collected and the tasks to be carried out, but anyone can contribute to the dataset by adding their voice or transcribing a recording. It’s this open participation that could help ensure that the dataset represents a more diverse set of people.
Edubase is maintained by the Department for Education (DfE). It’s our national database of schools. It’s used in a variety of different applications. Like Mozilla, DfE are acting as the steward of the data and have defined what information should be collected. But the work of populating and maintaining the shared directory is carried out by people in the individual schools. This is the best way to keep that data up to date. Those who are know when the data has changed have the ability to update it. The contributors all benefit from shared resource.
Build a shared directory is a common use for collaborative maintenance. But there are others.
For example, we can work together to decide what data to collect. We can share the work of collecting and maintaining data, ensuring its quality and governing access to it. We can use open source to help to build the tools to support those communities.
We’ve developed the collaborative maintenance guidebook to help support the design of new services and platforms. It includes some background and a worked example. The bulk of the guidebook is a set of “design patterns” that describe solutions to common problems. For example how to manage quality when many different people are contributing to the same dataset.
We think collaborative maintenance can be useful in more projects. For civil society organisations collaborative maintenance might help you engage with communities that you’re supporting to collect and maintain useful data. It might also be a tool to support collaboration across the sector as a means of building common resources.
The guidebook is at an early stage and we’d love to get feedback on it contents. Or help you apply it to a real-world project. Let us know what you think!
There’s lots to love about the “Value of Data” report. Like the fantastic infographic on page 9. I’ll wait while you go and check it out.
Great, isn’t it?
My favourite part about the paper is that it’s taught me a few terms that economists use, but which I hadn’t heard before. Like “Incomplete contracts” which is the uncertainty about how people will behave because of ambiguity in norms, regulations, licensing or other rules. Finally, a name to put to my repeated gripes about licensing!
But it’s the term “option value” that I’ve been mulling over for the last few days. Option value is a measure of our willingness to pay for something even though we’re not currently using it. Data has a large option value, because its hard to predict how its value might change in future.
Organisations continue to keep data because of its potential future uses. I’ve written before about data as stored potential.
The report notes that the value of a dataset can change because we might be able to apply new technologies to it. Or think of new questions to ask of it. Or, and this is the interesting part, because we acquire new data that might impact its value.
So, how does increasing access to one dataset affect the value of other datasets?
Moving data along the data spectrum means that increasingly more people will have access to it. That means it can be used by more people, potentially in very different ways than you might expect. Applying Joy’s Law then we might expect some interesting, innovative or just unanticipated uses. (See also: everyone loves a laser.)
But more people using the same data is just extracting additional value from that single dataset. It’s not directly impacting the value of other dataset.
To do that we need to use that in some specific ways. So far I’ve come up with seven ways that new data can change the value of existing data.
Comparison. If we have two or more datasets then we can compare them. That will allow us to identify differences, look for similarities, or find correlations. New data can help us discover insights that aren’t otherwise apparent.
Enrichment. New data can enrich an existing data by adding new information. It gives us context that we didn’t have access to before, unlocking further uses
Validation. New data can help us identify and correct errors in existing data.
Linking. A new dataset might help us to merge some existing dataset, allowing us to analyse them in new ways. The new dataset acts like a missing piece in a jigsaw puzzle.
Scaffolding. A new dataset can help us to organise other data. It might also help us collect new data.
Improve Coverage. Adding more data, of the same type, into an existing pool can help us create a larger, aggregated dataset. We end up with a more complete dataset, which opens up more uses. The combined dataset might have a a better spatial or temporal coverage, be less biased or capture more of the world we want to analyse
Increase Confidence. If the new data measures something we’ve already recorded, then the repeated measurements can help us to be more confident about the quality of our existing data and analyses. For example, we might pool sensor readings about the weather from multiple weather stations in the same area. Or perform a meta-analysis of a scientific study.
I don’t think this is exhaustive, but it was a useful thought experiment.
In this post I wanted to clarify the differences between three types of agreements that will govern your use of data. There are others. But from a data consumer point of view these are most common.
If you’re involved in any kind of data project, then you should have read all of relevant agreements that relate to data you’re planning to use. So you should know what to look for.
Data Sharing Agreements
Data sharing agreements are usually contracts that will have been signed between the organisations sharing data. They describe how, when, where and for how long data will be shared.
They will include things like the purpose and legal basis for sharing data. They will describe the important security, privacy and other considerations that govern how data will be shared, managed and used. Data sharing agreements might be time-limited. Or they might describe an ongoing arrangement.
When the public and private sector are sharing data, then publishing a register of agreements is one way to increase transparency around how data is being shared.
Data sharing agreements are most useful when organisations, of any kind, are sharing sensitive data. A contract with detailed, binding rules helps everyone be clear on their obligations.
Licences are a different approach to defining the rules that apply to use of data. A licence describes the ways that data can be used without any of the organisations involved having to enter into a formal agreement.
