This is a profile from the 2020 version of the DataIQ 100.
The 2021 list is available here
I have more than 20 years’ experience in the IT industry, specifically in enterprise software for data analytics and artificial intelligence. I also hold a first-class honours degree from UWE Bristol in business decision analysis.
Early in my career, I held various sales leadership roles within HP and Cisco in the UK and globally. In 2012, I was appointed vice-president at Qlik UK & Ireland and was promoted to managing director and country manager in 2016. During this time, I helped to grow the company significantly through self-service analytics and the rise of data visualisation platforms.
In my current role, I am responsible for the go-to-market strategy within EMEA. The company has expanded significantly in the region over the past 18 months and we are now operating with teams in the UK, France, Germany, Spain, Nordics and South Africa.
I have been fortunate to be involved with some amazing teams and fast-growing companies during my career so far. Perhaps my proudest achievement, though, is scaling out the DataRobot business in Europe over the past 18 months.
We started with just a handful of people in the UK. Now we have eight offices in the region and serve customers in all industries across EMEA. We have built a fantastic, collaborative team with some of the industry’s most talented data science and analytics experts.
I feel inspired by many business leaders and sporting heroes but to name one I would choose Richard Branson. He naturally leads people and I strongly share his belief that “no one can be successful alone, and you cannot be a great leader without great people to lead”.
We were pleasantly surprised about just how much interest there has been in Automated Machine Learning (AutoML), which DataRobot pioneered to automate many of the steps needed to develop AI. The new challenge though is that in 2019 everyone started using the term, making it harder for customers to cut through the noise.
To maintain our leadership position, our product team focused on enhancing our end-to-end AI platform and we made three acquisitions in 2019: Cursor, ParallelM and Paxata. These further accelerate AI projects and ROI with market leading capabilities for data collaboration, MLOps (deployment, monitoring and management) and data preparation.
2020 will bring many exciting developments. The market for AI platforms in business is maturing rapidly, with early adopters achieving massive returns, while others risk falling behind.
AI is essential to make more informed decisions and will become much more ubiquitous.This will be further accelerated by super-fast 5G networks and IoT devices, intensifying the feeling that AI is everywhere. I believe that “hyperautomation”, which blends robotic process automation and AI, will also take off. This allows businesses to automate more quickly, broadly and deeply. It will lead to more complex end-to-end processes and work previously reserved for humans to be automated.
The biggest opportunity is to use AI for more than just targeted adverts or commercial gain, but also for wider societal good. Many customers are placing huge value on AI to make their staff more productive and their lives more fulfilling. AI is increasingly used in critical domains, like in healthcare for better diagnosis and treatment of diseases, or in banking to combat fraud and money laundering. At DataRobot we are doubling down our AI for Good programme, providing free support and services to worthy causes, like using AI to improve Africa’s water supplies.
There are several obstacles, most with a human dimension. Perhaps the single biggest is trust. Businesses need to understand and trust the results from their data. They need to be able to explain in human-friendly terms why decisions are made, not use black-boxes. More broadly, using data in an ethical manner will be a hot topic throughout 2020.
Automation is crucial to maximise the productivity of data professionals and their peers, as is general education around AI for the business and executives. Selecting and framing business problems correctly is vital, alongside enhancing data literacy across organisations.
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