Graham Collins, FCMA, CGMA, is vice president of finance and economics for one of the world’s largest energy companies. He’s worked there to create new roles that bridge the gap between finance and data science – and he’s set to share lessons from his experience at the AICPA & CIMA Conference on Corporate Finance and Controllers October 27-29 in Las Vegas.
“I’m looking at how we’re transforming the finance function to take advantage of the massive advances we’ve made in digital and digitization, simpler coding languages, and cheaper cloud storage and computing power than in the past” , Collins mentioned. “And that actually opens up a huge opportunity for finance functions.”
His presentation is intended to guide finance professionals on their journey from “Excel to Python” – in other words, to help them adopt new data science solutions that can be more automated and customizable than legacy software.
“What we’re seeing, more and more, is that there are digital solutions that you can actually put on top of your ERP system that actually take that data in a much more powerful direction,” said Collins.
There’s a huge range of new options, from custom coding to powerful database tools. But finance professionals don’t need to become expert programmers or data scientists to succeed in this new environment, Collins said. Much of this expertise will still reside within IT and data science teams.
Instead, he said, “the role we’re particularly trying to evolve is the translator – someone who can translate between business and IT.”
Data translators can help identify a business problem, “then they can work with IT to help identify and design the IT solution,” Collins said. “They understand the language of computer scientists. They understand what code can and cannot do, what data can and cannot do.”
Follow these six strategies to develop data translators in your own team.
Build a broad knowledge base
A translator is not a leading expert in data science. Instead, Collins said, their skills are grounded in finance, but they are broadly familiar with emerging technologies and may have first-hand programming experience.
Collins suggests that experimenting with coding can help build that familiarity for individuals. Self-directed online courses range from introductory to advanced. Progressing through these courses might not make you a master programmer, but it will teach you to speak the language and understand the capabilities of data science.
Collins suggests treating data and programming skills like a musical instrument, something to be developed over time with consistent practice. “You have to keep going with this,” he said. This could include developing home projects, such as automating your personal finances.
As you become familiar with technology, it will become easier to chat and navigate the workplace.
Start with a problem
“Companies have made the mistake of creating entire data science departments because ‘that’s what we need to do,'” Collins said. But it’s a recipe for failure, he says.
A data science effort, no matter how large, is much more likely to succeed if it begins by focusing on a tangible problem.
Some common topics include:
- Automating: If your team frequently exchanges spreadsheet files between multiple people via email, it might be time to consider an automation effort. “As soon as you think about scale – is it going to be used by multiple people on a standard basis? Excel very quickly reaches a limit of what it’s capable of,” Collins said.
- To analyse: Economic modeling can often require careful data collection and collation – a process that can be greatly improved with automated scans. Once a solid foundation is established, companies can also rely on machine learning solutions to provide predictive analytics.
By understanding the capabilities of data science, translators can identify issues and inefficiencies that others miss.
Adopt an experimental mindset
Once you’ve identified a problem, start thinking like a scientist. There will not be a single “correct” solution. Instead, you’re about to explore different possibilities and see what works.
“Data science is all about experimentation. You have to be very well prepared to fail,” Collins said. “You have to recognize that it won’t work the first time, but by doing this you can head in the right direction.”
The translator will not be expected to know or implement all possible solutions, but becoming familiar with emerging technologies, especially through external training and practice, can help you understand the possibilities.
Find your allies
Data science projects often require the cooperation of multiple departments, and a translator can build that agreement.
It starts within the finance team, where Collins suggests looking for people with a “natural affiliation” who can be “allies and champions” for an automation or AI project.
“They may not be coders, but they have some background,” he said, whether it’s a knowledge of statistics or a programming hobby.
The translator also knows how to win support from the “technical side,” Collins said, whether that be data engineers, contract experts or the IT department. Often the resources of these teams are in high demand.
To win their support for a project, Collins said, go back to the original problem that defines the project. Clearly specifying the problem can help convince others that a project won’t be an endless waste of time. Above all, it can help them understand why the project should be a priority.
“When you take a step back, make sure you’re addressing the issues that matter most to the business,” Collins said.
Learn more about data culture
Launching individual projects is just the beginning. As you gain traction, you’ll come across a question familiar to businesses large and small: How will your team handle its new uses for data?
“In some companies, it’s not always clear who owns the data,” Collins said. In other words, it is often unclear who is responsible for managing and maintaining a particular data source. Who will ensure that it is properly formatted, up-to-date and accessible?
The rush to adopt data can become increasingly risky, Collins said, if people start to freely introduce custom software and solutions. Sometimes downloading open source code packages from the internet can open up new cybersecurity vulnerabilities.
“It poses a risk to the business,” he said.
These data culture issues are often best addressed by policies set at the executive level, but anyone working with data has a responsibility to consider them.
“As we all hone and expand our skills, we obviously enter new areas outside of traditional finance,” Collins said. “You may be an exceptional CPA or FP&A analyst, but you’re getting into areas where we don’t have five, 10, 15, 20 years of experience. You have to make sure you’re engaging with the experts. It really has to be a collaborative approach.”
Hire and manage for data
Finally, Collins suggested ways to staff these roles as “translators”. With extreme demand for tech skills in the job market, it may be easier to look in-house.
“In fact, upskilling your own staff is a great way to do that,” he said. Whether it’s hiring or training, look for people who have shown an interest in data tools, including visualization software like Tableau. “These are people who have an analytical mind and a kind of numerical savvy.”
From there, encourage team members to engage in the cycle of learning, experimenting, and collaborating described above. Allow them to try new skills in automation and analytics, and give them permission to fail.
But he added a caveat: “Remember, we’re not trying to make the finance function the IT function. The IT function will always have the data engineers or the data scientists,” he said. . Instead, finance should be looking for people “who add a tool to the tool belt, but it’s to add to their finance qualifications, the finance skills they have”, he said. declared.
And when hiring is needed for these new data-driven roles, Collins said a hiring plan is mandatory. Simply advertising a data-related job is unlikely to work, given the competitive market.
Instead, he suggested specifically identifying universities and building relationships, seeking out those candidates rooted in finance but savvy about data. “You have to go the extra mile,” he said. “Just advertising on the web probably won’t get you the quality of people you want.”
— Andrew Kenny is a freelance writer based in the United States. To comment on this article or suggest an idea for another article, contact Neil Amato, a FM editor-in-chief of the magazine, [email protected].