All right. Hello, everyone still here So we're going to talk about real life at work and I'll first like to Have our first panel, which is the product managers to introduce themselves Okay, let's start Okay. Hey guys. My name is Doro. I'm a product manager at Zalora right now my background is actually business studies, but my first job was as a video game tester and That was a really great job actually a great culture at that place as well And that's why I stayed on in that company to become a project manager there and that's where I got exposed to software development the first time and I was talking to a friend of mine at the time who was a product manager and that was a job that I had never heard of And when we exchanged a bit what our roles were I realized that her role actually addressed a lot of the pain points I felt in my own role so she had a lot more to Influence on the product and to choose the problems that she wanted to solve and how she wanted to solve them And I found that really really intriguing Because my company was kind of corporate and also being a tester. You're very downstream, right quality assurance It's kind of the last thing before go live So yeah, I got really curious about it And I looked for openings and I applied and became a product manager and here I am Still at the same company actually after almost five years Thanks, Doro. I'm E1. I'm a product manager at Grapp But how I first got into the product manager role was actually from Shopee So I joined as actually regional ops and I was a lot more involved in risk Management because actually honestly, I would have to say that it has nothing to do with what I studied One thing or another I wanted better opportunities I wanted to go in tech as a non tech person. So I found a position and So happened that when I was working in that team my product manager whom I worked with Was going to go advance his studies like to get an MBA. So he thought that I had all the qualities that Could replace Basically could could do what he was doing. So that's how I got, you know a confidence boost and went for that role And here I am today Thank you. Hi, I'm E-ling from Honest V The most recent project that I worked on was habitat by Honest V Some of y'all might have visited it before and how I got into product management was that I found my way to Silicon Valley and I fell in love with product management. I've never looked back since then So how I found my way to Silicon Valley is a program by NUS to know where they actually post people to You know, take entrepreneurship classes at Stanford and you know, I also get to work at the start up there So that was how I found my way towards product management Yeah, thank you. So let me start Asking the next question for the product managers is you know, how does What does it mean to be a product manager in your company? What do you actually do?  What should they do? So product management is a role that kind of sits at the intersection of technology business and design and Often you come in just having, you know, one of those areas a bit stronger or really a strength and the others still need to be developed When you are in this role or what does it mean to be in at the intersection of it, right? Is that you need to understand those business problems and try to solve them using technology and design? I think that's probably summarized very much in a nutshell Maybe for the second part of the question Yeah, so to build on those answer. So I think you mentioned that you are the intersection as a PM between Tag between your business teams between your country operations teams and a very very important skill that is absolutely necessary Is your people skill? people, communications Basically because as a PM you are the owner of the product you are responsible for how the product performs So it is absolutely necessary for you to be able to adapt and talk to different kinds of people in their language To make sure that the product gets, you know, shipped successfully and performs as intended Yeah, so I think communications is for sure always the quality I look for in any PM candidates So some characteristics that I look for in hiring PM is having an analytical girl mindset and being a problem solver and The third is, you know, just doing what it takes to get shit done So literally, you know, sometimes they are just fighting fires every day and sometimes, you know, you're saying that oh You know, my customers are really loving the product and Day to day it really varies for a product manager and that's why it's really exciting Yeah, thank you. And this panel is really for you guys Unfortunately, we only have 10 minutes for each Role we have now about two questions for the audience. Do you have any question? Raise your hand up high. Yeah, we have one over there. Is there a mic? Yeah, go ahead Thank you for the question. So To repeat that her question was what's the biggest challenge as a PM That you think you face and you don't think anyone else in the industry in the tech industry phase And you know anyone like to share about that? Maybe I can answer this question first So I think I some of the speakers have said, you know, product managers is at the intersection of business You know tech design and that's where there's a lot of gray areas, right? So, you know, where does the line stop? Where do you draw the line? So sometimes one of the challenges is really finding the capacity to focus on The area that you think will have the biggest impact and really getting alignment from all the different teams because All the stakeholders have their different priorities, you know, the data teams once a space to deliver their data Recommendations, you know, and then the design team wants to make their UI UX, you know, improvements So then how do you prioritize? That's the biggest challenge I would say Yeah, totally agree Good summary also and I think maybe communication that you mentioned was, you know an important skill for us I guess to bring everyone on the same page speak everyone's language also to a certain extent You know whether you talk to UX designers and you also need to know your customer Of course as a product manager or your talk tech and you want to at least know high level what you're talking about I think the the challenges. Yeah to communicate well And Actually, I think there are plenty of product management materials online You can definitely go on medium content Asia a ton of books that you can start reading as well So I think a lot of things that actually we're covering now is quite Easy to find online and accessible to everyone but one thing that I want to say about the challenges question They were talking about Because I also just had a couple of screening calls yesterday. So I'm like still fresh from that So I think especially you need to service agent that companies There tends to be a lot of changes all the time be it from business be it from regulations You don't see them coming and it when it come you have to handle it You have to handle any curveball that is thrown at you because for example, you could have a product really developing 70% And then something comes and say hey actually we are doing this You know, this is like this thing just came out yesterday. It's all wrong So I think the challenge actually especially for a PM in Southeast Asia is to be able to handle curveballs really well and That takes I think a fair amount of patience skills obviously communications, but of also a lot of Managing expectations with different kinds of people who definitely don't immerse themselves in product management every day So they also don't or maybe not necessarily familiar with the things that you are facing So as a product manager, you kind of have to be like that all-rounder person To make sure that things Suffer as little as possible due to any of these changes. Thank you so much I think I will move on to the next Section and if you have any question when they get off the stage make sure you grab them All right, so next up we have the web developers Well, I think a lot of you may be able to imagine how that looks like But I think I will still like them to introduce about you know How a day for them is like and what actually excite them in the day and in their role in in this web development role So I'm Nicole. I'm from top works. I'm a software engineer Typical gay will be we start with a scan up So we scan around and we go one round talking about our updates progress and any brokers I think it's the most vital round in the day because you get to know if someone needs help or you know Something that someone else doesn't and also as a team you need to help each other in order to make the product So it's not just about your personal progress. How fast you can finish a story cut So in order to make a product everyone has to be on the same page. Also, I think scanner is really important And it should not just be about I get this I get that but more about like do you need help? So you need to be a bit proactive in Asking and also offering help in order to make the team great Hi, I'm feel from the circus life and as Nicole see the daily stand-up is important for the everyday of the Software engineer life. I agreed on that. So how stand-up have our walk So in the stand-up usually we talk about three things yesterday What had we done today what we are doing and then what will be the blocker of the today? So that every everyone in the stand-up will know that what will be the blocker being a senior how to How to clear the blocker so that the teams will keep moving because we have we are promising for the deliverable outcomes to the PM So from the PM side, they will also want to know that is there any blocker in the team? Well, they affect to the deliverable outcome and then also at the start of the everyday walks We will be looking inside is the blocker if it is a blocker. We will contact to the PM Hey, there is a blocker how to resolve it and after that we will keep progressing on the team