A licence will describe how you can use some data. It may also place some restrictions on your use (e.g. “non-commercial”) and may spell out some obligations (“please say where you got the data”). So long as you use the data in the described ways, then you don’t need any kind of explicit permission from the publisher. You don’t even have to tell them you’re using it. Although it’s usually a good idea to do that.
Licences remove the need to negotiate and sign agreements. Permission is granted in advance, with a few caveats.
Licences aren’t time-limited. They’re perpetual. At least as long as you follow your obligations.
Licences are best used for open and public data. Sometimes people use data sharing agreements when a licence might be a better option. That’s often because organisations know how to do contracts, but are less confident in giving permissions. Especially if they’re concerned about risks.
Sometimes, even if there’s an open licence to use data, a business would still prefer to have an agreement in place. That’s might be because the licence doesn’t give them the freedoms they want, or they’d like some additional assurances in place around their use of data.
Terms and Conditions
Like a Data Sharing Agreement, a set of terms and conditions is something that you formally agree to. It might be by checking a box rather than signing a document, but its still an agreement.
A good set of terms and conditions will clearly and separately identify those rules that relate to your use of the service (e.g. how often you can use it) from those rules that relate to the data provided to you. Ideally the terms would just refer to a separate licence. The Met Office Data Point terms do this.
This article was first published in the February 2030 edition of Sustain magazine. Ten years since the public launch of GUIDE we sit down with its designers to chat about its origin and what’s made it successful.
It’s a Saturday morning and I’m sitting in the bustling cafe at Tyntesfield house, a National Trust property south of Bristol. I’m enjoying a large pot of tea and a slice of cake with Joe Shilling and Gordon Leith designers of one of the world’s most popular social applications: GUIDE. I’d expected to meet somewhere in the city, but Shilling suggested this as a suitable venue. It turns out Tyntesfield plays a part in the origin story of GUIDE. So its fitting that we are here for the tenth anniversary of its public launch.
SHILLING: “Originally we were just playing. Exploring the design parameters of social applications.”
He stirs the pot of tea while Leith begins sectioning the sponge cake they’ve ordered.
SHILLING: “People did that more in the early days of the web. But Twitter, Facebook, Instagram…they just kind of sucked up all the attention and users. It killed off all that creativity. For a while it seemed like they just owned the space…But then TikTok happened…”
He pauses while I nod to indicate I’ve heard of it.
SHILLING: “…and small experiments like Yap. It was a slow burn, but I think a bunch of us started to get interested again in designing different kinds of social apps. We were part of this indie scene building and releasing bespoke social networks. They came and went really quickly. People just enjoyed them whilst they were around.”
Leith interjects around a mouthful of cake:
LEITH: “Some really random stuff. Social nets with built in profile decay so they were guaranteed to end. Made them low commitment, disposable. Messaging services where you could only post at really specific, sometimes random times. Networks that only came online when its members were in precise geographic coordinates. Spatial partitioning to force separation of networks for home, work and play. Experimental, ritualised interfactions.”
SHILLING: “The migratory networks grew out of that movement too. They didn’t last long, but they were intense. ”
LEITH: “Yeah. Social networks that just kicked into life around a critical mass of people. Like in a club. Want to stay a member…share the memes? Then you needed to be in its radius. In the right city, at the right time. And then keep up as the algorithm shifted it. Social spaces herding their members.”
SHILLING: “They were intense and incredibly problematic. Which is why they didn’t last long. But for a while there was a crowd that loved them. Until the club promoters got involved and then that commercial aspect killed it.”
GUIDE had a very different starting point. Flat sharing in Bristol, the duo needed money. Their indie credibility was high, but what they were looking for a more mainstream hit with some likelihood of revenue. The break-up of Facebook and the other big services had created an opportunity which many were hoping to capitalise on. But investment was a problem.
LEITH: “We wrote a lot of grant proposals. Goal was to use the money to build out decent code base. Pay for some servers that we could use to launch something bigger”.
Shilling pours the tea, while Leith passes me a slice of cake.
SHILLING: “It was a bit more principled that that. There was plenty of money for apps to help with social isolation. We thought maybe we could build something useful, tackle some social problems, work with a different demographic than we had before. But, yeah, we had our own goals too. We had to take what opportunities were out there.”
LEITH: “My mum had been attending this Memory Skills group. Passing around old photos and memorabilia to get people talking and reminiscing. We thought we could create something digital.”
SHILLING: “We managed to land a grant to explore the idea. We figured that there was a demographic that had spent time connecting not around the high street or the local football club. But with stuff they’d all been doing online. Streaming the same shows. Revisiting old game worlds. We thought those could be really useful touch points and memory triggers too. And not everyone can access some of the other services.”
LEITH: “Mum could talk for hours about Skyrim and Fallout”.
SHILLING: “So we prototyped some social spaces based around that kind of content. It was during the user testing that we had the real eye-opener”.
