Python Series

The Reporting Paradox Finance teams often find themselves caught in a frustrating paradox: they invest enormous effort into producing detailed reports that executives barely read. A survey by Financial Executives International found that finance departments spend up to 30% of their time on report preparation, yet 65% of executives admit to reading only summaries or conclusions (FEI, 2023). As David Parmenter observes in “Winning CFOs: Implementing and Applying Better Practices” (2012), “Most finance teams are producing a monthly financial pack that is more like a doorstop than a decision support tool.” This disconnect wastes valuable resources and misses the critical opportunity for finance to influence decision-making. ...

Post 4: Core Data Structures Welcome to the fourth post in my Python learning journey. So far, we’ve installed Python, set up a development environment, and explored the basic syntax. Now it’s time to dive deeper into Python’s core data structures; the building blocks you’ll use to organise and manipulate data in your programs. In this post, we’ll cover: Lists: Python’s versatile sequence type Tuples: Immutable collections Dictionaries: Key-value mapping Sets: Unique value collections Choosing the right data structure I’ve found these data structures similar to concepts we use every day; lists are like columns in spreadsheets, dictionaries resemble lookup tables, and sets are perfect for tracking unique items like account codes. ...

The KPI Crisis in Modern Business Most organisations are drowning in metrics while starving for insight. According to research by MIT Sloan Management Review, over 70% of executives believe their KPI systems don’t provide the strategic guidance they need (Neely & Bourne, 2020). This disconnect illustrates a fundamental problem: despite the prevalence of performance measurement systems, few organisations have metrics that genuinely drive improvement. As David Parmenter explains in his seminal work “Key Performance Indicators: Developing, Implementing, and Using Winning KPIs” (2015), “Most organisations have been measuring the wrong things in the wrong way.” The result is what he calls “measurement dysfunction”—where metrics create perverse incentives, drive suboptimal behaviors, or simply fail to influence performance at all. ...

Post 3: Python Syntax Fundamentals & Language Features Welcome to the third post in my Python learning journey. In the first two posts, we installed Python and set up a proper development environment. Now it’s time to dive into the language itself. This post covers the fundamental building blocks of Python code that I’ve been learning. We’ll explore: Variables and basic data types Operators and expressions Control flow with conditionals and loops List comprehensions and lambdas Iterators and generators Error handling with try/except This post is a bit longer than the previous ones, but these fundamentals form the foundation of everything else in Python, so it’s worth taking the time to understand them. ...

The Stereotype vs. The New Reality For decades, finance professionals—particularly accountants and controllers—have battled the “bean counter” stereotype: detail-oriented, historically focused, compliance-driven, and somewhat disconnected from the operational realities of the business. This perception, while increasingly outdated, persists in many organisations. As David Parmenter notes in his book “The Leading-Edge Manager’s Guide to Success” (2011), “Yesterday’s finance team was renowned for producing financial information that was too late, too detailed, and not focused on what matters to the management team.” This observation captures the fundamental challenge that modern financial controllers must overcome. ...