Performance ending at the end of the spin. We will achieve our deliverable outcomes Thank you So well, I think for what you know what you require to become a developer or a software engineer Maybe very different from any other role in the tech industry. So And it always seems like there is a very high level of entry That you you need to achieve. So what would be your advice to anyone who has no background and who want to become someone like you You know, what do you think is the hiring process right now? What would be your advice? So a few questions yoga entry process. It actually depend on your own I would say depend on your background and where you come from. So if you're scared really scarring computer science I would say the entry level will not be get hard, but if your career if you're doing a career switch like you went to Like coding Academy and all sorts of get right the inertia would be higher because you're Entering a totally different industry. So there would be more difficult If the CV really matter So in the hiring process, I believe most of the tech company now proceed with a code assignment first So your CV in the Senate matters, but not as much as I would say a traditional job now I guess so you do have to submit your coding assignment It needs to work and also it needs to look good and reusable in order for us to consider you for the next stage So I would say go and get too bogged about your years of experience and CV and just try it out Try to code assignment even if you don't think you are going to get a job because it's a very good practice Yes from my point. So to become a software engineer, we need two types of skill One is called a hard skill another one is called soft scale. So what is hard scale? So that is what we can learn from the university or any kind of the online courses. There will be the hard scale So that is the fundamental so we need the hard scale and another one is what is the soft scale soft scale is a kind of Problem-solving and analytical thinking being a software engineer You are facing to the system who is a really using and there will be a Complexity over there if there is a complexity come at how you will be solving it. That is an analytical skill So that kind of analytical skill is you can grow up along the way in your career So first of all first we have to concentrate on how we can acquire the hard scale So how to best start learning the programming first. So when you can start Doing the simple programming and based on the what is your analytical skill? You can improve yourself to resolve in the Complexity complex complex problem So I might say that to visit as hard and solve is that must have for the software engineer All right. I guess to summarize if you have no experience, but you are learning The CV don't really matter. There's going to be an assignment. So, you know a graduate of computer science And you who has just learned something from cosera you guys are going to go through the same process So don't be afraid if you think that your you know college certificate doesn't count So now I have one more question that I can get the floor to ask Is there anyone who's interested in web development or software engineering who like to ask any question? Great question. So the the question was what is the language that I should start with there is so many out there Okay, so in order your question. So from my side, there is no language preference So first in first if you know the pseudo language, let's see how to write a simple program. That will be a good start So because every programming language we start with the variable Declaration and after the for loop this kind of thing every programming language is the same just a matter of the And then send us different. So just get the visit knowledge of the how to write a simple program and after that that will be your first start So I would say that if you're just scouting out, maybe try a course online Go for the one get teach you how to build a website from scratch making from all the way from planning design Database modeling and such so you really get a few of how the process work and then decide for yourself Do you really like doing this and once you are you acquire the basic skills like what she said, then you move on to Strengthen your basic skill I would always say go and look at the language because there's just too many new tech and language student But most likely go for like hugging your basic like Understand what is a database understand what how can you make something faster? So the theories behind all the language are the same So just apply them as such go and get to focus by the new language because if you keep switching languages to learn You're not going to get very far in the journey. I would say yeah Thank you All right So moving on to our last panel and again if you have more questions once they get off the stage make sure you grab them We have our last panel the the data scientists and I think I would like to start off with the first question to You know tell us more about what you think is unique about data scientists There are many people out there who are working with data in their own ways It could be working, you know with data in Excel. They could be writing sequel, you know They could be called data analysts. So what's unique about data scientists? Okay, probably I do a self introduction first. I'm Roshi from shopping So currently I'm a data scientist focusing on recommendation system in shopping. So regarding the question. What's Unique about data scientists as compared to DA or BA I will say that for DA and BA they are they are finding facts about data But for data scientists, we need to deal with uncertainties Maybe I give a real real life example So for the recommendation system that I've been doing in shopping is like when you land on the product page We need to give a model which we'll recommend related products to you But it's a very like uncertain topic because we can think of different strategies to recommend a product It can be like the items that you recently view so that you will be more interested in it Then it can also be that the products like they say you are viewing a dress And then we can recommend for the high-heeled shoes that matches this dress or we can recommend for the cosmetics Then for another strategy, it can be like we recommend similar products. So for for doing the modeling and The engineering in data science you need to deal with this uncertainty Then maybe I go to DA in in shopping for an example So after we do this recommendation model our DA team will help us to Do the visualization and to compute the metrics about how the more or how good the model performance is So most of the time the metric is well defined So they are basically coming out with algorithms to find how to Most efficiently find the metrics that we want and how can we do an efficient visualization About the model tracking. So that's my thoughts about the difference between DA and DS Hi, hi again. I was part of the previous panel. So just a brief introduction. So I'm Janet So I'm leading the big data and analytics team for Oracle in ASEAN So I think what differentiates or what makes it special, right? So on a day-to-day basis is that you've probably heard about the term like data science is the sexiest job One of the sexiest jobs right now, right? So I think it's really, you know Being in a data science is data science field is more than just the technology skills and and all the You know the tools that you use and the programming and all but it's really about, you know Going and looking deep inside you like what is that vision that you are trying to achieve and what is that output? You know, so if you have I've just probably Just a little bit of sharing about myself, right? So I have this grand vision of you know Being when I was a kid of being able to save lives and contribute to society But unfortunately for me I wanted to become a doctor, but I was afraid of blood So I think I think faster than probably the patient. So there's really no chance for me to become a doctor So I thought like lucky for me. I have this love for numbers, right? And I thought that if I go to the engineering side, then I will have another means of saving lives So that vision was in me throughout, right? So and I thought that being in this data science field with all the advancement in technology, machine learning, artificial intelligence You could do a lot with that good vision inside your heart. You could do into health care You could do maybe even improving Customer experience and all these various industries, right? So again, I think in data science more than the technology itself It's really that grand vision within you of what you want to do and that output that makes it really exciting And once you have all these pieces together I think you know regardless of how difficult writing algorithms are Coding dealing with data cleaning data all these I think with that passion in heart It makes it very Not very easy but easier for you to overcome. So I think that's what makes it special. Yeah Hi, my name is Yan and I'm a data scientist in grab So I'm in the economics team and my team focuses on things like pricing and demand and supply positioning So I think To be a data scientist one makes it special is that other than being good with the data and analysis You also have to be good in modeling As well as formulating a problem such that it can be eventually incorporated into the product or the business And in addition, you probably have to have also have a knowledge on reading pipelines as well as data architecture So it's more of like a full stack thing But on the other hand for data analyst analyst, I think they're very good at Understanding the data and they know about the nuances of the data So whenever I need to look at a new table and if I have questions that actually might go to person And I think in general, they are very good at visualizing and presenting the data to the business so