It’s not rocket science, it’s data science! How data transformed the future of finance

Data scientists and analysts were once emerging roles in the IT arena, but now they are in red hot demand within finance teams.

Today’s companies are increasingly calling for CFOs who can harness the power of data to produce cutting-edge insights and competitive advantages.

The staggering flow of data captured by businesses has driven this demand for data-literate finance professionals. So much so that finance teams of the future are just as likely to include data scientists as they are payroll officers.

Must-have data skills

Rowena Burgess

The rapid evolution of technology means few finance or accounting students graduate with the know-how to manage the behemoth that is big data. But data science and analysis skills are the must-haves for today’s CFOs.

For Starbucks CFO Rowena Burgess, the overlap between data and finance has intensified to the point where data skills are critical in any new hire.

“I wouldn’t employ anyone who didn’t have a level of data in their experience,” she said.

“From a finance point of view, they need to at least have an appetite to learn or have been across data whether it’s coding, systems or the actual data.”

The speed of data’s evolution – thanks to the popularity of digitised services and platforms, and the increased risks of data fraud – has meant finance graduates are often learning on the job. It can be a steep learning curve, said Burgess.

“We’ve just had a new person start here and she’s come through the typical path of professional services. I said to her, ‘Here’s SQL manager’ and she said, ‘I don’t know how to code’ and I said, ‘Neither did I, but guess what? We’re in data warehouses and we’re coding’,” she said.

“It’s a skillset that’s not a full time job and it’s not a skillset coming through with finance degrees, but it’s a skillset that lets you connect with others in the business and lets you tell stories with factual information as well as data integrity.”

Not just a way to make more money

By offering discounts or freebies with the use of loyalty cards or apps, businesses can quickly amass a variety of information about their customers from typical purchasing times to average amounts spent.

Other data collection methods include online forms, surveys, point of sale, online tracking through cookies and social media monitoring. Information gleaned from these sources allows businesses to generate a myriad of insights from customer behaviour to product performance.

But data science and analysis aren’t just commercialisation tools.

Al Cranswick

For Al Cranswick, business strategist and data scientist at consultancy firm Business Models Inc., data can be a powerful problem-solver.

“We had a client who was experiencing high turnover rates and we had more than 50 variables associated with each employee going back to their demographic status, their recent performance reviews, tenure in the organisation,” he said.

“If we didn’t have machine learning we could have hypothesised what the causes were and then looked at the data to see if it supported the hypothesis.

“So, what machine learning allows us to do is look at every combination of those 50 variables to see which combinations are most predictive in terms of turnover rates and that generated a whole series of hypothesis that we wouldn’t have thought of just by talking to people.”

As compelling as data insights can be, Cranswick warns finance teams not to rely on them alone. He said there is still an important place for human judgement.

“We also combine conversations and observations in our work because we would never dismiss human insight,” he said. “We embrace human insight and gut feel as part of the decision making process.”

Driving internal improvements

Companies typically leverage data to improve their products or services. But smart businesses will also use it to shine a light on how they operate as an organisation, said Emma Seymour, CFO at cloud-based workforce management and scheduling platform Deputy.

Emma Seymour

“What data does is just empower decision making,” she explained.

“It’s understanding what’s going right, what’s going wrong? It’s kind of the why behind everything.

“So you know really nice and early if things are going and things are not. It gives you an immediate snapshot into the health of the business and what areas you need to go and look at. There’s really no end to it.”

The sheer volume of information reeled in can be overwhelming for some finance teams. The term big data seems somewhat of an understatement when the constant influx of complex information threatens to flood traditional data analytic systems.

In recent years, the role of finance business partner has emerged to harmonise the direction of data between finance and other departments within a business.

The trick, said Seymour, is collecting pertinent data and delegating it to responsible teams.

“It’s a very, very broad scope and that’s why, when you build the data team, you end up having analysts dedicated to different functions,” she said.

“They need to learn the functions and they need to learn what all of those underlying business drivers are so that when you know how the engine works, you know how to make sense of what you need to look at and it becomes a real partnership.”

The next future of data science

For Scott Butterworth, chief data and analytics officer at online property settlement company PEXA and former CFO at law firm Slater and Gordan, data science is more than just statistics.

Scott Butterworth

“Primarily, I am intrigued by how data and analytics can synthesise an understanding of human behaviour and economics to shed new light on how business and society can work more effectively for everyone,” he said.

“The opportunity to utilise PEXA’s unique data enables us to explore how we can create value for both our customers and the broader community.”

Using data, analysts can take a microscopic lens to facets of their business they may have never properly explored. They can better interpret sales patterns, assess investments, define customer profiles and gauge new product performance. Using data to better inform the future is the next frontier of data science, said Butterworth.

“The task in finance teams is moving away from collection and basic reporting of that data, to data analytics and interpreting what the data means,” he said.

“The ability to use data to create forecasts is moving finance teams away from predominantly having a backward reporting view, to instead having a more horizon focused mindset.”

For many companies, data science is a complete game-changer. It not only allows them to become a more agile and cost-effective operation, but drives them to be more competitive.

Savvy CFOs will recognise the importance of sharpening their skills in this increasingly data-driven business landscape.