The first iterations of the app that ultimately became GUIDE were pretty rough. Shilling and Leith have been pretty open about their early failures.
LEITH: “The first iteration was basically a Twitch knock-off. People could join the group remotely, chat to each other and watch whatever the facilitator decided to stream.”
SHILLING: “Engagement was low. We didn’t have cash to license a decent range of content. The facilitators needed too much training on the streaming interface and real-time community management.”
LEITH: “I then tried getting a generic game engine to boot up old game worlds, so we could run tours. But the tech was a nightmare to get working. Basically needed different engines for different games”
SHILLING: “Some of the users loved it, mainly those that had the right hardware and were already into gaming. But it didn’t work for most people. And again…I…we were worried about licensing issues”
LEITH: “So we started testing a customised, open source version of Yap. Hosted chat rooms, time-limited rooms and content embedding…that ticked a lot of boxes. I built a custom index over the Internet Archive, so we could use their content as embeds”.
SHILLING: “There’s so much great stuff that people love in the Internet Archive. At the time, not many services were using it. Just a few social media accounts. So we made using it a core feature. It neatly avoided the licensing issues. We let the alpha testers run with the service for a while. We gave them and the memory service facilitators tips on hosting their own chats. And basically left them to it for a few weeks. It was during the later user testing that we discovered they were using it in different ways that we’d expected.”
Instead of having conversations with their peer groups, the most engaged users were using it to chat with their families. Grandparents showing their grandchildren stuff they’d watched, listened to, or read when they were younger.
SHILLING: “They were using it to tell stories”
Surrounded by the bustle in the cafe, we pause to enjoy the tea and cake. Then Shilling gestures around the room.
SHILLING: “We came here one weekend. To get out of the city. Take some time to think. They have these volunteers here. One in every room of the house. People just giving up their free time to answer any questions you might have as you wander around. Maybe, point out interesting things you might not have noticed? Or, if you’re interested, tell you about some of things they love about the place. It was fascinating. I realised that’s how our alpha testers were using the prototype…just sharing their passions with their family.”
LEITH: “So this is where GUIDE was born. We hashed out the core features for the next iteration in a walk through the grounds. Fantastic cake, too.”
The familiar, core features of GUIDE have stayed roughly the same since that day.
Anyone can become a Guide and create a Room which they can use to curate and showcase small collections of public domain or openly licensed content. But no more than seven videos, photos, games or whatever else you can embed from the Internet Archive. Room contents can be refreshed once a week.
Visitors are limited to a maximum of five people. Everyone else gets to wait in a lobby, with new visitors being admitted every twenty minutes. Audio feeds only from the Guides, allowing them to chat to Visitors. But Visitors can only interact with Guides via a chat interface that requires building up messages — mostly questions — from a restricted set of words and phrases that can be tweaked by Guides for their specific Room. Each visitor limited to one question every five minutes.
LEITH: “The asymmetric interface, lobby system and cool-down timers were lifted straight from games. I looked up the average number of grandchildren people had. Turns out its about fives, so we used that to size Rooms. The seven item limit was because I thought it was a lucky number. We leaned heavily on the Internet Archive’s bandwidth early on for the embeds, but we now mirror a lot of stuff. And donate, obviously.”
SHILLING: “The restricted chat interface has helped limit spamming and moderation. No video feeds from Guides means that the focus stays on the contents of the Room, not the host. Twitch had some problematic stuff which we wanted to avoid. I think its more inclusive.”
LEITH: “Audio only meant the ASMR crowd were still happy though”.
Today there are tens of thousands of Rooms. Shilling shows me a Room where the Guide gives tours of historical maps of Bath, mixing in old photos for context. Another, “Eleanor’s Knitting Room” curates knitting patterns. The Guide alternating between knitting tips and cultural critiques.
Leith has a bookmarked collection of retro-gaming Rooms. Doom WAD teardowns and classic speed-runs analysis for the most part.
In my own collection, my favourite is a Room showing a rota of Japanese manhole cover designs, the Guide an expert on Japanese art and infrastructure. I often have this one a second screen whilst writing. The lobby wait time is regularly over an hour. Shilling asks me to share that one with him.
LEITH: “There are no discovery tools in Guide. That was deliberate from the start. Strictly no search engine. Want to find a Room? You’ll need to be invited by a Guide or grab a link from a friend”.
SHILLING: “Our approach has been to allow the service to grow within the bounds of existing communities. We originally marketed the site to family groups, and an older demographic. The UK and US were late adopters, the service was much more popular elsewhere for a long time. Things really took off when the fandoms grabbed hold of it.”
An ecosystem of recommendation systems, reviews and community Room databases has grown up around the service. I asked whether that defeated the purpose of not building those into the core app?
LEITH: “It’s about power. If we ran those features then it would be our algorithms. Our choice. We didn’t want that.”
SHILLING: “We wanted the community to decide how to best use GUIDE as social glue. There’s so many more creative ways in which people interact with and use the platform now”.