that business users will be able to understand the data and Digest it in bite-sized pieces Thank you. So looks like the business or the data analyst works closer to the business and the data scientists, you know Walk out with you know, I'm certainty and I'm pretty unpredictable stuff, right? So well the next question that I would be curious about is what could be the bigger challenge You think in your role as a data scientist And with that what kind of skill set for anyone who want to work with data who want to come into the data world What kind of skill set do you think they should acquire? To get into this I think the biggest challenge for me is actually customer education Because I find most of my customer there like two extremes one one extreme is they think that AI can do everything So actually we are not the God then it's it's it's very likely that we explore a model for one or two months But it doesn't work. So there is definitely disappointment Then the other extreme is that people think that we are just API users. So They think that it's very similar to software engineering Then so we just basically call API then we are able to do model very quickly if they give us the requirement So I will say that communication is very important and customer education is very important for data science It's not just about technical Maybe just to add I think open-mindedness is one of the very important thing that you you need to Look into data science so so she mentioned that education is one because people tend to think that we are doing magic, right? So everything we can solve everything but as you go deep into the the act of it Right of doing data science and all you will realize that you know every data set is different every industry is different Basically, it's everything is unique So we'd have you need to have that critical thinking and open-mindedness to go into the data science to have that kind of strong will to do it Yeah For me, I think the biggest challenge is to keep learning because there's actually a lot of new technologies coming out every day So you have to actually read for example things website-side medium or our research papers to keep yourself Abraised with the new technologies Thank you Unfortunately, we don't have time but since I'm holding the mic Anyone have a burning question you want to ask the data scientists on stage No one. Yes one question. So I will repeat the question. What's the difference between? Well in definition, what's the difference between data scientists and data engineer? They're right. Yeah, I think it's company specific. So probably like I started with Sharpie first So for us the data engineering, they are doing the data warehouse like maybe their user behavior tables and standard tables every day So they are to basically doing the data aggregation daily then for us when a new model comes in We also need to do data engineering is basically we extract data from those Standard tables then we need to do the feature engineering It which is basically to do the data aggregation pipeline to get the feature that we want in order to go for production So I will say there's overlap between these two teams then Because my previous working experience like the structure is a bit different. So even for the products For the data science team the data engineering pipeline is Done fully by the data engineers. So I will say it's companies of a specific. Yeah If I may just add to that I think the whole data science thinking has now evolved to include various aspects here So one business is one being having some skills in terms of the computer like programming and all and of course the math and statistics So there's actually a fusion of these skills together and at It worked with the customers and all the expectation is not so much now in silos, right? So if you are in the data science field, you are expected to basically understand the end-to-end process of understanding even identifying what data sources you need to look into Given an understanding of the business context, right? So that's a very important thing that you need to understand what business problems you're trying to address And once that's clear then you have to understand each step, right? So from a data engineering data management data handling cleansing Transformation to building the models all the way to communicating the results visualizing it and communicating the results and going back To why it didn't work. So that whole process. I think slowly slowly that is becoming the trend. Yeah So my understanding is that data engineers that generally very good at building ETLs and pipelines and they're good at architecture So I'll just give you an example. So we work quite closely with The data engineers and data analysts. So for example, we want to production lies a new table So for example, I'm grab food is a relatively new business for us And so when we are trying to design the scheme of the tables, we actually work very closely with the data engineers So they can understand our needs and how we want to use the data. Yeah Thank you. All right. So we have come to the end of real life at work While they are on stage Anyone have question keeping them on stage you can just come forward Have a good lunch and I'll see you in the afternoon workshop


Hi everyone, welcome to our pre-owned vehicles of the week. I'm Gabrielle and I'm here at Oxmer Toyota. You guys, I have three certified Toyotas that I would like to go over some details on. With that certification, we're going to have the 7-year 100,000 mile powertrain limited warranty. Let's go ahead and start with this one right here. Our first vehicle is a 2022 Toyota RAV4 Hybrid XLE Premium. The exterior color is in Blizzard Pearl. We are going to have alloy wheels. There's a powered lip skates. This is all-wheel drive. We're going to have blind spot monitoring, backup camera. There is a sunroof and we're going to have Bluetooth. Now the interior is in a nutmeg soft text. The mileage is $26,582. Our price is $38,995. Our second vehicle is a 2023 Toyota RAV4 Adventure. The exterior color is in a ruby flair pearl. There is alloy wheels. This is going to be all-wheel drive, blind spot monitoring. There's a Bluetooth backup camera. We're going to have six speakers and there's a sunroof. Now that interior is in a mocha. Mileage is $6,472 and the price is $41,995. Our last vehicle is a 2021 Toyota Highlander XSE. The exterior color is in a midnight black metallic. We are going to have alloy wheels. There is a powered lift gate. We're going to have a sunroof, blind spot monitoring. There is also a backup camera. The interior is in the cockpit red leather. Mileage is $53,790. The price is $39,995. If you all have any questions, feel free to let us know. All of our contact info is listed right here. Thanks for watching guys and I'll see you all soon. Bye.


I'm Jadon Antony, I'm Namdy Offerbo, and this is getting to know... I forgot the words, I completely forgot the words. What's with the pause? I forgot the words. I'm Jadon Antony, and I'm Namdy Offerbo, and this is getting to know... I'm getting five out of five. This is me, I know. Where you going? Let's get started. What place was I born? Wellesden, Vauxhall, or Hackney. Give me that one, give me that one. Wellesden, Vauxhall, or Hackney. You see what he's done, he's never told me this one. He's never told me this one. I'm just trying to think. You see, now you feel free in there on purpose.  Hackney, it has to be Hackney. It has to be Hackney. Yeah, it is. What's that one out of one?  This is all me. Up to the next, up to the next. Who is the most famous person I've met? Thierry O'Reilly, Orson Wenger, or Danny Rhodes. See, he has a funny picture with Danny Rhodes. His head's crazy. His head shape. I know he's met Orson Wenger before, and I know he's been pushed by Thierry O'Reilly because he doesn't stop telling me about that. It has to be Thierry O'Reilly. That's the one. Unfortunately, yes. What is my favourite ever film? Sure Shack Redemption. High School Musical. Or Casino Royale. It's easy. If you don't get this, I don't know. You see him. He's a big softy. High School Musical. Hundred percent. Correct. I'll tell you, Joyce. It's too easy. What month was I born in? September, November, December. Once again, easy. Too easy. It's too bad that it is December the 1st. December the 1st. What is my favourite ever football goal? Cristiano Ronaldo, a free kick against Portsmouth. Thierry O'Reilly's flick and volley versus Manny Niles. Dennis Beckham's touch and finishing. I'm going to go over to Ronaldo because he really thinks he can do a knuckleball. But, I haven't seen him do it in a while. That's his favourite goal. Hundred percent. Five out of five. Guys! Guys! He's set the tone. Name my former clubs. Not included in here. Former clubs. Top the level. Easy. Onto the next. He's part of Easy. Who is my footballing idol? Steven Gerrard, Patrick Vieira, or Jack Wilsham? Tick. Tick. Can you challenge him? Patrick Vieira. Easy. I started the start of life. Can I change my answer? Five out of five. Watch. Five out of five. Where was I born? Hackney. The voxel. Or Louisian? I'm going to go Louisian. Easy. Why are you going Louisian? Because I know you left the back of my hand. Louisian. You still live there. Okay. What is my favourite food? Spaghetti bolognese. Jolot fries. Or pizza. Tick tock. I know you get this one. The easy answer is Jolot fries. But that's not it. It's not. It can't be. It's pizza. I'm going to go pizza. Four out of four. I promise you. You will not get this answer. I promise you. When I made my debut for Nigeria under 20s. What was my shirt number? Number four. Number 15. Or number 20. You've got that picture. You walk around. What did you mean? It's either 20 or 50. It's not four. I remember. I wasn't happy about the number. I'm going to go. 20 is a good number. So I'm going to go 15. Easy. Five out of five. Thank you. Don't tell him. Five out of five. Sometimes. Not going to draw. Easy one.


