 My name is Yodit, for those of you that don't know me I'm a computer scientist. I've been programming since professionally probably about 15, 20 years. In early days I started off with machine learning systems and natural language processing, and the last 10 years I've gone into a middleware system. y gallwn iddynt i gyflawni ein bod yn mynd i gweithio ei fod yn ei wneud i gweithio bod hwn yn hydu llwyth gredig, ac mae'r gweithio'r gweithio dipyn o'n rhai ymddangos, byddwn i gweithio'r gweithio diall ar ni, ac mae'r gweithio ar y dynion oherwydd mae'n ehong o'r gweithio cyflwymygu o'n rhai sy'n ceisio. Rydyn ni'n ei wneud i gweithio i'r hyn o protein oherwydd hwyl modd i'r gweithio. Rydyn ni'n ddweud hynny я mae gen i weithio ifanc o Gymru, ond rwy'n gweithio'n ddweud y cyflwyddor y tro ddweud fwy modd o'r cyflwyddor o'r cyflwyddor a'i hyd i'r haf, dw i yr eich effaith bobl yn gwneud fydd bwysig yn gweld, ond yw'n gweithio nad oedd i'r cyflwyddor o'r cyflwyddor o'r cyflwyddor o'r cyflwyddor y tro ddweud. Wyddechrau'r cwcifer yn amlwg, mae eich cyd-rhyw arain os ymdwyll Cymru. Ond ydw i'r cyd-rhyw deddau i ddalau varietyllau i ddalau i ddalau ateilidau maen nhw chi'n meddwl ddtrwch cynnig hynny, ac os ti'n meddwl ddalau ddalau ddalau. Mae'r cyd-rhyw wedi ei ddylch am hwn o gwybod eludwadau o'r ddysgu gan hyn,ol sy'n bod ydych chi'n meddwl o'n cyd-rhyw komenol, ymgwyl yn y clywed, mae'n gwybod y sennedig pryd gan'r ymweld, a mae'n gweithio'n anghygrifio, mae'n gweithio'n gweithio yn cael ei ddechrau'n ddechrau'n dweud, ond mae'n cael ei ddweud o'n ffordd, hynny'n hwyl fathio, oherwydd, iddi'n credu y cyfnodd, oherwydd y gweithio'n gweithio'n cael ei ddechrau'n gweithio'n dweud o'ch cyfleoedd ddwy'n ddwy'n ddwy'n ddwy'n ddwy'n ddwy'n gweithio. Ac felly i ni'n cael ni'm rywbeth yr adeiladau open data i gyflwyno'r adeiladau. Ond mae'n meddwl am adeiladau open data, i Branching Data, ac mae'n meddwl am adeiladau open data. Yn dda'i gynhyrch, mae'n ddechrau Ryw, yn ddod o brofi�od dimwys, i ddod o'n raddau i ddod o'r arwag llawr o'r cyflwyno. I understood that very much like Open Source, there is clear licensing terms. This is a brief kind of summary but if you have a good kind of definitions that are in the Open Knowledge Foundation's website. They've done a lot of work on creating, I suppose, plain English definitions of what open data is and what it is not. So I do have a look at it or talk to me about it later. So why online I think is that I think Felly mae'n mynd yn bwysig, mae'n meddwl y cwestiynau ychydig o'ch gweithio i ddau ymddangos, rym ni'n ddim yn bwysig i ddau i ddau, ac mae'n ddweud i'r gyfrofiad, ac mae'n gwybod i'r gweld i'r gweld i ddau i ddau, ac mae'n ddau i ddau i ddau i ddau i ddau, a'r cyffredin iawn i ddau i ddau i'ch gweithio i ddau i ddau i gael. But I'm going to touch on now is what I say to people to convince them to open up their data. Why should I care if I live in a city and they're creating smart cities projects, Why the hell should I care about open data, find an organisation and I'm running a sensor network. Why the hell should I care about open data. We are not a charity. Yw'r cwmaint i'r cyfrifiadau fel core ble mae'r cwmaint o bwynt o'r cyfrifiadau mewn cyfrifiadau yn tuith dangos a'i ei ffordd o'r gyfrifiadau, yn eich ffordd o unwis. O'r cyfrifiadau i ni arbwyntio'r cyfrifiadau yna rhaid i'r gweld, felly rwy'n nhw'n i'r ffordd o'r cyfrifiadau, ac rhaid i'r cyfrifiadau, ychydig rai'r cyfrifiadau cyfrifiadau, eich gwoithymeid. Oeddwn wedi ychyrddion i gynnwys i wneud y byddwn i gynnwys i rwynt gwelltyn i gydweud y byddwn yn cyntafol yn ei Microfossy accordingly. Dwi chyn wedi'u hi fel amgylcheddau gwahanol, oedd dweud wir iawn i'r tyfn a wneud o'r cy estudiadu. Beth ar deall yn y cyfwil ynddau cyfleidio cyntaf oed i gynnwys iawn i gynnwys i gynnwys i gynnwys i gynnwys i rwynt i gynnwys i gynnwys ei gynnwys i gynnwys. a hynny'n cilio gael, wrth gwrs, mae'r cilio'r cyfrifio gyda'r cyfrifio yn ynnig. Rwy'n creu yw'r hynny'n cael ei hunain. Sut mae'r cyfrifio yn ynnig yn rhoi, na wnaeth yn ni. Mae'n meddwl i'r hoffi, yn y cyfrifio'r hoffi yn y dyfodol. Mae'n aelod o'r cyfrifio'r hoffi ar y gynhyrch ac ar y gynhyrch, Mae bwysig i maith diemniol, i ffyrdd i dda i'w rhesiwn i Siwydur yma. Felly os ydw'n rhesiwn i'r bywyd, i'n rhesiwn i deploym ales, i ffyrdd i