I grew up in an academic family, and I was interested in technology from the age of nine, when I coded my first game. Doing a degree in electrical engineering quenched my thirst for maths, signal processing and programming. This was the start of my interest in data.
After I graduated from university, I got a job at Accenture, where I was able to develop my skills even further and apply what I had learned to great success. This resulted in me taking up a position as the head of business intelligence for the world’s largest diamond mining company, De Beers.
After that I was given a massive opportunity by JustGiving, which offered me the chance to use my love for data, algorithms and AI to solve the challenge of generosity. Very quickly we found that the use of data and complex algorithms could grow the world of giving and fundamentally change the way consumers engaged with causes that they cared about.
Our algorithms generated millions for charity and successfully enabled JustGiving to get a valuation of over $100 million. After the sale of JustGiving, I spent a year writing up our successful methodology for others to share and this ended up as an Amazon best seller in 2019.
My career has continued to grow since, and I am now the founder of a company called www.lens.ai, where we provide consultation, training and solutions for businesses that understand the importance of knowing and understanding their data.
There are several achievements I am proud of. The first is being a part of a team of eight who sold JustGiving; my work was key, and I am proud to have been involved in a transaction of that size.
Secondly, becoming a best-selling author in 2019 with my book, Cracking the Data Code: Unlock the Hidden Value of Data for Your Organisation. Writing a book takes a lot of time, and I am proud that people were able to benefit from it, as the reviews have been amazing.
I am also extremely proud of my reputation, and the awards I have won. I am totally committed to my career, and many say that my reputation precedes me. This is why I have been sought after for consultation on projects, and why I am in demand as an international speaker.
There are many people who I look to for inspiration but lately it has been the author Bill Bryson. Simply because of his ability to take complex subjects and make them simple for anyone to understand. He removes the requirement for you to be an expert in any specific subject but then informs you in a way that you leave with a comprehensive understanding. I have a couple of other books and projects underway and he is a great source of inspiration.
The world of data and AI is not going to stop but I have to say that I was surprised in 2019 by the number of companies that have still not invested in these technologies. I expected more companies to have both understood the benefits and made greater investments to reap the game-changing transformation that data promises. The risk and cost of not getting involved are so high. It will be interesting to see what happens when they realise that their competitors are investing in it.
I expect that we will see some significant advances in AI use-cases, particularly in the domains of healthcare and security. Explainability, trust and ethics will receive a greater amount of attention, particularly after some of the very visible failures around trust in the AI marketplace, such as the Apple Pay rollout, the coming US election and the inevitable resurging interest in the Cambridge Analytica scandal. If companies are paying attention, we can expect to see VCs and new start-ups operating in this space too.
Personally, I think that there is a huge opportunity in non-technical training. As data and AI become increasingly pervasive and more mainstream, the requirement to understand it has also increased. Non-technical executives need to build an understanding of what data and AI really are; how can they find differentiating opportunities to use them and what do they need to do to guarantee success in the significant investment in tech?
The biggest challenge generally I would say is around culture change. From a technical perspective, one of the biggest challenges remains in data cleansing. The core raw material required to deliver value from complex algorithms is still messy and requires cleaning and preparation in order to be used. More effort at the source of data collection is still required. In addition, non-representative data-sets are also exposing natural human biases that are now coded in data. For consumer driven applications, this really needs attention.