My name is Mark. I am a consultant with Second Quadrant and have been a minor contributor to Postgres over the years. Today I'm going to go over some performance-related topics from over the past year and specifically the patches that have been committed to the 9.6 branch, a couple of extensions that are available now and take a little segue into looking at some mainframe performance and pose a few paths that Postgres may take after 9.6 is released at the beginning of whatever the next version may be, 9.7 or whatnot. So the first patch I wanted to mention is how many people have large tables, more than a terabyte? How many of you fear when that auto vacuum comes around? So this freeze is one of the things I wanted to talk about and I wanted to talk about how many people have large tables, more than a terabyte? How many of you fear when that auto vacuum comes around? This freeze is patched by Sawada Masahiko at NTT. What this patch does is it automatically marks rows that have, well mark rows frozen so when that vacuum process comes around it'll, if the majority of that table hasn't been changed it'll fly through that and hopefully alleviate any tension that you may have when that process comes around. The next patch that's worth mentioning is this atomic pin and unpinning of buffers in a shared buffer pool. The purpose of this is to improve read scalability and has been authored primarily by someone who might have to help me with my pronunciation. Uri Zerlov with Postgres Professional. What this patch does is that it starts to use, it uses in place of spin locks, the atomic instructions to move the buffers or mark the pin and pin those buffers in a shared buffer pool. There's a chart that Alexander Korotov posted also with Postgres professionals on the hackers list showing the performance of how 9.6 looked before the patches were applied. So that bottom graph down there, this is from a four socket Intel server with 18 cores per processor, 144 total threads. Anyone have that many cores on their system? No? One person? All right. Oh, well, it may be worth noting that this patch was tested on Intel and Power and I don't know if anyone's tried it on Spark yet. So with the spin locks, that bottom line down there on the 9.6 branch before this patch was applied, you see it peak at about 70 clients running against the system and afterwards drops fairly dramatically, in my opinion. So when the three colored lines are just different variations of the patch over its development and with these removing those spin locks and using these atomic instructions, this patch allows the throughput to peak around 130 clients and afterwards, while it doesn't really get any better, it does level off and does not drop as dramatically as before. So this patch allows about twice as many clients in a PG bench test to connect and push the system closer to its limits and just looking at the peak performance, the throughput peaks, the peak of the throughput doubles with these changes to the shared buffers. How many people saw Robert Haas talk about parallelism earlier today? All right. So I'm really not going to try to repeat too much. Robert Ahmet and Harry Babu, David, among many other people who have helped get this new functionality into Postgres. What I want to do is just kind of illustrate in a slightly different way that how you still need to be careful with this advanced parallelism or the addition of the parallelism, that it's not going to always make everything better. So what I have is a... Oh, now I forgot who... Sorry, someone posted these numbers on the hackers web, so what they were were 100 gigabyte TPC-H database. One of the tables, the line item table, is 80 gigabytes in size is going to have 60 million... 60 million? 600 million rows in it. And just looking at the basic parallel seek scan, just because it simplifies the example, what we're going to do is just take a quick look at the behavior of selecting rows from a table and varying the selectivity. What happens when we select all the rows, selecting 25% of the rows and selecting 12% of the rows. So, you know, pretty well contrived. We'll just select one of the key columns and do a modulo on the keys to see if we want that row or not. So in this contrived example, the planner has been modified such that it will always try to do a parallel seek scan just so we can look at the behavior. And if that red line on top, what that's showing is single process, selecting all the rows, going to take, what, 250 seconds. And you had a second process to help divide up that work. So you end up doubling your response time for that query. And then selecting 25% of the rows, you had a second process. Sure, it speeds it up. About 25% start adding more processes to scan that table even though you're only selecting 25% of the rows when response time starts going up. So the purpose is just to illustrate that it's important that we get the planning right and between how many rows the planner thinks you're going after and how many processes it thinks it should be using to perform well. So very contrived, but as this thing keeps improving, probably still need to be careful on when it's actually in use. Now this chart shows a little more about how well the parallel aggregation works. It's the same table as before with the 600 million rows, 80 gigabytes of data. Using query one of the TPCH benchmark, which is doing a sum and an average over those 600 million rows to produce only four rows of output. So in this chart, the time axis is on a log scale so we can see the behavior a little bit better. The blue line that's going through the middle, I guess, is the actual response time of running query one as we increase the number of back ends that are performing the parallelism across the x-axis. So the top line, which is supposed to be some kind of yellow-orange, I think, is a percentage on the right side of how far the efficiency of the parallelism, in other words, if we look at the response time of when one process is performing this aggregation, is two processes cutting the time in half or not. So what we see is that with the parallel aggregation of these sum and aggregates, the degree of parallelism scales up to about 10, fairly close to 10, meaning that the response time with 10 processes is about one tenth of the time it took with one process. And then the next, looking at increasing the 30 parallel processes, the efficiency was measured at about a 20% loss of efficiency, still 25 times faster than how long that aggregation took with a single process. So now, just to mention a couple of extensions that may be worth looking at. Anyone use a lot of numerics in their database? No? A couple of people? Are you happy with how fast it is to do calculations with numerics? So just another illustration of that line item table used previously with 100 gigabytes of data, 600 million rows. There's four of these columns in this line item table of the quantities in the line item and the extended price of the discount and tax for a line item. If all four of these columns were numerics, and yeah, so if comparing what happens with this same query one that's doing an average and a sum across these 600 million rows. Sorry. Oh, yeah. Sorry about that. To describe that fixed decimal extension a little bit more, what this is is it's using 64-bit integers to provide a predefined precision decimal similar to numeric where numeric is a selectable precision data type. And an important thing to note is that with the fixed, implementing the fixed decimal this way, calculations may be truncated as opposed to rounded in case those were issues with your normal use with numerics. So again, just these four columns out of 16 columns in this 600 million row table will compare how the aggregation goes between having them as numerics compared to fixed decimal. And I feel like I have a slide that disappeared. Oh, oh, I see. Sorry. The thing on the laptop is a little too small. So with that 22, there's a set of 22 queries