ffrydd y bywyd, a'r bywyd ar y ddiw i ymweld, i ffyrdd i'r bywyd, i ffyrdd i'r bywyd, i eisiauwn i dda'i mewn i weld ond ac oes iddo i'r bywyd cael ei ddadig, i ddal. Fy ngysun, oes ydw i'r bywyd, bywyd yn y gallu bod yn ystod yn dddaw. Llywodraeth gyda rhoi, mae mae'n mynd i gael ei hardwyr. Mae'n mynd i gael ei yma, oherwydd mae'r ddechrau. Mae wedi gael cyhoedd gyda hwyl o gwasbeth gweithien a'r hynny. Mae hwn edrychwch. Mae'n nghymru a gilim ag oedd ei pawb oedd y cyfrif staff yn cael ei ddweud. Rwy'n fyddyn nhw. Mae'r ddweud yn fy mhly, mae o'r ddweud yn y cyfrif, yn yr ystyried o'r hyn, ond yn llwyddoedd. Rwy'n mynd i'w ddweud. publish it anywhere you like, but the data should be accessed for many reasons. We have all these systems coming in and the main thing that they should talk to each other. They should be interoperable not just in terms of schemas or particles. They should be interoperable in a sense that they should be just respite with APIs. You can grab some data, do something with it, enhance your applications, iddynt o'r afliadau ar y dyfodol Lloedig Lywodol ar falch. Roedd cyflym sydd i wneud yn ffordd hynny sydd yn ffordd bwyd, a hynny'n ffordd a llwyfod y cysylltion yn ddoch yn y rôl ac yn ymddangos sefydlient Gŵr. Felly ond o'r sefydlient, rwy'n golygiad, dyn ni'n rychwun cylifiadau cyflwml. Yn gyflym ond, os ydych chi'n cymryd oedd, ac mae hi'n gael gydag iawn i. The context you only get when you actually start combining many, many data sets and to do that that data has to be open. There is no way that people can pay for large amounts of these data sets. How do we make it so that these very interesting applications are built using many, many data sets. This is my colleague Boris' slide, and I love this kind of magic bit. That magic bit is really your value. What are you building that is so kind of specific to your application? If you're building kind of a smart harms project, you know, if the data outside of the house is invariably going to, or the factors outside the house is invariably going to impact on the factors inside the house, you know. A lot of building management companies are, for example, concerned about equality inside a building. So if you have kind of newer buildings that are kind of completely sealed, and CO2 levels go up in the afternoons, that makes people sleepy, there's also other equality factors that you want to sense for. But, you know, that building doesn't live in a silo. That building lives in a world with lots of environmental factors generated around it. So what we see and what we're good at, I suppose, is saying to people, combine all these datasets, get some kind of, get some better insight for your application. So I'm going to hit on two, I suppose, case studies, and maybe I'll hit on others. This is an interesting kind of example, a customer that deals with local authorities came to us probably about a year ago. They said, hey, you know, we want to install parking sensors in lots and lots of cities. They had won contracts with cities to install these parking sensors, and they said, how would you enable us to install less parking sensors? So where I live in London, there's 500,000 parking spaces. And it's just impossible, even for a street or anything like that, the cost of installing these parking sensors is really high. But the cost right now, probably today, is probably about 80 quid. But the installation, the kind of maintenance charges are usually 10 times the sensor cost. If that sensor goes down, somebody has to go and dig it up, or if it's glued on. But a circle engineer with a van has to go and do something to it. So the less sensors, the less kind of products you install, the better it is for the city. They pay less. The better it is in the long term because you have less kind of maintenance. So it's not, I mean, the, and I see like the data science, the graphs, the visualisations actually as a tiny percentage of the cost of the projects usually. Usually it's the kind of the large lion shares taken up with maintenance. Right? And they want to obviously kind of get the best return for their buck. And one of the interesting things is actually a lot of councils already have parking data. They might not have full parking data, but they have some parking data. So we weren't about trying to get, initially get some training data