Now they'd recognized the need to test the software, so they had a dedicated test team for the program. None of those found the bug, the software then arrived with our clients. The product team had a set of unit tests that were run over the software. So what went wrong? First the product testing failed to stop the problem. Now these were two mature organizations, both were foot FTSE 100 companies at the time, they'd recognized the risk of bugs in the software, and they put a number of barriers in place.īut those barriers failed. At the end of the month, when the the back office settlement process kicked in, that's when they noticed the issue and it was rapidly resolved at that point. And this obviously put the client out of balance so that what they were generating or consuming didn't match what they'd captured in the trading system.Īnd that resulted in punitive imbalance charges about a million pounds a day, for a month. If the trader booked to sell under these specific conditions, we notified the market that they were buying. He was meaning well, but unbeknownst to him he introduced a bug, which meant that under certain conditions, when a trader booked to buy we were notifying the central market agent that they were selling and vice versa. While he was in the code, he noticed an area of the code which to him was a confusing, there was a range of conditional statements and he decided to tidy those up to make it clearer what was going on. The lead developer on the product team was making some final small changes to the product as part of preparing the final release to go out to our clients. Now the bug was introduced about a week before go live. ![]() I was on client site responsible for taking the software from the product team, configuring it and integrating it into the client's technology environment. I was working with a client based in Glasgow. We had a product team based in London building the software and a number of clients around the UK. The client I was working for had been running a two year program to get ready for the new market, and the company I was working for was one of the vendors. This dates back to 2001 and the launch of the new electricity trading market in March of that year. So what went wrong? What resulted in a change to a single line of code costing the client I was working for 30 million pounds? Did we achieve what we set out to achieve and what did we learn along the way? And then hopefully at the end, we'll have time for some Q&A. I'll cover the vision, the people who are involved and the tools that were used, which will allow me to demo some of those tools as well.įinally, I'm gonna wrap up and reflect on the project. Then I'm gonna talk about a project that we ran last summer in this space, where we set about generating synthetic customer data. I think that sets the scene really well about why production grade test data is really important. I'm gonna start with a story about how a change to one line of code cost a client I was working with 30 million pounds. So what do I want to talk to you about today? And we do that generally in two ways, through data analytics and. We like to say that "we help small teams achieve big things". ![]() Then having graduated in 2019, I joined endjin. I was a CTO at a financial services company, but having attended some of the data lab events and with the support through their MSC program, I completed Masters in artificial intelligence right here at the University of Strathclyde. They inspired me back in 2019 to do a career pivot. In particular, I wanted to call out the Data Lab. Data Scotland is very much a community driven event, but without the backing from these sponsors, it wouldn't have been possible to host the event today and bring the community together. But before I do that, I just wanted to call out all of the wonderful sponsors who've made today's event possible. And today I'm going to talk to you about generating production quality test data at scale. ![]() He describes how this approach can be used to build better products, to test products using production quality data at production scale, and embed data quality and best practice information security practices in your engineering processes. The challenge is providing product and engineering teams with a sufficient volume of realistic looking synthetic data to enable them to design, develop and test their solutions.īarry presents open source tools and open data sources that can be used to tackle this challenge, and then demos this in action to generate thousands of synthetic customers. Many organisations provide digital products or services that need to handle personally identifiable information.
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