The two decline to get into discussion of the commercial success of GUIDE. It’s well-documented that the two have become moderately wealthy from the service. More than enough to cover that rent in the city centre. Shilling only touches on it briefly:
SHILLING: “No ads and a subscription-based service has kept us honest. The goal was to pay the bills while running a service we love. We’ve shared a lot of that revenue back with the community in various ways”.
Of course, all of this was possible before. YouTube and Twitch supported broadcasts and streaming for years, and many people used them in similar ways. But the purposeful design of a more dedicated interface highlights how constraints can shape a community and spark creativity. Removal of many of the asymmetries inherent in the design of those older platforms has undoubtedly helped.
While we finished the last of the tea, I asked them what they thought made the service successful.
SHILLING: “You can find, watch and listen to any of the material that people are sharing in GUIDE on the open web. Just Google it. But I don’t think people just want more content. They want context. And its people that bring that context to life. You can find Rooms now where there’s a relay of Guides running 24×7. Each Guide highlighting different aspects of the exact same collection. Costume design, narrative arcs and character bios. Historical and cultural significance. Personal stories. There’s endless context to discover around the same content. That’s what fandoms have understood for years.”
LEITH: “People just like stories. We gave them a place to tell them. And an opportunity to listen.”
This post is a thought experiment. It considers how existing laws that cover the registration and testing of hazardous substances like pesticides might be used as an analogy for thinking through approaches to regulation of AI/ML.
As a thought experiment its not a detailed or well-research proposal, but there are elements which I think are interesting. I’m interested in feedback and also pointers to more detailed explorations of similar ideas.
A cursory look of substance registration legislation in the EU and US
Under EU REACH legislation, if you want to manufacture or import large amount of potentially hazardous chemical substances then you need to register with the ECHA. The registration process involves providing information about the substance and its potential risks.
“No data no market” is a key principle of the legislation. The private sector carries the burden of collecting data and demonstrating safety of substances. There is a standard set of information that must be provided.
In order to demonstrate the safety, companies may need to carry out animal testing. The legislation has been designed to minimise unnecessary animal testing. While there is an argument that all testing is unnecessary, current practices requires testing in some circumstances. Where testing is not required, then other data sources can be used. But controlled animal tests are the proof of last resort if no other data is available.
To further minimise the need to carry out tests on animals, the legislation is designed to encourage companies registering the same (or similar) substances to share data with one another in a “fair, transparent and non-discriminatory way”. Companies There is detailed guidance around data sharing, including a legal framework and guidance on cost sharing.
The coordination around sharing data and costs is achieved via a SIEF (PDF), a loose consortia of businesses looking to register the same substance. There is guidance to help facilitate creation of these sharing forums.
The compensation, along with some exclusive use arrangements, are intended to avoid discouraging original research, testing and registration of new substances. Companies that bear the costs of developing new substances can have exclusive use for a period and expect some compensation for research costs to bring to market. Later manufacturers can benefit from the safety testing results, but have to pay for the privilege of access.
Summarising some design principles
Based on my reading, I think both sets of legislation are ultimately designed to:
increase safety of the general public, by ensuring that substances are properly tested and documented
require companies to assess the risks of substances
take an ethical stance on reducing unnecessary animal testing and other data collection by facilitating
require companies to register their intention to manufacture or import substances
enable companies to coordinate in order to share costs and other burdens of registration
provide an arbitration route if data is not being shared
avoid discouraging new research and development by providing a cost sharing model to offset regulatory requirements
Parallels to AI regulation
What if we adopted a similar approach towards the regulation of AI/ML?
When we think about some of the issues with large scale, public deployment of AI/ML, I think the debate often highlights a variety of needs, including:
greater oversight about how systems are being designed and tested, to help understand risks and design problems
understanding how and where systems are being deployed, to help assess impacts
minimising harms to either the general public, or specific communities
thorough testing of new approaches to assess immediate and potential long-term impacts
reducing unnecessary data collection that is otherwise required to train and test models
exploration of potential impacts of new technologies to address social, economic and environmental problems
to continue to encourage primary research and innovation
That list is not exhaustive. I suspect not everyone will necessarily agree on the importance of all elements.
However, if we look at these concerns and the principles that underpin the legislation of hazardous substances, I think there are a lot of parallels.
Applying the approach to AI
What if, for certain well-defined applications of AI/ML such as facial recognition, autonomous vehicles, etc, we required companies to:
register their systems, accompanies by a standard set of technical, testing and other documentation
carry out tests of their system using agreed protocols, to encourage consistency in comparison across testing
share data, e.g via a data trust or similar model, in order to minimise the unnecessary collection of data and to facilitate some assessment of bias in training data
demonstrate and document the safety of their systems to agreed standards, allowing public and private sector users of systems and models to make informed decisions about risks, or to support enforcement of legal standards
coordinate to share costs of collecting and maintaining data, conducting tests of standard models, etc
and, perhaps, after a period, accept that trained models would become available for others to reuse, similarly to how medicines or other substances may ultimately be manufactured by other companies
In addition to providing more controls and assurance around how AI/ML is being deployed, an approach based on facilitating collaboration around collection of data might help nudge new and emerging sectors into a more open direction, right from the start.
