Lead Global Performance Analysis Manager

Building dynamic scenario models, writing basic VBA macros for automation, and using Power Query for complex data ingestion and transformation. You'll also be teaching advanced techniques to your junior team members.

Writing complex multi-table joins, subqueries, and window functions to extract and manipulate data. You'll also be profiling and debugging data quality issues directly in the database, ensuring your team's data is sound.

Building complex, interactive dashboards from scratch, incorporating parameters, level-of-detail (LOD) calculations, and custom data sources. Your focus will be on data storytelling and ensuring the visualisations clearly communicate key insights to executives.

Designing compelling 'decks' from a blank page, crafting a clear narrative for C-suite presentations. You'll also lead complex problem-solving sessions using Miro frameworks (like issue trees) with your team and stakeholders.

Navigating these platforms to independently find and validate source data for your projects. You'll need to understand the underlying business processes that generate the data (e.g., Order-to-Cash in SAP) to ensure accuracy.

Building and modifying models within these platforms to support financial and operational planning cycles for your projects. You'll be using them to test scenarios and integrate your analysis into the wider planning process.

• Heads of Business Units (e.g., Sales, Marketing, Operations)
• Finance Leadership (CFO, Finance Directors)
• Product Management Leads
• Data Engineering and IT Teams
• Senior Leadership Team (SLT) members for project updates

• Industry bodies for benchmarking data
• External research firms (occasionally for market insights)
• Technology vendors for new analytical tools

As projects grow in complexity and data volumes increase, manual data preparation becomes a bottleneck. You'll need to move beyond simple scripts to orchestrate complex data pipelines, ensuring your team spends less time on data wrangling and more on analysis. This is about building repeatable, scalable solutions.

ETL/ELT Principles · Workflow Orchestration Tools (e.g., Airflow, Prefect) · Cloud Data Warehousing (e.g., Snowflake, BigQuery) · Data Quality Monitoring

• This week: Identify one recurring data preparation task in your team that takes more than 2 hours weekly.
• This month: Research a simple Python library (e.g., `pandas`) or a low-code tool to automate that task.
• Month 2: Successfully automate that task and document the time savings for your team.
• Month 3: Explore how to schedule your automated script using a basic cron job or a simple cloud function.

Quick win: Automate a weekly data refresh in Excel using Power Query and document the time saved. It's a small win, but it builds momentum.

Business units are increasingly asking for forward-looking insights, not just historical reporting. While you're not a Data Scientist, understanding and applying more sophisticated predictive techniques will allow you to deliver higher-value insights and better anticipate future trends. This means moving beyond simple averages.

Time Series Analysis (e.g., ARIMA, Prophet) · Classification & Regression Trees (e.g., Decision Trees, Random Forests) · Model Evaluation Metrics (e.g., RMSE, MAE, R-squared) · Feature Engineering for Predictive Models

• This week: Find a free online course or tutorial on basic time series forecasting (e.g., using Python's `Prophet` library).
• This month: Apply a basic time series model to one of your existing datasets (e.g., sales data) and compare its forecast to simple averages.
• Month 2: Experiment with different features or external variables to improve your model's predictive power.
• Month 3: Present your findings to your manager, highlighting the benefits of more sophisticated forecasting.

Quick win: Use Excel's built-in forecasting functions (if available) on a simple dataset and understand its limitations compared to more advanced methods.