The changing landscape for analytical talent

Pythonistas! Everywhere!

eFinancialCareers have hit the nail on the head with their aptly titled: "Forget Excel: banks want Python monkeys now" which has this data point in it: "around 50% of incoming analyst classes have some knowledge of Python coding".

Meanwhile, in other recent news, Accountancy Age, in their article "PwC’s rising star" acknowledge this year's Britihs Accountancy Award winner, Harry Pampiglione, as having developed a proprietary Python model for optimising pricing. This analysis earned the firm a six-figure fee, and has subsequently been productised. Harry's qualifications include Alteryx Core and Udemy's Python for Data Science and Machine Learning.

So what's the moral of the story here? A knee-jerk, instant reaction might say … "Go enrol in Python for Financiers"! But that is kind of losing sight of the wood for the trees. Take a moment and step back … with this diagram:

Source: Beautiful.ai

Hardware & software infrastructure

Firstly, let's talk about hardware & software infrastructure in place. The eFinancialCareers article has this tidbit:

“When I arrive at my desk as an analyst who is using Excel, then I am good to go to do simple things like modelling an M&A target," he says. "But if I want to model ALL likely M&A targets using predictive analytics, then I will probably find there is no infrastructure to help me. There’s not the data and there’s no access to the cloud. Banks need to do more than just enable today’s analysts to use Python if they are going to truly benefit from this next generation of bankers.”

In the first step in our data driven journey … it's about having the supporting hardware & software infrastructure in place. For some environments, you'd may want to think about having server facilities to run ad-hoc analytic processes. For some environments, you'd want to think about doing everything in-cloud, and having no local resources. It's quite likely that you'll fall in to the "in the middle" Goldilocks camp : a hybrid of the two because 100% local is "too much admin overhead" whilst 100% cloud may be awkward with transferring large data sets over bad connectivity link. You'll have to find a balance.

In the quote above, the analyst is saying that the next generation of Financial Pythonistas are expecting this infrastructure to be ready and in place.

Processes

Secondly, let's talk about processes. All the top-shelf hardware and software in the world won't be of much help if …

… you don't have the processes to collate, sanitise, cleanse, maintain and distribute data sets.

As a data driven strategy practitioner, one of my biggest challenges is in having hygienic, clean data that's ready to go and is robust. One of my projects earlier this year involved a dataset that didn't apply a consistent naming convention for column names - I wasted a lot of time just getting to grips with how data was being recorded differently at different times across different processes.

My prediction for one of the hottest buzzwords for 2020 is : data lakes - which are large databases of structured and unstructured data. But now enter the augmentation from AI. Using cloud-driven, machine learning processes to sanitise and improve the usefulness of data is a strategy that I expect leading proponents of data driven strategy to pursue this 2020. Because having AI is just a way to augment … our people. Which brings us to :

People

Thirdly, let's talk about people. The two referred articles above have another thing in common: today's financial analysts are not the same breed as those from yesteryear! You might even hear the term "snow flake" bandied about, or "Millenial", but the truth of the matter is that the next generation of financial analysts may be short on patience and high on job/social mobility. Meaning that if we, the business leaders of today, don't flex our people management strategies that suit the new realities of the talent market, then we risk irreverence.

More than half of the new incoming analyst classes are pythonistas. If the ibanking crowd are tackling problems in a data-driven way, what do you reckon that the rest of the financial services industry will expect of us, practitioners of data driven strategy?

Because the talent that we hire today, shapes the culture of the workplace tomorrow … which brings us to …

Culture

Lastly, let's talk about culture. With more and more of financiers and straegists "thinking in Python" … you can bet your bottom dollar that the tomorrow's projects will feature more "computational thinking" than yesteryear's projects. What does that look like? 

Well, rather than hiring hordes of junior staff and throwing time/resource at problems and hoping that some of them stick … these new bankers who are thinking in Python would be more accustomed to thinking "I can delegate this to Python and free up my time from this less glamorous work".

All in all : thinking about the different components of the stack, from hardware/software to processes, people and culture is a great way to structure one's thoughts on how we can best tackle this challenge of being more data driven.