There are a number of potential risks and issues which I will acknowledge up front:
sharing of data about hazardous substance testing doesn’t have to address data protection. But this could be factored in to the design, and some uses of AI/ML draw on non-personal data
we may want to simply ban, or discourage use of some applications of AI/ML, rather than enable it. But at the moment there are few, if any controls
the approach might encourage collection and sharing of data which we might otherwise want to restrict. But strong governance and access controls, via a data trust or other institution might actually raise the bar around governance and security, beyond that which individual businesses can, or are willing to achieve. Coordination with a regulator might also help decide on how much is “enough” data
the utility of data and openly available models might degrade over time, requiring ongoing investment
the approach seems most applicable to uses of AI/ML with similar data requirements, In practice there may be only a small number of these, or data requirements may vary enough to limit benefits of data sharing
Again, not an exhaustive list. But as I’ve noted, I think there are ways to mitigate some of these risks.
Let me know what you think, what I’ve missed, or what I should be reading. I’m not in a position to move this forward, but welcome a discussion. Leave your thoughts in the comments below, or ping me on twitter.
We’re at the end of week 5 of 2020, of the new decade and I’m on a diet.
I’m back to using MyFitnessPal again. I’ve used it on and off for the last 10 years whenever I’ve decided that now is the time to be more healthy. The sporadic, but detailed history of data collection around my weight and eating habits mark out each of the times when this time was going to be the time when I really made a change.
My success has been mixed. But the latest diet is going pretty well, thanks for asking.
This morning the app chose the following feature to highlight as part of its irregular nudges for me to upgrade to premium.
Downloading data about your weight, nutrition and exercise history are a premium feature of the service. This gave me pause for thought for several reasons.
Under UK legislation, and for as long as we maintain data adequacy with the EU, I have a right to data portability. I can request access to any data about me, in a machine-readable format, from any service I happen to be using.
Rather than enabling this access via an existing product feature, they’ve decide to make me and everyone else request the data directly. Every time I want to use it.
This might be a deliberate decision. They’re following the legislation to the letter. Perhaps its a conscious decision to push people towards a premium service, rather than make it easy by default. Their user base is international, so they don’t have to offer this feature to everyone.
I’m hoping to see more exploration of the potential benefits and uses of data portability in 2020.
I think we need to re-frame the discussion away from compliance and on to commercial and consumer benefits. For example, by highlighting how access to data contributes to building ecosystems around services, to help retain and grow a customer base. That is more likely to get traction than a continued focus on compliance and product switching.
MyFitnessPal already connects into an ecosystem of other services. A stronger message around portability might help grow that further. After all, there are more reasons to monitor what you eat than just weight loss.
Clearer legislation and stronger guidance from organisations like ICO and industry regulators describing how data portability should be implemented would also help. Wider international adoption of data portability rights wouldn’t hurt either.
There’s also a role for community driven projects to build stronger norms and expectations around data portability. Projects like OpenSchufa demonstrate the positive benefits of coordinated action to build up an aggregated view of donated, personal data.
But I’d also settle with a return to the ethos of the early 2010s, when making data flow between services was the default. Small pieces, loosely joined.
If we want the big platforms to go on a diet, then they’re going to need to give up some of those bytes.
It’s hard to read an article about data science or really anything that involves creating something useful from data these days without tripping over this factoid, or some variant of it:
Data scientists spend 80% of their time cleaning data rather than creating insights.
Data scientists only spend 20% of their time creating insights, the rest wrangling data.
It’s frequently used to highlight the need to address a number of issues around data quality, standards, access. Or as a way to sell portals, dashboards and other analytic tools.
The thing is, I think it’s a bullshit statistic.
Not because I don’t think there aren’t improvements to be made about how we access and share data. Far from it. My issue is more about how that statistic is framed and because its just endlessly parroted without any real insight.
What did the surveys say?
I’ve tried to dig out the underlying survey or source of that factoid, to see if there’s more context. While the figure is widely referenced its rarely accompanied by a link to a survey or results.
Kaggle, 2018: “During a typical data science project, what percent of your time is spent engaged in the following tasks? ~11% Gathering data, 15% Cleaning data…”
Again, much less than 80%
Only the Crowdflower survey reports anything close to 80%, but you need to lump in actually collecting data as well.
Are there other sources? I’ve not spent too much time on it. But this 2015 bizreport article mentions another survey which suggests “between 50% and 90% of business intelligence (BI) workers’ time is spend prepping data to be analyzed“.
And an August 2014 New York Times article states that: “Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data“. But doesn’t link to the surveys, because newspapers hate links.
