Lyndsey Weber, a seasoned expert in financial and supply chain modeling join us this week for a lively conversatio on both financial and supply chain modeling. She recounts her experience with some of the worst models she has encountered, the lessons learned, and the importance of organization and design in creating effective models. Lyndsey shares her journey from ExxonMobil to Quantrix, offering a deep dive into the art and science of financial modeling.

Key takeaways from this week's episode include:

Having a well-defined structure and approach is crucial for creating and maintaining effective financial models. Poorly organized models can lead to significant challenges and inefficiencies.

A horror story about inheriting a model filled with bad practices, including hidden reference cells and broken VBA code. The experience teaches her the value of starting over and building a model from scratch with best practices.

The importance of understanding stakeholder needs, both emotional and practical, and designing models that cater to these needs. This approach helps in building models that are not only functional but also user-friendly.

The benefits of using Quantrix over traditional Excel, highlighting features like always-on pivoting, natural language formulas, and role-based access, which enhance efficiency and collaboration.

One of Lyndsey's key pieces of advice is to prototype models on paper before building them. Mapping out inputs, outputs, and key calculations in plain English helps in creating a clear and effective model structure.

In today’s episode, Paul engages in a conversation with Carl Seidman to discuss why mastering presentation skills, and understanding the needs of the audience is crucial for excelling in your FP&A career.

Carl is a premier FP&A and Fractional CFO expert, providing financial advisory and leadership development services to top companies including Deloitte, Verizon, and Walmart. Carl is known for his impactful training programs and advisory work that drive success in financial planning and analysis.

Key takeaways from this week's episode include: :

Here is a concise summary of the key points from the discussion:

In FP&A, the ability to distill complex information into a concise, impactful message is paramount. It's not about how much you know, but what you can communicate effectively.

The ability to distill complex information into a concise, impactful message is paramount. It's not about how much you know, but what you can communicate effectively.

FP&A isn't just about numbers, it's about storytelling with data, painting a vivid picture of the financial landscape that guides strategic decisions and drives organizational success

Anticipating potential disruptions or changes in the business environment is a fundamental aspect of FP&A. By identifying risks and developing strategies to mitigate them, FP&A professionals can help the organization stay ahead and adapt effectively.

In this episode, expect to learn:

Excel's flexibility is both its greatest strength and weakness. While it allows for complex modeling, this same flexibility can lead to chaotic and error-prone spreadsheets if not managed properly.

Today's Excel is vastly different from the Excel of a decade ago. With features like Dynamic Arrays, Power Query, and the integration of Python, Excel has become a powerful tool for modern data analysis and financial modeling.

Python’s capabilities in handling time series analysis, data visualization, and complex computations complement Excel’s functionalities, making it a valuable skill for modelers.

Excel excels in prototyping and wireframing due to its immediate data visibility and user-friendly interface. This makes it ideal for quickly iterating models and understanding data before moving on to more complex tools when necessary.

George shares advice on how to get started learning Python and other analytical tools to take your analysis and modeling to new heights.

A core principle for George is the importance of sharing what you know about Excel and data analytics. He emphasizes building a culture of knowledge sharing to foster growth and efficiency within teams.

In this episode, expect to learn:

• Raphael emphasizes the critical need for regular audits of financial models to prevent errors and ensure accuracy. He shares a horror story about a model that wasn't audited for five years, leading to significant financial discrepancies.

• While Excel is known for its flexibility, Raphael discusses the trade-offs of using more structured tools like Causal, which can help reduce errors and provide more insightful analysis through its object-oriented approach and natural language variables.

• The importance of integrating operational data with financial models to provide actionable insights. He stresses that understanding business operations is crucial for building effective and useful models.

• Raphael shares his journey from using traditional Excel spreadsheets to adopting Causal, a modern financial modeling tool. He explains how Causal's dynamic dashboarding, probability distribution modeling, and seamless integration with other systems offer significant advantages over Excel.

• The discussion focuses on the potential of AI in financial modeling, Raphael notes that while AI can build models, the real question is whether it should. He also touches on the importance of human judgment and expertise in interpreting and using these models effectively.

In this episode, expect to learn:

• A distinction between those who use spreadsheets casually and those who build models as a core discipline. He emphasizes the importance of creating models that are understandable and usable by others, akin to the difference between a sketch artist and an architect.

• Intellectual curiosity, an almost obsessive desire to solve problems, and the ability to bring structure out of chaos are highlighted as key traits for successful financial modelers. Brian stresses that these traits are more critical than technical skills alone.

• One of the profound insights shared is the importance of simplifying complex problems. Brian believes that the true measure of a modeler’s intellect is their ability to present complex ideas in a straightforward and understandable manner.

• Financial modeling is described as a deeply creative exercise. Brian compares it to art, where each model reflects the modeler’s thought process and experiences, much like an artist’s painting.

• Brian shares his experience transitioning from being a modeler to managing a team. He discusses the challenges of letting go and empowering others, and the iterative learning process involved in both building and reviewing models.

Welcome to a special episode of our podcast! Today, we're thrilled to bring you an exciting Podswap. Stay tuned for an engaging conversation you won't want to miss. Here are the show notes from our previous episodes.