in part of that power test. That chart on the left is query one that I was describing earlier. So in a query like that, using a fixed decimal, this fixed decimal data type instead of numeric increased the response time, made it twice as fast. And in three other, well, a total of four queries out of the TPCH benchmark, which used a fair number of numerics in the queries, also improved the performance fairly significantly because of the use of aggregates in those particular queries. Some increased as much as three times, others one and a half times. So now I'll go over a little bit of replication extension. How many folks are using Sloney or Vondies? No one? Streaming replication? How about that? All right. How many of you guys using streaming replication wish you could break it down a little bit more, not replicate the entire database? And do you guys choose not to use the Vondies or Sloney because it doesn't meet your performance needs? No? Convocation? All right. So Thomas Vondra also had second quadrant. He did a little test of this extension that that second quadrant has worked on called Pgeological. Anyone heard kept up with that? All right. So he took it out for a spin. Using Postgres 9.5, he took a look at how well, well, took a measurement of how well PG Bench ran using streaming replication with Pgeological taking a look at Sloney and Vondies. And just to give you an idea of the system size, he was using an I2-4XLR JW instance, four virtual CPUs, four SSDs attached to the instance, 122 gigs around. And this is what he saw. The blue line on the top was what he saw with, just using streaming replication. So with one client, one client in the PG Bench test, everyone's relatively close and not a huge difference between using streaming replication, Vondies, and Pgeological. Just setting up two clients. Everyone's scaling up okay. But then after two clients, Vondies, yes, Vondies has passed its peak. It's that green line on the very bottom. It doesn't seem to be doing any better as we're increasing the number of clients of PG Bench against the masternode. Then the remaining guys, Pgeological and Sloney, are keeping pace with streaming replication up to four clients. At which point, Sloney starts to fall off a little bit and take a little bit of a dive towards how well Vondies is doing at that point. At eight clients with PG Bench, Pgeological starts to not keep pace, though, but it will continue to keep up a little bit until it reaches about 16 clients, at which Pgeological flattens out. So at 16 clients, it is interesting. We're levels out at that point at 16 clients. What that sort of shows us is that Pgeological has a 15% overhead from streaming replication to keep up with the activity on the drive of PG Bench. The limiter? Oh, oh. I don't, but I think it's fair to say that the overhead of the logical decoding that's used underneath Pgeological is very likely around that. Yes, so PG Bench is running against the measure and these numbers are the results that PG Bench are returning. But for, right, right, right. So I don't think I have a good answer. I don't think I know the point. Yes, right, to have a right, right, right. I'm not sure where the code pass is exactly, but I actually wonder if it, I probably really shouldn't guess. I probably should just ask someone to tell me. But I was always under the impression it was part of the streaming part that before it streamed, it's decoded in sense. At least. Right. Alright, so anyone ever had any interest in mainframe? Anyone have a mainframe? No? Anyone ever had an interest in mainframes? No. Yeah? So then, has anyone seen the pricing of a mainframe? Alright. So shortly before the 9.5 release, I took a look at some OLTP results against Z13, which I think is what the current Z13 platform came out. There's this 1x1 line that has come out, some of which is based on the Z13. Ran some tests on the mainframe, took a look at how well it did to a popular competitor. And well, because this was shortly before 9.5 was released, we actually did these numbers on 9.4, but I hope it would be encouraging to also think that what we see here is going to be repeated on what we would see with 9.5 and 9.6. So a couple of basic details. These Z13 mainframes are clocked at 5 GHz. This particular one had 8 cores and can be threaded up to 16, 64 GB around. The competitive system was chosen to also have 8 cores, 16 threads, although these processors were clocked at 2.4 GHz, also with 64 GB around. So this first test is the result of PG Bench, doing only a read-only test. So what this is showing here is that bottom line is the transaction performance of the competitive system without threading enabled. Next line up, threading is enabled, and we can see that there is a little bit of an increase in transaction throughput. The third line up is what the mainframe can do without threading enabled and then the top line showing that we can still increase throughput on the system by enabling threading on the mainframe. So comparing the mainframe in this read-only test to its common competitor is showing that it's able to produce twice the throughput. So similarly, using PG Bench to do a simple write-only test, what we see on the bottom is that with the competitor system, the reason why you only see three lines is because the two lines overlapping on the bottom is showing that the competitor system threading enabled or not enabled is not showing much improvement for a simple write-only test with PG Bench. Whereas the mainframe above it, the next line up is the throughput we're able to get without threading and then showing that we can get a little more throughput with threading. Yes. Yeah, usually legally related. It is not going to be necessarily condoned by both parties. But you're welcome to guess. Yeah, so the mainframe is showing what did I write on there. Still, even with the write-only test, the mainframe is able to get twice the throughput as its competitor. This DBT2 test is a more complicated OLTP type test and it's based on the TPCC benchmark if anyone's familiar with that. And what we're trying to show here is the scalability of a more complex OLTP workload. So as we're increasing the number of clients against these two systems, what it's showing that the mainframe is able to more or less keep pace of 1.7 times the throughput, almost twice the throughput still of its competitor as the number of clients hitting the system increases from 10 all the way up to 80. Sorry, a couple slides got out over here. So at this point I wanted to point out a couple of directions that Postgres may take in the future. Of course, these are not going to be the only things that are going to be happening in development, but these are a couple of things that I have some numbers and charts to show. So the first thing I wanted to point out was column oriented databases. Does anyone use any column oriented databases? And for you guys using them, are you using them because you need to be able to do calculations better than Postgres can aggregations or whatnot or massive parallel in compression? So a couple of use cases that I'll mention, which of course are not everyone's, or people have other use cases for them, is some people are looking at column oriented databases for data warehousing, ad hoc querying, and computing aggregates over large volumes of data. So for those of you using it, I would imagine you took a look at the column oriented databases because of some deficiency in Postgres. Well, I'm assuming that you're all Postgres users here in the room anyway. You took a look at it because of deficiencies in Postgres and you needed to be able to do more than what Postgres could provide for you. So what we're going to do is use this dbt3 kit, which is basically a TPCH derivative. We're going to take a look at a couple of anonymized column store databases, which again you are welcome to guess at what they may be. But these are open source column stores. Oh, sorry, was that a... It doesn't mean there's still not legal issues to work around. So we'll call these guys CS1, CS2. All right. So then we'll take a look at the load test. Part of the TPCH benchmark is to see how fast you can load up your database. These are one of the metrics that when people review results of these benchmarks that they want to know. So we'll take 100 gigabytes of raw data just to give you an idea of what this is. It's eight tables on Postgres. It's also part of the load test. What's the word I'm looking for? The building of the indexes is part of the load test in addition to getting the data into the tables. And for both these column stores, building the indexes were not necessary for this to be able to run a power test, which is actually the queries against the data later. So in other words, we're building the indexes for Postgres because we need them to run the future tests whereas these column stores do not. All right. So our first column store database was able to load 100 gigabytes and a little more than an hour. Second one took about three and a half hours. Maybe a little twice still is the first one, but who wants to guess how long it took Postgres to load and build these indexes? 