How to combine data with business insights?
Posted: Sat Dec 07, 2024 10:09 am
Federico Maffini, Senior Leader at Amazon, talks about why everyone should master financial analysis.
blog-cover-federico-maffini-64c98d364b265483328946-min-64e326036acfc476630960.jpg
Can working with numbers be fun? Yes, says Federico Maffini, a professional data scientist, without hesitation. As Head of Business Management at Amazon Web Services, Maffini leads a large team of business analysts and program managers. A true Amazon veteran, Maffini has spent the last decade working in various teams at the company, including finance, program management, sales, shipping, customer service, and Amazon Web Services.
An effective problem solver, he led cost control for an import business worth over $50 million and worked with leading carriers such as UPS, FedEx, DHL, Hermes and Royal Mail to automate invoicing and improve audit practices.
In our interview, Maffini shares his thoughts on how to improve decision-making by combining data with business insights, why great storytelling can help you stand out at work, and how AI will disrupt financial data analysis.
Why do entrepreneurs and people outside the financial industry need financial analysis skills?
The word “finance” often evokes a certain amount of reluctance. However list of georgia cell phone numbers a solid foundation in financial data and a good understanding of financial mechanics give you an advantage. It allows you to make data-driven business decisions, no matter how far removed from finance they are.
For business owners, assessing the health of their company, identifying growth opportunities, and evaluating investment options are key – and all of this comes from a solid financial background.
Are there any case studies from your career where financial data analysis helped increase company profits?
At Amazon, everyone has the power to act – as if they owned the business.
My experience in finance and other functions gave me the opportunity to lead cost-saving and profit-enhancing initiatives. I helped improve the efficiency of parts of Amazon’s operations and transportation. I evaluated different transportation providers at Amazon and selected the optimal provider based on the type of deliveries and shipments that were to be made. And this was possible thanks to data.
I also refined pricing algorithms that provided optimal selling prices and evaluated pricing models for different customer groups. This is another example of how financial analysis can help improve business profitability.
Amazon pioneered this approach by using data for everything, right?
Amazon is incredibly data-driven across all business units and teams. It doesn’t matter if you’re in finance, marketing, or recruiting. Data is key because it allows you to cut through the clutter. It allows you to eliminate noise, challenge assumptions, and think deeper and broader about problems. And do it in a way that’s defensible and repeatable.
When I started my career, I was certain of one thing: I didn't want to do finance. Of all the subjects I studied at university, this was the one I definitely didn't want to pursue in my career.
But I ended up in finance. When Amazon offered me a job, I decided to give it a try. It was the best decision I could have made at that point in my career because it gave me a solid foundation. Amazon taught me that being good at data makes you versatile. You can use that skill anywhere. If you don’t have it, it takes a lot of investment and time to learn it from scratch, and it gets harder the higher you go.
What is the difference between statistical and business forecasting?
Data is only as good as the insights that go with it. Often, especially in the technical teams I’ve worked with, you see this gap. People are building statistical models and machine learning algorithms, but they lack business understanding.
Data will not help you make decisions if it is not aligned with the needs of the business.
So, as the saying goes, "you give garbage, you get garbage"?
Exactly. It's the same problem that generative AI struggles with. It all comes down to the data you feed it. And distilling the information you need based on your understanding of the business. That takes experience and learning.
What pitfalls should you avoid when analyzing financial data?
Sometimes a lack of understanding of the business leads to selecting data that is incomplete or imprecise. That is why it is important to verify information and thoroughly understand the sources, their biases and assumptions.
Depending on your career stage, there are different pitfalls. Early in your career, I often encounter an inability to compare data with other sources, or basic data checking. Triangulating sources that may not have the same data will help ensure that your data is reliable.
blog-cover-federico-maffini-64c98d364b265483328946-min-64e326036acfc476630960.jpg
Can working with numbers be fun? Yes, says Federico Maffini, a professional data scientist, without hesitation. As Head of Business Management at Amazon Web Services, Maffini leads a large team of business analysts and program managers. A true Amazon veteran, Maffini has spent the last decade working in various teams at the company, including finance, program management, sales, shipping, customer service, and Amazon Web Services.
An effective problem solver, he led cost control for an import business worth over $50 million and worked with leading carriers such as UPS, FedEx, DHL, Hermes and Royal Mail to automate invoicing and improve audit practices.
In our interview, Maffini shares his thoughts on how to improve decision-making by combining data with business insights, why great storytelling can help you stand out at work, and how AI will disrupt financial data analysis.
Why do entrepreneurs and people outside the financial industry need financial analysis skills?
The word “finance” often evokes a certain amount of reluctance. However list of georgia cell phone numbers a solid foundation in financial data and a good understanding of financial mechanics give you an advantage. It allows you to make data-driven business decisions, no matter how far removed from finance they are.
For business owners, assessing the health of their company, identifying growth opportunities, and evaluating investment options are key – and all of this comes from a solid financial background.
Are there any case studies from your career where financial data analysis helped increase company profits?
At Amazon, everyone has the power to act – as if they owned the business.
My experience in finance and other functions gave me the opportunity to lead cost-saving and profit-enhancing initiatives. I helped improve the efficiency of parts of Amazon’s operations and transportation. I evaluated different transportation providers at Amazon and selected the optimal provider based on the type of deliveries and shipments that were to be made. And this was possible thanks to data.
I also refined pricing algorithms that provided optimal selling prices and evaluated pricing models for different customer groups. This is another example of how financial analysis can help improve business profitability.
Amazon pioneered this approach by using data for everything, right?
Amazon is incredibly data-driven across all business units and teams. It doesn’t matter if you’re in finance, marketing, or recruiting. Data is key because it allows you to cut through the clutter. It allows you to eliminate noise, challenge assumptions, and think deeper and broader about problems. And do it in a way that’s defensible and repeatable.
When I started my career, I was certain of one thing: I didn't want to do finance. Of all the subjects I studied at university, this was the one I definitely didn't want to pursue in my career.
But I ended up in finance. When Amazon offered me a job, I decided to give it a try. It was the best decision I could have made at that point in my career because it gave me a solid foundation. Amazon taught me that being good at data makes you versatile. You can use that skill anywhere. If you don’t have it, it takes a lot of investment and time to learn it from scratch, and it gets harder the higher you go.
What is the difference between statistical and business forecasting?
Data is only as good as the insights that go with it. Often, especially in the technical teams I’ve worked with, you see this gap. People are building statistical models and machine learning algorithms, but they lack business understanding.
Data will not help you make decisions if it is not aligned with the needs of the business.
So, as the saying goes, "you give garbage, you get garbage"?
Exactly. It's the same problem that generative AI struggles with. It all comes down to the data you feed it. And distilling the information you need based on your understanding of the business. That takes experience and learning.
What pitfalls should you avoid when analyzing financial data?
Sometimes a lack of understanding of the business leads to selecting data that is incomplete or imprecise. That is why it is important to verify information and thoroughly understand the sources, their biases and assumptions.
Depending on your career stage, there are different pitfalls. Early in your career, I often encounter an inability to compare data with other sources, or basic data checking. Triangulating sources that may not have the same data will help ensure that your data is reliable.