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Jay’s career has never followed a straight line. Electronics engineering. Automotive systems. A social app for hostels that was about to launch just as COVID closed every hostel in the world. A pivot into web development. And eventually, Fetchify - where he now leads the team building the technology that keeps millions of data lookups running accurately every day. Looking back, the route makes perfect sense. Jay has always been drawn to what’s next. To faster feedback. To building things that work and seeing them work quickly. Software gave him all of that in a way that automotive engineering, for all its complexity, eventually stopped doing. The long way round Jay studied electronics engineering and came out of university specialising in embedded systems. By 2015, he was working on automated parking systems - the kind built on sensors and split-second decisions - and for a while, he found it genuinely interesting. But something was missing. “I wanted to see results faster,” he says. “With embedded systems and automotive work, the feedback loops are long. I wanted to build something and see it working.” So, he pivoted. He taught himself mobile development and from there, a startup building a social app for hostels and hotels - a platform that matched guests by shared interests, so someone travelling alone could find other guests up for the same activities. It was a genuinely good idea, with a handful of places trialling the beta version. Then 2020 arrived, the hospitality industry stopped overnight, and the timing simply couldn’t have been worse. Most people would have counted it as a setback. Jay counts it as part of the story. Finding something that fits He joined ClearCourse, initially working on the membership CRM side of the business. When a role came up at Fetchify, he knew it was the one. Tech Lead. A team to run. Real scope to build, improve and innovate - and enough space to do it properly. “What I love most about my job is the chance to be innovative and improve the quality of the software - and the opportunity to keep learning. There’s always something new.” His approach to leading the team reflects the same values. He talks about trust a lot - giving people the space to do things the way they think makes sense, rather than prescribing the path. The team checks in daily, whether that’s to swap ideas, talk through a problem, or join a scrum call. It’s not just his immediate team either: the wider Fetchify team, and within the ClearCourse group, there’s a culture of helping out. Of people being willing to lend a hand when it’s needed. “Software development can feel like a solo job, but actually the team here is solid, and we enjoy working together.” The thing he's most excited about Ask Jay what he’s most passionate about right now, and the answer is immediate: AI. Not in an abstract, trend-chasing way - but with a specific and considered view of what it actually means for software developers and the organisations they build for. “AI is raising the bar for what developers can produce. But I see it as a two-way collaboration - a helping hand to do the grunt work, while the ideas, the creativity, the innovation still come from people. It should help people achieve more in less time. Not replace the thinking.” His long-term goal is to help other ClearCourse businesses integrate AI into their products - starting, naturally, with Fetchify. For a company built on data accuracy, the intersection of clean data and AI capability is not an abstract future conversation. It’s already the direction of travel. Beyond the screen Jay grew up in Egypt, and travel is still one of the things he values most. He heads home to family a couple of times a year, and fits in city breaks wherever he can - somewhere new, with good food and different people and things to explore. His ideal off-duty scenario involves a beach, good conversation, and absolutely no particular agenda. The gym, friends and music round it off - time away from the screen that, for someone whose working life involves building technology that processes millions of data points a day, seems like a fairly sensible skill. When he imagines the distant future - the looking-back version - he pictures a career of creation, innovation and the willingness to embrace whatever comes next. That, and a beach somewhere warm. We’re very glad the winding road brought him to Fetchify.

“Fetchify turned what felt like a crisis into a straightforward fix - and in just a couple of days. We went from not being able to contact anyone to generating four new client applications from a single send. The data cleanse didn't just fix a problem - it opened the door again.” – Marcel Stirling, Phoenix Insolvency

There is a lot of enthusiasm right now about what AI can do for ecommerce and CRM teams. Personalisation at scale. Predictive analytics. Automated outreach that learns and adapts. The pitch is compelling, and much of it is real. But there is a foundational question that almost nobody is asking loudly enough: what happens when you run AI on bad data? The answer is not that the AI fails gracefully. The answer is that it fails at scale, confidently, and in ways that are harder to trace than a simple spreadsheet error. This is not a theoretical risk. It is already happening inside the organisations that have moved fastest to adopt AI-driven tools without first addressing the quality of the data those tools run on The assumption nobody questions Most organisations treat AI as a layer that sits on top of their existing data. Feed in the CRM, connect the customer database, and point the model at the transaction history. The assumption is that AI is smart enough to work around imperfections. It is not. AI systems are pattern recognition engines. They find what is consistent in the data and treat it as a signal. If your data consistently contains errors - outdated addresses, duplicate records, lapsed contacts still marked as active - the AI learns those patterns as the truth. It bases its predictions, segments, and recommendations on a foundation that does not reflect reality. B2B contact data decays at 30% per year. For a database of 100,000 records, that means 30,000 entries become inaccurate every 12 months. When an AI personalisation engine is drawing on that data to decide who to target, when to contact them, and what to offer, it is working with a picture of your customer base that is one-third wrong AI doesn't fix bad data. It amplifies it. What this looks like in practice The problems that emerge are not dramatic. They are quiet and cumulative, which makes them harder to catch. Automated email sequences reach the wrong people or the wrong addresses, generating hard bounces that damage your sender reputation and, in serious cases, trigger blocks from email service providers. Personalisation that references a customer's last purchase or location draws on a record that has not been updated in two years. Predictive models identify high-value customers to target for retention campaigns - but a portion of those customers moved, changed roles, or lapsed long ago. Each of these is a cost. Collectively, they represent a significant drag on the performance of tools that were supposed to be driving efficiency. The irony is that AI makes these problems less visible, not more. A human reviewing a list might notice that an address looks wrong. An AI processes it at speed and acts on it. A case study: what happens when AI meets dirty data A professional services firm recently experienced this directly, who work with our sister company FLG for lead management. The team began bulk emailing an existing database through their email marketing system - a reasonable use of automation for a business trying to re-engage contacts at scale. The data, however, was old. Hard bounces accumulated quickly, and their account was flagged and blocked from sending. Fetchify cleansed the data. Contact information was standardised, and inactive or undeliverable entries were identified and removed. When they resumed outreach, the results were immediate - higher engagement, no delivery issues, and the kind of performance the automation was always supposed to deliver. The AI-driven outreach did not fail because of the tools. It failed because the data had not been maintained. Once the data was clean, everything else worked as intended. The AI readiness question organisations should be asking As AI becomes a standard component of ecommerce and CRM operations, the conversation around data quality needs to change. It is no longer just a compliance issue or an operational nicety. It is a prerequisite for AI to function as intended. Before deploying any AI-driven personalisation, automated outreach, or predictive analytics tool, the right question is not 'which AI platform should we use?' It is 'is our data clean enough for AI to learn from?' For most organisations, the honest answer is no - not without first running a data cleanse. The good news is that this is not a complex or expensive process. It is a one-time exercise that resets the foundation, followed by ongoing validation to prevent decay from accumulating again. What clean data actually enables Organisations that address data quality before deploying AI achieve fundamentally different outcomes. Personalisation engines draw on accurate records and produce recommendations that reflect the real customer base. Automated outreach reaches real inboxes and generates real responses. Predictive models identify genuine opportunities rather than ghost records. The regulatory dimension is worth noting, too. The ICO can issue fines of up to £17.5 million or four per cent of global annual turnover under UK GDPR for data governance failures. AI that acts on inaccurate or out-of-date data does not protect organisations from that exposure - it amplifies it, at speed and scale. Clean data is not an enhancer of an AI strategy. It is the essential prerequisite that makes an AI strategy viable. The organisations seeing the best results from AI aren't necessarily the ones with the best tools. They're the ones with the cleanest data. Start with a free data health check and find out where you stand.