38 days. Anyone else want to take another guess? I don't have any prizes, but it will be fun to guess. 15 minutes? Almost nine hours. I think the 15-minute guess beats our 15-day guess. So whether it's because we need to build indexes, whether it's because it takes that long for Postgres to show that much data in the tables, this is two and a half to seven and a half times slower than getting data into a column store. So now we'll take a look at the power test, that test of running those 22 queries consecutively to see how well they do. My math isn't that good, but what I'm going to show you is how the score of these power tests are scored by a weighted geometric mean of the response times of each of the queries. So our column store, first column store scores almost 8,200. Our second column store scores almost 7,300. Who would like to guess what Postgres scored? 350? 10,000? 20,000. Did I hear 6,000? I already forgot who was closest, but Postgres only scored just a little over 3,900, which is almost half as low as those other two guys. And just to give you an idea, the queries that Postgres were particularly bad at compared to these column stores, these queries were intended to answer questions like, what is the 10 un-shipped orders with the highest value we weren't able to collect on? Another one was to query the revenue volume of local distributors of this database about a part supplier data warehouse. So it wants to see how well each of its local distributors are doing. Another query is supposed to show you which of your customers are having problems based on how many orders they've been returning. So these are the type of questions that you're trying to get out of your data. It seems like column stores are able to do these sort of things better. So then the question to pose is what can Postgres do to close the gap? Showing one possible path, which isn't really just for closing the gap for column stores, it's talk about Postgres XL a little bit. Any users of Postgres XL now? XC maybe? Something else that looks like a massively parallel database? No? All right. So Postgres XL is a... What's the best word for it? It's the next generation of implementation that was started by people who have worked on Postgres XC. Have people heard of XC or Stato? So Postgres XL is an MPP implementation, massively parallel processing database. And it is supposed to be an all-purpose, meaning use it for OLTP workload, use it for data warehousing, Foley Acid, Open Source, Scale Out. The Postgres XL project has just released its update against 9.5 earlier this week. For those who might have heard of it, the last time XL was actually released was against a 9.2 code-based. So if people have looked at it before, maybe interested in taking another look at it now. So before we get back to the numbers of how this thing looks against a data warehouse, this chart illustrates the scalability against a simple read-only PG bench workload. The bottom blue line there is the baseline of where 9.6 was at in running PG bench. Setting up two nodes with PGXL increased the throughput 1.8 times, almost doubled it. Doubling the number of nodes to four increased the throughput only about three times from the baseline. And then update nodes, it almost gets to six times the throughput of what 9.6 does on a single node. Also take a look at, with updates, Postgres 9.6 on the bottom, two nodes with PGXL. It seems to be a little bit of a benefit at the lower number of clients, but as you increase the load, it's really not looking too much better than a single node with 9.6. But then when we increased the number of nodes to four, we were able to be able to double the throughput with a simple update workload. And again, adding up to eight nodes, the total throughput is about three and a half times more. So now this is what we see going back to the power test and what might be encouraging down the roads of competing with column stores. What this bar chart is showing is the 22 queries on the power test and the response times in the lighter blue on the left is what Postgres 9.6 does compared to Excel on the right. Oh, a couple other important things to probably mention is that the Excel cluster here is 16 nodes. The amount of data here is actually not 100 gigabytes. This is actually three terabytes of data that these queries are running against. So if someone can help me do math, 100 gigabytes is 600 million rows in that table. Now we multiply by 300. 1,800 million rows. So as we're getting into the terabyte range, Postgres Excel is showing that it can pull ahead from a single node Postgres instance pretty well. And throw out a couple of numbers. In case you're wondering, the power score at three terabytes for 9.6 was only about 700. And if you remember at 100 gigabytes, the score was as it was able to get almost 4,000. Whereas now Excel with three terabytes of data scores almost 4,400. So it's able to score about six times better than what 9.6 can do with three terabytes of data. Sorry, I have more slides out of order here. Oh, yes. All right. So if those of you who didn't see me flash the answer will have some more quizzes. Did everyone see how long it took to load three terabytes of data? Okay, does anyone want to guess this one then? All right, so the load three terabytes of data, it took more than 79 hours for Postgres to load that and build all those indexes. Who wants to guess how long it took Excel to load that data? 10 hours. Any other guesses? 16. Three hours? 13 hours. And yeah, so math says that that is five and a half times faster than 16 nodes. A 16 node cluster can load data about five and a half times faster than a single node with three terabytes of data. Oh, sorry. I'm in the same slide as before. So that's all the charts and questions I, quizzes I had for all of you. Yes. Oh, sorry. Oh, so the buffers in the shared buffer, you have to know if it's dirty, not dirty. You have to make sure you, sorry, it's not the dirty, not dirty. You have to know whether you can pull that out of the, or when you're pulling data out or pushing data in, you got to make sure that you're the only one touching that buffer. So traditionally that's used by Spinlock and Spinlock bringing on the processor just keeps checking, are you free yet? Are you free yet? Are you free yet? And the methodology of doing that kind of test and set, well, no, I'm sorry, that's the atomic operation. So to use the Spinlock to see if that buffer is free is significantly slower than using the atomic processor instructions like a, I think it's compare and swap. Thank you. I had the cast in my head. So using those atomic operations from that the processor provides is actually a more efficient way of being able to say, can I do something with this or not? Any other questions? All right, thank you for listening.