Prepare to be captivated as hosts Paul Barnhurst and Glenn Hopper welcome Sasha Orloff, CEO of Puzzle, to uncover the technological advancements reshaping the world of finance and accounting. In this eye-opening episode, you'll discover:

- How generative AI is seamlessly integrating into everyday software, automating tasks and delivering data-driven insights.

- The rise of "AI agents" in 2024 - advanced systems designed to autonomously process complex problems and provide tailored solutions.

- Sasha's visionary project, Puzzle, modernizing accounting software with real-time, flexible financial data processing.

- The transformative potential of blockchain technology to enhance transparency and traceability of financial transactions.

- The future of finance reimagined through AI and predictive analytics, empowering more precise forecasting and strategic decision-making.

In this episode, expect to learn:

• Use dynamic arrays to reduce the number of formulas and potential error points. Clearly separate inputs, calculations, and outputs into distinct sheets. This enhances clarity and ease of use.

• Ensure uniform column and row structures across sheets. This consistency minimizes errors and simplifies formula management, making models easier to audit and understand.

• Mixing traditional Excel formulas with dynamic arrays reduces the benefits. A complete commitment ensures better performance and error reduction.

• Using graphs and charts helps quickly identify trends and anomalies, making it easier to ensure realistic assumptions and accurate models.

• Establishes a solid foundation, ensuring you learn best practices early on, which prevents the development of bad habits and enhances overall modeling skills.

• Implementing dynamic arrays can drastically reduce the file size and complexity of models, making them more efficient and manageable.

• Issues like mixed calculations and unrealistic assumptions emphasize the importance of thorough checks and realistic inputs in financial modeling.

In this episode, expect to learn:

• Organize workbooks with clear, logical structures. Separate inputs, calculations, and outputs into distinct sheets. Use consistent naming conventions and separator sheets to enhance clarity and ease of use.

• Maintaining a consistent design throughout the workbook. Use uniform column and row structures across sheets. Consistency minimizes errors and simplifies formula management, making models easier to audit and understand.

• Formal training in financial modeling is crucial. It establishes a solid foundation, ensuring you learn best practices early on, which prevents the development of bad habits and enhances overall modeling skills.

• Real-life horror stories highlight common errors, such as improper data copying and pasting. Ensuring consistent data formats and understanding the intricacies of your calculations are essential to avoid costly mistakes.

• Use Macros for repetitive tasks like generating output sheets, not for complex calculations. They help automate processes and reduce manual errors but should be applied judiciously to maintain model integrity.

• Implement data validation to restrict inputs. This minimizes user errors by ensuring only valid data entries are made, such as positive numbers or specific date ranges, enhancing the reliability of your models.

• Regularly test models to ensure accuracy. Use master check sheets to consolidate checks and quickly identify errors. This proactive approach helps maintain the model’s integrity and reliability over time.

In this episode, expect to learn:

• An early career encounter with a poorly designed financial model, filled with hard-coded numbers, underscores the necessity for creating flexible and dynamic financial models that can adapt to changing business needs.

• The discussion highlights the critical need to avoid hardcoded data and external links in financial models, which can lead to rigidity and errors.

• Insights from the financial modeling newsletter and courses show a wide range of professionals benefiting, from newcomers to seasoned directors. This variety demonstrates the widespread need for improved modeling skills.

• The discussion focuses on the importance of transforming data into actionable insights. It's stressed that without the ability to analyze and interpret data effectively, its value is minimal.

• The conversation sheds light on techniques for deriving actionable insights from complex data, crucial for any financial modeler. It also discusses the value of formal certifications like the Advanced Financial Modeler Certification as tools for professional development and skill validation.

In this episode, expect to learn:

• An early career encounter with a poorly designed financial model, filled with hard-coded numbers, underscores the necessity for creating flexible and dynamic financial models that can adapt to changing business needs.

• The discussion highlights the critical need to avoid hardcoded data and external links in financial models, which can lead to rigidity and errors.

• Insights from the financial modeling newsletter and courses show a wide range of professionals benefiting, from newcomers to seasoned directors. This variety demonstrates the widespread need for improved modeling skills.

• The discussion focuses on the importance of transforming data into actionable insights. It's stressed that without the ability to analyze and interpret data effectively, its value is minimal.

• The conversation sheds light on techniques for deriving actionable insights from complex data, crucial for any financial modeler. It also discusses the value of formal certifications like the Advanced Financial Modeler Certification as tools for professional development and skill validation.

In this episode, expect to learn:

• An unconventional entry into investment banking at 43, highlighting how the background in education enriches the approach to financial analysis and modeling, emphasizing the crossover skills between teaching and financial analysis.

• This highlights how teaching skills are transferable and beneficial in the financial analysis and modeling sectors.

• An anecdote about a significant error in a financial model, which failed to detect a $29 billion discrepancy, underscoring the critical importance of rigorous validation and diagnostic checks in financial modeling.

• The role of behavioral factors in financial modeling, adopting a stance of professional skepticism and the need for understanding and questioning the assumptions underlying a model before trusting its output.

• The importance of simplicity and planning in financial modeling, the most effective models are those that are straightforward, well-planned, and can communicate complex financial insights in an accessible manner.

• Strategies for mitigating bias, such as incorporating different perspectives and rigorous testing of hypotheses