Applied Data Analytics for Impact

First 30 Days: Build Strong Foundations Aim:  "Stop using Excel like a spreadsheet and start using it like a data tool" What to learn: IF , IFS , TEXT , LEFT , RIGHT , TRIM , LEN Cleaning, structured datasets without breaking formulas. Working with  Excel Tables Structuring data cleanly Reducing manual work Converting ranges to Tables Structured references Basic data cleaning Practice: 1.Take a messy dataset and clean it 2.Separate raw data from analysis 3.Sorting and filtering properly Up to day 60: Analytical Excel Skills Aim: "Start using Excel to analyse data, not just store it." What to learn: Lookup logic: XLOOKUP , INDEX + MATCH Aggregation: SUMIFS , COUNTIFS , AVERAGEIFS Pivot Tables for exploration Conditional formatting for validation Practice: Build KPIs from raw data Validate numbers using multiple methods Use pivots to answer “why” questions Days 61 to 90: Advanced & Professional Use Aim:  " Use Excel like an exper...

 In this article, I explore how data transformation, CRM optimisation, and analytical modelling can meaningfully improve decision making in small organisations. Drawing from applied analytics projects and academic research, I share practical frameworks and technical approaches that others can use when modernising their data ecosystem. Introduction In my work across data analytics projects and academic research, I have repeatedly seen the same issue: organisations collect large volumes of data, but very little of it is structured in a way that supports confident decision making. This challenge is especially common in small and mission driven organisations, where legacy systems, manual processes, and limited analytical capacity create barriers to insight. In this post, I want to share a practical, experience based framework for transforming raw, inconsistent CRM data into clean, reliable, and analysis ready datasets. The aim is to make these methods accessible to others working on si...