Welcome to Toffy TV, it is my three things, not a shitty two ever and one ever and I've lost again, this time it's two, it was to the bottom, not a shitty day or not, bottom any more, they are off the bottom after that winning.  Again took a big following to Norwich, the five hour journey down to Norwich and they've seen Everton turning and which is becoming regular now, another awful performance and they've left with no points again. The problem is when you play awful football you've got to win games, if you're not winning games playing awful football then what are you playing it for? We are in huge, huge trouble now and this is on the owner really. First thing I want to talk about today is the slow start of the team, every single game. I think it's nine games on the run we've conceded the first goal but most of the games we played this season we are on the back foot from the first whistle and I do not understand why. I do not understand where fast starts the games have gone, it was something that we were kind of, under David Moyes we were kind of built on it, other managers too get on the front foot early, get a shot away, put them on, try to get them on to the heels a little bit. This is a team today that we were playing, having scored half a dozen games I don't think, haven't won for months all over the place, can see goals for fun and we give them, we just let them take the scruff of the game by the scruff of the neck earlier. Before you knew it we were two goals down, we've done the same with Brighton the week before, another team that doesn't score a lot of goals and great 2-0 down after 20 minutes. We've got a manager telling us that we work hard and train and we do this, we do that, I'm showing them what to do, they're going out doing it. It's something, pick a different team there, pick different players, Michael Keane, awful again, truly awful again to the half-foot, certainly the first goal and a better part of the second goal. Make the change, put a different partnership there, do something, make it look like you're trying to make a difference, they're all over the place. You can sing you to sit back and let the other team off the ball and give them the opportunity to feel away into the game, especially with the crowd, you're going to find it difficult to turn it round and we went sooner down and were never able to turn it round and that's on the tactics for me. It's on the players as well but it's on the tactics, he should be saying to them, get in, face balls, get up the end of the pitch, get shot away, get crosses into the box and make them worry about us and we seem to be. Slow start, let's feel the game out, see what happens, a lot of games are out of sight and we're not just nodding it and today we were tunled down and we were never coming back from tunled down ever because there's nothing in this team to suggest it and the way he plays doesn't suggest it. So, if any manager he comes in next, whoever it may be is in charge for that feeling again, if that's going to be the case because I look, I'll talk more about the manager in a bit but we need to get that slow start. The second thing I want to talk about briefly is just Vitaly Michalenco, Vitaly Michalenco. It wasn't amazing today, some decent things, some poor things but we just got to give this lad time. I hope that the fans aren't going to jump to conclusions and it's terrible, we should have kept Lluca Deane, you've never bought this kid and everything else. Listen to that ideal world, we would have still had Lluca Deane as well but we haven't and we have to move on from that. It's not this lad's fault, he's come from a new league, he's not played much in the last two months because of the break and all of that. Got to let him grow into the Premier League, he shows some nice touches, he's calm on the ball, couple of silly decisions that he's made, he'll have to grow into that but he's 22 and learning the game and we have to back him and I think he'll do alright, he's a good athlete but you know what I'd rather make a slow start to his ever in Korea and grow into it and understand it and I'm sure the fans will anyway, get behind them and back them, he's got a bit of growth to do and that's fine but we do have to stay with them, stay with the young lad and really get behind them and help them because it is difficult for him as well replacing a left back who people didn't want to go, didn't want to go but it's always going to be a difficult job anyway. So hopefully he'll grow into that and the fans will back him and I'm sure they will. Finally just very briefly on the manager, the record speaks for itself doesn't it? I've tried to give the manager the benefits of the doubt all season, I've felt at times he's been justified in saying we've got no players, it's certainly not the case now.  We're still rubbish off set pieces, he still hasn't realised that we can't play with two men in the field which is terrifying and until he, well I don't know whether he's going to get any longer and maybe he shouldn't get any longer but until he's laying that he's always going to be playing catch-up, you know he's not the most exciting manager, he doesn't get you up for games, he doesn't inspire you so if he's like that we're going to be playing catch-up, you know he's not the most exciting manager, he doesn't get you up for games, he doesn't inspire you. So if he's like that with players then obviously it's difficult but he's not inspiring these players, he's not sorting out the set pieces, he still hasn't worked out that everything can't play with two men in the field and it's the middle of January and he's been here since July the first when pre-season started, he's had all of that time and there's been nothing, there's no pattern of play, there's no set pieces, we're not organised off set plays. And at the moment it just looks a huge mess and it's like it said on my mat reaction that the owner's got a huge decision to make now. One winning 13 is as bad as anything that's happened in my lifetime, anything. We're shambolic, absolutely shambolic and we're not just losing to teams who are near the top, we've just lost 2-1 to the bottom team, we're the only team in the Premier League to have lost to all three promoters this season. We are a mess and we're in huge, huge trouble and if the owner doesn't realise that, we're in even more trouble than a lot of people think. I don't know how the owner can justify not making the change now, really. I don't, I think other managers got half the time when he says he's had with half as bad a result on the chase, so we'll have to see if he doesn't make the change then the manager has to realise we cannot play with two men in the field, we need three in the field, we simply do, we need to get legs around people, especially when the two are hands high Gomez and De Corrie, De Corrie can't do it all, Gomez is nice on the ball but he's not all action, he's not a six, he's not winning balls on the edge of the box. We need three men in the field, we need to be solid at the back, Michael Keane's been a problem for a while, his form's terrible, he's letting goals in, he's responsible for a lot of them, he hasn't made the change there, he's left him in. Rondon is never the answer to no matter what the question is and yet he's playing him, so he's all over the place and he's got to make these changes. We've got a big game next week against Villa, it's going to be a horrible game, Gerard, Catino, Llew Cedin, there's a lot on it already and if Everton don't make the managerial change this week then I imagine the atmosphere is going to be quite difficult. Some out of the team have got to get a result there, we simply have to, we haven't had a result, we've had one win since September 25th, we've had one clean sheet since September 25th, it's absolutely terrifying that he seems to not understand the difficulties and doesn't understand where we are. I've seen a couple of his quotes after the game going on about we're trying to fix things from years ago. Mate, you've got a squad of players there, it's your job to put them on the pitch to win games, you can talk about trying to fix errors from years ago, about the off the pitch stuff, I understand that. You can talk about flipping the squad saying we're trying to, but these are your players, you brought it right back, he hasn't had a minute yet. So you can't quote problems off the pitch from five years, if over the last five years we're trying to fix when your team isn't getting results. You can talk about that when you're winning games and you're drawing games and you're losing the odd game, you can say we're making the changes and this is happening, that's happening. We simply don't win games, our last win was December the 8th, before then it was September the 25th. These numbers are terrifying, these numbers are relegation, we are in a relegation battle because of the manager, or not fully because of him, but under his watch something's got to change very quickly, otherwise we are in for the most terrifying season. Even worse than the likes of the Wimbledon game at the moment because at least that season Mike Walker got wins somehow. Benitez is record, worse than Mike Walker's at the moment which is frightening. So something's got to give, something's got to change, he's got to have a totally changes attitude and is the way he approaches games this week if he's in charge of a villa, but we'll see what happens. Anyway, that's it, I've gone on enough. Thanks for watching, give the video a thumbs up, subscribe if you haven't, and if you want more videos join me on Patreon, have a good weekend, see you later.


