Is number-crunching fun? Yes, responds without hesitation Federico Maffini, a professional number cruncher. As Head of Business Management at Amazon Web Services, he leads a large team of business analysts and programme managers. By all means an Amazon veteran, Maffini has worked over the last decade within several teams across Amazon, including Finance, Programme Management, Sales, Transportation, Customer Experience and Amazon Web Services.
A natural problem solver, he has led the cost controllership of a $50M+ import business and partnered with leading carriers like UPS, FedEx, DHL, Hermes and Royal Mail to automate invoicing and improve auditing practices.
In the following paragraphs, Federico shares with ELVTR his thoughts on how to marry data and business insights to improve decision-making, why great storytelling can help you stand out as an employee, and how AI will disrupt financial data analysis.
Why should entrepreneurs and non-finance people in general master financial analysis?
There's always a bit of repulsion when people hear the word “finance”. But being grounded in financial data and having a good understanding of the financial mechanisms gives you an edge. It allows you to be more data-driven when it comes to business decisions, no matter how far removed from finance they might be.
For business owners, assessing their business’ health, identifying growth opportunities, evaluating investment options, is key. That comes from a sound financial background.
Any examples from your career where financial data analysis helped your employer increase profits?
At Amazon, everyone's empowered, like being the owner of your own business.
Through my experience within finance teams and other functions, I've had the opportunity to drive cost-saving and profit-improvement initiatives. For instance, I've contributed to improving the efficiency of parts of Amazon's operations and transportation. I was evaluating different transportation service providers at Amazon and identified the optimal one, based on the type of deliveries and shipments that needed to happen. And that was possible because of the data.
It’s important to understand the concepts and techniques of financial analysis to assess the size of the pie, and potentially the price. And that's a transferable skill. It doesn't matter where you work.
I have also fine-tuned pricing algorithms to ensure the optimal selling price and assess the best pricing model, based on customer cohorts. This is another example of how financial analytics can help improve business profitability.
Amazon has been a pioneer of this approach, using data for everything, right?
Amazon is incredibly data-driven across all business units and teams. It doesn't really matter if you're in finance, marketing or recruitment. Data is paramount, because it allows you to cut through the clutter. It allows you to remove the noise, challenge assumptions and think deeper and bigger about problems. And all that in a way that you can defend and reiterate.
When I started my career, I was sure about one thing: I did not want to do finance. Of all the subjects I had studied at university, that's definitely the one I didn't want to pursue in my career.
I ended up in finance anyway. Given the opportunity came from Amazon, I decided to give it a try. That was the best decision I could have made at that stage of my career, because it gave me that grounding.
Amazon has taught me that data literacy makes you versatile. You can use that skill anywhere. If you don't have it, building it from scratch requires a lot of investment and time, and becomes harder the more senior you get..
In your course you will be talking about statistical and business forecasting. What's the difference?
We will indeed draw the difference between a purely data-driven statistical or analytical forecasting model and business-driven forecasts.
Data is only as good as the insights that go with it. Oftentimes, especially with technical teams I've worked with, you see that gap. People are building statistical models and machine learning algorithms, but they lack business understanding.
Data does not help inform the decision-making if it is not tailored to the business’s needs.
During the course, we will go through the process of building a forecast model in Excel that will be driven by business inputs. And that differs from purely statistical models.
Is that the famous “garbage in, garbage out” problem?
Exactly. That’s the same problem generative AI has. It goes back to the data you feed into it. And distilling the information you need, based on your business understanding. That requires experience and training.
What are the main pitfalls you have to avoid when doing financial data analysis?
Sometimes, not understanding the business well leads you to select data that's incomplete or not accurate. So cross-checking things and understanding sources well, their biases and the caveats behind assumptions, is important.
There are different pitfalls depending on the stage of your career too. One thing that happens earlier in one's career is the inability to cross-check numbers with other sources, basically doing a sanity check. Triangulating sources that might not have the same data can help you ensure that your data is reliable.
Later on, as one gets more comfortable with data, what becomes more important is understanding the business concepts, which is often something lost. Analysts often miss the forest for the trees. They get too deep into the data, and they miss the strategic insights.
Why is storytelling important for financial analysis?
For any professional that has to communicate upwards, storytelling is important. If the leadership and senior stakeholders are data-driven, it's easier. But not everyone is like that. It's on you to break down your analysis or recommendations in a way that appeals to them.
Too often, financial analysts are too close to their numbers to understand this. And they fail to make the most out of their work because they can’t relate information back to the audience.
It’s even worse with developers! But is it an inherent talent or a skill that can be learned?
You're leaving something on the table, which is the result of your hard work.
Now, everything is a talent. Someone's particularly good at Excel, Python, mathematics, creative thinking, etc... Others are good at storytelling. But there are techniques you can learn to go around that and build your strategy around storytelling.
You don't have to be a movie director or an accomplished author to be a storyteller. Anyone can be and anyone should be a storyteller. We'll go through that in the course.
Do you have any examples of how storytelling helped you throughout your career?
It helps me every day, for example when working with my leadership, which has a very wide span of control: from sales operations, sales strategy, executive communications and sales planning to customer optimisation and enablement.
Being able to break the problem down in a way that relates to their problems, helps you get the point across. Over the years, that has helped me get funding for my teams, initiatives approved, support for experiments and pilots, even when outcomes were uncertain. If you find ways to make an idea appealing to your audience, you have a way in. It opens a door.
And also to get the green light for pilot projects whose outcome was uncertain. If you can find ways to make an idea appealing to your audience, that is a way in, it opens a door.