So looking at the figures, it looks to me that this is a bullshit statistic. Data scientists do a whole range of different types of task. If you arbitrary label some of these as analysis and others not, then you can make them add up to 80%.
But that’s not the only reason why I think its a bullshit statistic.
Who, might I ask, is supposed to do this janitorial work?
I would argue that spending time working with data. To transform, explore and understand it better is absolutely what data scientists should be doing. This is the medium they are working in.
Understand the material better and you’ll get better insights.
Secondly, I think data science use cases and workflows are a poor measure for how well data is published. Data science is frequently about doing bespoke analysis which means creating and labelling unique datasets. No matter how cleanly formatted or standardised a dataset its likely to need some work.
A sculptor has different needs than a bricklayer. They both use similar materials. And they both create things of lasting value and worth.
We could measure utility better using other assessments than time spent on bespoke work.
Thirdly, it’s measuring the wrong thing. Actually, maybe some friction around the use of data is a good thing. Especially if it encourages you to spend more time understanding a dataset. Even more so if it in any way puts a break on dumb uses of machine-learning.
If we want the process of accessing, using and sharing data to be as frictionless as possible in a technical sense, then let’s make sure that is offset by adding friction elsewhere. E.g. to add checkpoints for reviews of ethical impacts. No matter how highly paid a data scientist is, the impacts of poor use of data and AI can be much, much larger.
Don’t tell me that data scientists are spending time too much time working with data and not enough time getting insights into production. Tell me that data scientists are increasingly spending 50% of their time considering the ethical and social impacts of their work.
I like to read things when I have time to reduce distractions and give me change to absorb several viewpoints rather than simply the latest, hottest takes.
I’ve fine-tuned my approach to managing my reading and research. A few of the tools and services have changed, but the essentials stay the same. If you’re interested, here’s how I’ve made things work for me:
Manages all my subscriptions for blogs, newsletters and more into one easily accessible location
Lots of sites still support RSS its not dead, merely resting
Feedbin is great at discovering feeds if you just paste in a site URL. One of the magic parts of RSS
You can also subscribe to newsletters with a special Feedbin email address and they’ll get delivered to your reader. Brilliant. You’re not making me go back into my inbox, its scary in there.
Feedme. Feedbin allows me to read posts anywhere, but I use this Android app (there are others) as a client instead
Regularly syncs with Feedbin, so I can have all the latest unread posts on my phone for the commute or an idle few minutes
It provides a really quick interface to skim through posts and either immediately read the or add them to my “to read” list, in Pocket…
Pocket. Mobile and web app that I basically use as a way to manage a backlog of things “to read”.
Gives me a clutter free (no ads!) way to read content either in the browser (which I rarely do) or on my phone
It has its issues with some content, but you can easily switch to a full web view
Not everything I want to read comes in via my feed reader so I take links from Slack, Twitter or elsewhere and use the Pocket browser extension or its share button integration to stash things away for later reading. Basically if its not a 1-2 minute read it goes into Pocket until I’m ready for it. Keeps the number of browser tabs under control too.
The offline content syncing makes it great for using on my commute, especially on the tube
The end result is a fully self-curated feed of interesting stuff. I’m no longer fighting someone else’s algorithm, so I can easily find things again.
I can minimise number of organisations I’m following on twitter, and just subscribe to their blogs. Also helps to buck the trend towards more email newsletters which are just blogs but you’re all in denial.
Also helps to reduce the number of distractions, and fight the pressure to keep checking on twitter in case I’ve missed something interesting. It’ll be in the feed reader when I’m ready.
Long live RSS!
It’s about time we stopped rebooting social networks and rediscovered more flexible ways to create, share and read content online. Go read
Say it with me. Go on.
LONG LIVE RSS!
(*) not actually everyone, but all the cool kids anyway. Alright, just us nerds, but we loved it.
(**) not actually more civilised, but it was more decentralised
For years now at the Open Data Institute we’ve been working to increase access to data, to create a range of social and economic benefits across a range of sectors. While the details change across projects one of the more consistent aspects of our work and guidance has been to support data stewards in making data as open as possible, whilst ensuring that is clearly licensed.
Reference data, like addresses and other geospatial data, that underpins our national and global data infrastructure needs to be available under an open licence. If it’s not, which is the ongoing situation in the UK, then other data cannot be made as open as possible.
Data becomes more useful when it is linked with other data. When it comes to data, adding context adds value. It can also add risks, but more value can be created from linking data.
When data is published using bespoke or restrictive licences then it is harder to combine different datasets together, because there are often limitations in the licensing terms that restrict how data can be used and redistributed.
This means data needs to be licensed using common, consistent licences. Licences that work with a range of different types of data, collected and used by different communities across jurisdictions.
Incompatible licences create friction that can make it impossible to create useful products and services.