Thank you for coming. My name is Duncan Edwards. I am the director of global initiatives and impact that publish what you pay and that's really echoing so I'm going to move slowly. So the focus of the session today is both publish what you pay and open contracting we'd be sharing some of our experience in using data. And then we want to open it up to a conversation to share experience of different dimensions of building data literacy and make making greater usage of data that is now available. Is that clear to everyone? Yeah, brilliant. Thank you very much. So two parts of the session for the first part is about 30 to 40 minutes of presentation. So, unfortunately, Miles Lipanoff, who is the UK coordinator for publish what you pay, hasn't been able to travel. He had to cancel at the last minute, so I will be endeavouring to cover his work with some degree of quality. And then we have Joyce, if you'd like to stand up. Say hello. Joyce works for Zella in Zimbabwe, and she works with the published what you pay coalition there. She's going to be talking about their work there. And we have Catherine, an open contracting partnership, and she's going to be talking about her work. And then we'll be moving to a 40 minute, actually it's probably going to be near a 30 minute interactive discussion. Brilliant. Okay. Thank you. Okay. So publish what you pay, it's an organisation that started in 2002. It was really looking to bring more transparency and accountability around the revenues that paid to government in relation to extractive industries. So the extraction of oil, gas, minerals. Okay. Why is it a big problem? Because it's a huge amount of resources, a huge amount of money. We have NRGI in the room who know a lot more about extractives than I do. I'm very new to publish what you pay. So why is payment transparency important? Publish what you pay believes it deters and helps to reveal corruption or questionable deals and mismanagement of revenues. It can help citizens and civil society to make governments and companies more accountable. And it helps shows how far revenues compensate for negative impacts. A big part of publish what you pay's work is to try and make the payments of oil companies to governments or transparent. We call this mandatory disclosures. So what does that cover? It's essentially annual payments to governments reported by large registered listed oil gas mining companies in the UK, EU since 2016. In Canada since 2017 and in Norway since 2015. In the US there was a transparency law that was drafted in 2010 but that awaits implementation. The potential coverage with the US and all this legislation covers 84 of the world's 100 biggest oil gas companies and 58 of the 100 biggest mining companies. So what kind of data is being published? Here's an example. It's reported electronically online. This is an example of a Royal Dutch Shell report to the UK in 2016. The UK currently is the only country requiring open machine-readable data by XML. It covers payments by project disaggregated by seven payment types, project totals payments by recipient government entities and lots more jargon. If we want to get into that but you're not all extractives people. The UK and Canada are currently the only countries where there are central repositories and platforms that bring all this data from different companies together. So what we're seeing is there's a lot more data available that's been published by these countries. So this has been a big win for publish what you pay over its lifetime. But there's lots of data now being published but as yet not much of it is being really used effectively. So publish what you pay, conceive a pilot program to support the usage of extractives data and that was our data extractives program. So it's a two-year pilot that is supporting publish what you pay members in 40 different countries to make use of data. So in this pilot project we supported 24 individuals in 21 countries to make use of the data that is now published. We supported these activities via workshops, mentoring, WhatsApp groups, email lists, Slack, so on and so on. It's a real mix in terms of different publish what you pay members from lots of different countries. Some in the north, some in the south, very different levels of capacities, looking at different problems, looking at different sources of data as well. And it's resulted in a number of very different outcomes. So this is some of the work that Miles did using UK published data from UK companies. He did this in 2016. It was very much an experimental pilot project and it was using data from the first year of mandatory disclosures in the UK. It was very much a collaborative project trying to work with some of our coalition members in southern countries. And it was really trying to explore the relationship between home countries, the countries in which extractive companies are based, with people working, activists working in host countries to try and explore how they could be using this data for greater accountability. And I think given the nature of publish what you pay's work, these kind of cross-country collaborations will be a core focus of our work moving forward. So the aims of the project was to test how data could be used and what it can tell us. It was trying to strengthen and publish what you pay's work in kind of north, south, evidence-based advocacy. And it was trying to build essentially a cohort or cadre of infomeduaries who could analyse the data and make it more accessible to people. And it was also to demonstrate to companies and to governments that we were actually looking, that we were scrutinising the data they were publishing. The approach used in this example, Miles worked with four different publishers for what you pay coalitions in the host countries, so in southern-based countries, to identify key payments reported by UK companies. And he did some analysis and produced a number of infographics and drew out some key questions that could be asked to relevant government entities within those countries. So it's trying to get answers to questions that arose in the data. The countries he looked at were Nigeria, Iraq, Indonesia and Tunisia. The four companies he looked at were Shell, BP, the BG Group and Petrovac. And it was looking at two payments in 2015 by four companies in four countries totaling $7.4 billion. The infographics he produced were for Nigeria, Tunisia and Indonesia. And for Iraq he created a kind of Arabic translation in summary of some of the issues in the data. He highlighted main payments to government entities, identifying the highest paying projects and the largest payments. And he listed key questions that could be put to those government entities. So in terms of outputs and some of the outcomes, it was a bit of a mixed picture. Publish what you pay Nigeria, took the outputs from the analysis that Miles did. They sent them to the relevant departments, so the Department of Petroleum, the Federal Inland Revenue Service, et cetera, et cetera, with cover letters asking them to explain and verify these revenues. Every single one of them refused to respond to that. Then they made freedom of information submissions, asking the same questions. These were still refused. And then they then were looking at options in working with press, but for various reasons that didn't go forward. In Indonesia, published what you pay Indonesia, sent these infographics and these series of questions with covering letters to relevant departments. Again, refusal to engage and produce the information that was being asked of them. And they were actually told that they must wait for the next EITI report, so EITI, hello. There were some questions that were posed to the companies and they did actually respond and responded because it was identifying some discrepancies in the data and they were then corrected. The reasons for the payments are pretty clear. It's paying for the extractives revenue. The question to the government is, did you receive this and how have you used it? Then in Tunisia and Iraq, basically for a number of reasons, not much happened. So what we see is there's some analysis of some data, there's some questions being asked, but we're not really getting beyond answerability in terms of getting a response from government. And I don't think this is unusual for a lot of this kind of work. And so I think a kind of central focus of our work moving forward is how do we get from being able to do the analysis to getting answerability from government on some of these questions. So takeaways, limited success, I think there's outcomes that we've seen in terms of building people's capacity and confidence to use data. We've identified the importance of really strong relationships between our northern coalitions and our southern coalitions. That's going to be quite a central part of our future work. We've demonstrated that we're watching, that we're scrutinising this data. But lots of questions about what do we do next, how do we actually get answerability from government? What if the government refuses to engage? Demonstrates again, like in lots of other sectors, that accountability is very difficult and actually that kind of information availability to accountability is hard to follow. We experienced significant challenges in forging the kind of relationships and mutual priorities between different data extractors. So at the start of the programme, I think there was a real focus on using particular sources of data. But actually in reality a lot of the southern coalitions were interested in using other forms of data, other sources of data. Some questions about data ownership, language issues, and like a lot of this kind of work more broadly, it's early days and I think it's going to take a lot of time to learn and adapt and push on from here. Just a quick plug, one of the things we've done recently and this will be published either this week or next week is some kind of quick how-to-guides in terms of how to use UK companies data. That's published in English, French and Spanish. And with that, I will hand over to Joyce to talk about it.


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