After studying for a Master’s in electronic engineering, I wanted to learn more about business and finance, so I began my accountancy career focusing on private equity due diligence at one of the Big Four. After many late nights and long weekends in this job, I realised that I could apply the programming knowledge from my degree to cleanse and analyse financial data and analyse performance trends. This not only saved many hours but also identified trends and value that could not be seen from traditional analysis. This approach was noticed by senior stakeholders and clients, who were impressed by the more efficient approach to data handling and the insights it uncovered.
Off the back of this success, I was allowed to build a team and launch a new client solution to deliver advanced insights during M&A processes. I realised that to consistently deliver these value-added insights, analytics would really need to be understood across the consultancy industry, and so did EY. In 2017, they hired me to lead the development of their data and analytics strategy for transaction diligence and to help train client-facing staff in basic descriptive analytics. This, alongside the existing advanced analytics capabilities, revolutionised our go-to-market strategy and has consistently delivered additional value to our clients.
It is pretty amazing to think I have helped to scale analytics from a few case studies to now, where over 50% of diligence exercises are using data and analytics. This is the direct result of a two-year effort by me and my team to implement a major change in the way we do transaction diligence. EY is now working with new clients and winning more engagements with a large market still to address.
My family is full of game-changers who have managed to shake things up in their respective industries – from high tech processors to sheep farming to corporate finance. They’re all rather tough acts to follow but they’ve taught me that taking risks and challenging the status quo can often reap the rewards no matter how outside the box the ideas might seem.
It has been incredibly exciting to see how colleagues from diverse, non-technical, backgrounds have really been able to enhance their productivity and work in ways they hadn’t expected to. Over the year, I saw sector-focused communities and different service lines working together in new ways. The highlight of the year was a cross-Europe and India live analytics challenge set up by a member of my team where she co-ordinated over 80 people with little prior analytics experience to compete for prizes - it was great fun!
At EY we focus on training all client facing transaction staff self-service data analytics. This will have a huge impact on our capabilities and will reduce the knowledge gap for both clients and team members who may still see analytics as a magic black-box.
We expect to see significant progress in machine learning and artificial intelligence. For example, EY has applied these to predict working capital trends at an invoice level.
In the wider industry we see the focus on companies like Microsoft and IBM building more user-friendly interfaces for these advanced technologies which should continue to drive the uptake and deployment of innovative ideas
Data analytics no longer needs to be exclusive to companies who can afford it. Now that tools and training are more accessible (and less expensive), developing countries and charities, for example, can use them to their advantage too. Whether it’s deploying resources more efficiently, targeting potential donors more accurately, or, in the case of Belize, using data analytics in their jaguar conservation efforts, or AI to monitor the health of coral reefs, there’s a fantastic opportunity to expand the need and use of analytics.
The biggest challenge is around data quality. We have actively been working with clients to consolidate and cleanse data across multiple legacy systems and developing tools and techniques that allow them to begin their transformation journey.
The next step is for clients to consider three things: what data they should be capturing and how; what external data is available to enrich their own; and, ultimately, how can this data generate value?
Most importantly, we continue to encourage a culture whereby staff innovate and challenge the traditional ways of working. Data can be fun when you want it to be.