It’s well-reported that data scientists and other users spend huge amounts of time cleaning and tidying data because it’s messy and non-standardised. It’s probably less well-reported how many great ideas are simply shelved because of lack of access to data. Or are impossible because of issues with restrictive or incompatible data licences. Or are cancelled or simply needlessly expensive due to the need for legal consultations and drafting of data sharing agreements.
These are the hurdles you often need to overcome before you even get started with that messy data.
Here’s a real-world example of where the lack of open geospatial data in the UK, and ongoing incompatibilities between data licensing is getting in the way of useful work.
Introducing Active Places
Active Places is a dataset stewarded by Sport England. It provides a curated database of sporting facilities across England. It includes facilities provided by a range of organisations across the public, private and third-sectors. It’s designed to help support decision making about the provision of tens of thousands of sporting sites and facilities around the UK to drive investment and policy making.
The dataset is rich and includes a wide range of information from disabled access through to the length of ski slopes or the number of turns on a cycling track.
While Sport England are the data steward, the curation of the dataset is partly subcontracted to a data management firm and partly carried out collaboratively with the owners of those sites and facilities.
The dataset is published under a standard open licence, the Creative Commons Attribution 4.0 licence. So anyone can access, use and share the data so long as they acknowledge its source. Contributors to the dataset agree to this licence as part of registering to contribute to the site.
The dataset includes geospatial data, including the addresses and locations of individual sites. This data includes IP from Ordnance Survey and Royal Mail, which means they have a say over what happens to it. In order to release the data under an open licence, Sport England had to request an exemption from the Ordnance Survey to their default position, which is that data containing OS IP cannot be sublicensed. When granted an exemption, an organisation may publish their data under an open licence. In short, OS waive their rights over the geographic locations in the data.
The OS can’t, however waive any rights that Royal Mail has over the address data. In order to grant Sport England an exemption, the OS also had to seek permission from Royal Mail. The Sport England team were able to confirm this for me.
Unfortunately it’s not clear, without having checked, that this is actually the case. It’s not evident in the documentation of either Active Places or the OS exemption process. Is it clarifying all third-party rights a routine part of the exemption process or not?
So, to recap: Sport England has invested time in convincing Ordnance Survey to allow it to openly publish a rich dataset for the public good. A dataset in which geospatial data is clearly important, but is not the main feature of the dataset. The reference data is dictating how open the dataset can be and, as a result how much value can be created from it.
At the end of 2019, members of the OpenStreetmap community contacted Sport England to request permission to use the Active Places dataset.
If you’re not familiar with OpenStreetmap, then you should be. It’s an openly licensed map of the world maintained by a huge community of volunteers, humanitarian organisations, public and private sector businesses around the world.
The OpenStreetmap Foundation is the official steward of the dataset with the day to data curation and operations happening through its volunteer network. As a small not-for-profit, it has to be very cautious about legal issues relating to the data. It can’t afford to be sued. The community is careful to ensure that data that is imported or added into the database comes from openly licensed sources.
In March 2017, after a consultation with the Creative Commons, the OpenStreetmap Licence/Legal Working Group concluded that data published under the Creative Commons Attribution licence is not compatible with the licence used by OpenStreetmap which is called the Open Database Licence. They felt that some specific terms in the licence (and particularly in its 4.0 version) meant that they needed additional permission in order to include that data in OpenStreetmap.
Since then the OpenStreetmap community, has been contacting data stewards to ask them to sign an additional waiver that grants the OSM community explicit permission to use the data. This is exactly what open licensing of data is intended to avoid.
CC-BY is one of the most frequently used open data licences, so this isn’t a rare occurrence.
So, to recap, due to a perceived incompatibility between two of the most frequently used open data licences, the OpenStreetmap community and its supporters are spending time negotiating access to data that is already published under an open licence.
I am not a lawyer. So these are like, just my opinions. But while I understand why the OSM Licence Working Group needs to be cautious, it feels like they are being overly cautious. Then again, I’m not the one responsible for stewarding an increasingly important part of a global data infrastructure.
Another opinion is that perhaps the Microsoft legal team might be better deployed to solve the licence incompatibility issues. Instead they are now drafting their own new open data licences, which are compatible with CC-BY.
Active Places and OpenStreetmap
Still with me?
At the end of last year, members of the OpenStreetMap community contacted Sport England to ask them to sign a waiver so that they could use the Active Places data. Presumably to incorporate some of the data into the OSM database.
The Sport England data and legal teams then had to understand what they were being asked to do and why. And they asked for some independent advice, which is where I provided some support through our work with Sport England on the OpenActive programme.
The discussion included:
questions about why an additional waiver was actually necessary
the differences in how CC-BY and ODbL are designed to require data to remain open and accessible – CC-BY includes limitation on use of technical restrictions, which is allowed by the open definition, whilst ODbL adopts a principle of encouraging “parallel distribution”.
acceptable forms and methods of attribution
who, within an organisation like Sport England, might have responsibility to decide what acceptable attribution looked like
why the OSM community had come to its decisions
who actually had authority to sign-off on the proposed waiver
whether signing a waiver and granting a specific permission undermined Sport England’s goal to adopt standard open data practices and licences, and a consistent approach for every user
whether the OS exemption, which granted permission to SE to publish the dataset under an open licence, impacted any of the above
All reasonable questions from a team being asked to do something new.