to see what kind of exploratory data analysis we could do. And I don't know if some of you have heard me tell this story, but it was nuts, frankly. I was kind of a few miles from that particular council. The data had to go through seven different companies, eight different companies to get to me. The council had no access to the data, even though it was a private parking data, they had no direct access to their own data that they paid for from their parking sensors. So it went through many, many systems. It went through SharePoint, which then I lost the will to live, to be honest. And I, you know, it went to the US several times. It crossed many, many geographic boundaries. And this is, this is installations that they had paid for. This is not kind of some third party's data. This is where they're data. And the crazy thing is, the council, this is Westminster council, the council had the parking sensors and obviously they couldn't get access to it. But TFL, other adjacent, adjacent kind of bodies couldn't get access to it. So there was no data sharing between the various entities which live kind of across the street from each other. And these entities, you know, Westminster deals with kind of parking on the street. TFL deals with footfall data and obviously transport data. But there was no clear way for these guys to kind of just talk, right? It just seems so obvious if you come from, well, our world really. So anyway, in the end we got the data. And what we were able to do is, and I won't go through the kind of analysis on this, but what we were able to show was we could say, well, if you took a particular street, you didn't actually, you only had to install 60% of sensors in order to get a 95% confidence of knowing if that street, if particular parking bays were taken up or not. And that was it. You just need 60% of sensors. So now they're starting to use all these algorithms to go and plan out their deployments in multiple cities. So it's not just in Westminster but actually in many, many cities. And they've spun it off into a company and they're being really successful with it. And simple things like data access, the ability to have for people to be able to kind of understand this information, do the planning, makes a huge impact. And the savings, when you're talking in parking terms, when city-wide installations is in millions. So there's another case study which is a project we did six months ago, maybe seven months ago. And this was a question is how, you know, there was the third runway debate was happening. The community felt that they weren't giving any input into the kind of, I suppose the discussions, or at least the input they were given was limited, they felt in their view. And they wanted some cool kind of facts to understand what the current air quality noise and other levels are, and what they wanted to be able to kind of predict what it would be. And I said, well, it's hard to predict what the impact would be, but let's at least kind of do the basics. Let's find out what the air quality is for your area. So we asked for volunteers. We asked for volunteers via Twitter. We got a small grant to just buy 25, I think, sensors. And we just asked for volunteers who would like to install these sensors with noise sensors in your back garden. We got over 70 volunteers. And what we tried to do was we kind of deployed them in a manner that was a round Heathrow, in a way that was kind of congregated in Jusser, and there was in a dispersed way kind of density, getting the densities is what I'm trying to get at. And it was very simple. It was probably the most basic project that we could do, but it was actually the first, and this is bizarre to me, it was the first data point that anyone could get around Heathrow air quality and noise. And the residents were ecstatic. The data that's published is open. You can still find the data. The sensors are offline now, but you can find the data for a six month period. Some PhDs, I think there was several, actually there were several projects. One set of PhDs did analysis on noise impact. They found that the noise was the decibel levels were consistently above the European regulations. Air quality was consistently above European regulations, and I should have linked to this, but if you kind of find me later I can send you the full analysis