Like a number of organisations asked to sign waiver in Australia, SE have not yet signed a waiver and may choose not to do so. Like all public sector organisations, SE are being cautious about taking risks.
The discussion has spilled out onto twitter. I’m writing this to provide some context and background to the discussion in that thread. I’m not criticising anyone as I think everyone is trying to come to a reasonable outcome.
As the twitter thread highlights, the OSM community are not just concerned about the CC-BY licence but also about the potential that additional third-party rights are lurking in the data. Clarifying that may require SE to share more details about how the address and location data in the dataset is collected, validated and normalised for the OSM community to be happy. But, as noted earlier in the blog, I’ve at least been able to determine the status of any third-party rights in the data. So perhaps this will help to move things further.
So, as a final recap, we have two organisations both aiming to publish and use data for the public good. But, because of complexities around derived data and licence compatibilities, data that might otherwise be used in new, innovative ways is instead going unused.
This is a situation that needs solving. It needs the UK government and Geospatial Commission to open up more geospatial data.
It needs the open data community to invest in resolving licence incompatibilities (and less in creating new licences) so that everyone benefits.
We also need to understand when licences are the appropriate means of governing how data is used and when norms, e.g. around attribution, can usefully shape how data is accessed, used and shared.
Until then these issues are going to continue to undermine the creation of value from open (geospatial) data.
It’s part of my new research notebook to help me collect and share notes on research papers and reports.
This paper explores the impact of data infrastructure, and in particular the use of identifiers and the design of databases, on the delivery of human (public) services. By reviewing the use of identifiers and data in service delivery to support homelessness and those affected by AIDS, the authors highlight a number of tensions between how the design of data infrastructure and the need to share data with funders and other agencies has an inevitable impact on frontline services.
For example, the need to evidence impact to funders requires the collection of additional personal, legal identifiers. Even when that information is not critical to the delivery of support.
The paper also explores the interplay between the well defined, unforgiving world of database design, and the messy nature of delivering services to individuals. Along the way the authors touch on aspects of identity, identification, and explore different types of identifiers and data collection practices.
The authors draw out a number of infrastructure problems and provide some design provocations for alternate approaches. The three main problems are the immutability of identifiers in database schema, the “hegemony of NOT NULL” (or the need for identification), and the demand for uniqueness across contexts.
Three reasons to read
Here’s three reasons why you might want to read this paper:
If, like me, you’re often advocating for use of consistent, open identifiers, then this paper provides a useful perspective of how this approach might create issues or unwanted side effects outside of the simpler world of reference data
If you’re designing digital public services then the design provocations around identifiers and approaches to identification are definitely worth reading. I think there’s some useful reflections about how we capture and manage personal information
If you’re a public policy person and advocating for consistent use of identifiers across agencies, then there’s some important considerations around the the policy, privacy and personal impacts of data collection in this paper
Three things I learned
Here’s three things that I learned from reading the paper.
In a section on “The Data Work of Human Services Provision“, the authors highlighted three aspects of frontline data collection which I found it useful to think about:
data compliance work – collecting data purely to support the needs of funders, which might be at odds with the needs of both the people being supported and the service delivery staff
data coordination work – which stems from the need to link and aggregate data across agencies and funders to provide coordinated support
data confidence work – the need to build a trusted relationship with people, at the front-line, in order to capture valid, useful data
Similarly, the authors tease out four reasons for capturing identifiers, each of which have different motivations, outcomes and approaches to identification:
counting clients – a basic need to monitor and evaluate service provision, identification here is only necessary to avoid duplicates when counting
developing longitudinal histories – e.g. identifying and tracking support given to a person over time can help service workers to develop understanding and improve support for individuals
as a means of accessing services – e.g. helping to identify eligibility for support
to coordinate service provision – e.g. sharing information about individuals with other agencies and services, which may also have different approaches to identification and use of identifiers
The design provocations around database design were helpful to highlight some alternate approaches to capturing personal information and the needs of the service vs that of the individual
Thoughts and impressions
As someone who has not been directly involved in the design of digital systems to support human services, I found the perspectives and insight shared in this paper really useful. If you’ve been working in this space for some time, then it may be less insightful.
Why don’t we have a better approach to managing personal information in databases? Are there solutions our there already?
Finally, the paper makes some pointed comments about the role of funders in data ecosystems. Funders are routinely collecting and aggregating data as part of evaluation studies, but this data might also help support service delivery if it were more accessible. It’s interesting to consider the balance between minimising unnecessary collection of data simply to support evaluation versus the potential role of funders as intermediaries that can provide additional support to charities, agencies or other service delivery organisations that may lack the time, funding and capability to do more with that data.