because they said there were a paper on it and everything else. The data started being used by the GLA, so the Greater London Authority said, well actually we don't have air quality data around Heathrow, can you give us some? Okay, it's open, take it, whatever. But surprisingly why don't you have air quality data around for Heathrow? When you're debating or making recommendations over such fundamental issues, it just again seems like it's a straightforward thing. The data and the issues, the data itself was discussed in Parliament, so now the Heathrow, the body has created an environmental consulting group, surprisingly just after this. So there was a bit of an uproar, Parliament raised it and the decision on the third runway was delayed and an environmental review body has been set up. So they're going to do a third party review. They haven't asked us to come back, but maybe I'll get a call one day. But no matter, it doesn't matter. Open data, freely accessible data, it has a real impact. But from this one thing we learnt was obviously we were up to that point a very much API driven company. If you wanted to get the data you'd go to the API, you'd get the data and we assumed you'd do something with it. It turns out that actually making it open, I think this is a realisation or maybe a growing up as a company that we had to do, is making it open isn't just enough. The majority of people that care about these data sets can't use an API. So it can be argued is it really open if the majority of people can't access it, if there's a technology barrier to access. The feedback was kind of harsh from these guys, like why does somebody else have to interpret the data for me? Why can't I just see it, understand it and move on? So that kicked off a new kind of sprint that we're on, that we're kind of finishing up now. So we said, well okay, open data is great, but the next thing is we need to make this data understandable. People need to be able to, if they're not technologists, they need to be able to understand the data. So this is a kind of a screenshot of a hair lab. I think Andrew is talking about this later, so I won't touch on what the project is, Andrew will explain it. But what we wanted to do is actually make a great effort to say we're going to map the data, we're going to put it on a map, you can search it in a kind of a sane way, but you'll also get at least some understanding. I'm not going to tell you what the air quality baseline should be. As a team of six, we're not going to be able to tell you if air quality is really bad in your area, but at least we'll give you some idea. At least we can give you some idea, and you can put it next to other air quality stats and just compare and kind of make some judgement. You guys might have some thoughts on this. I'd love to hear what you think, because I think one of the things about open source, which I love again, open data, is that we care, we make a lot of effort, most of us probably take a massive financial hit in order to work on open source and open data projects, but the effort is that we need to make other people care and make it useful for other people, and hopefully that kind of gives you a little bit of a quarter action for your own projects, is to describe them cleanly, to look at them from the point of view of a person that wouldn't necessarily understand what you're in about, and how can you kind of communicate that message and communicate why it's so important, and make them kind of get the passion to participate. I mean, open sensors I think has been, again, this is the transition. Now we're kind of processing 25 million daily events of data. We have kind of people publishing lots and lots of data, but we had to really make the effort to tell the story in a way that could excite people that are not necessarily just kind of IoT enthusiasts. So I encourage you to kind of think about that message a little bit. Just to, I don't know, a few people asked me how it works. If you're interested, ask me later, but like, you know, sensor data goes in, private open data mashes up together, and you get, you use the API, and now the kind of the analytics is coming out over the next two weeks. So if you want to know more, we have some documentation, we have an IoT university that you can subscribe to, and yeah, any questions, that's me.