Applied Data Analytics for Impact

 Introduction CRM datasets are rarely analysis ready. They often contain duplicated records, inconsistent text fields, missing values, and dates stored in multiple formats. While tools like Power BI and Excel can handle some cleaning, analysts frequently face a point where repeatable, scalable data preparation is required. This is where Python becomes essential. The challenge isn’t just cleaning data once. It’s designing a process that works reliably as new CRM data arrives. Poor data quality directly impacts: customer counts segmentation accuracy campaign performance metrics downstream modelling and forecasting If cleaning logic lives only in ad hoc steps or manual fixes, errors reappear quietly over time. A Python based approach allows analysts to formalise assumptions, document decisions, and reproduce results consistently. In CRM analytics, this reliability is foundational. Intermediate technical explanation: how to think about CRM data cleaning Before...

 Introduction CRM data is rarely clean by default. Records are entered by different teams, updated at different times, and stored across multiple tables that were never designed for analytics. As a result, analysts often struggle with inconsistent metrics, confusing filters, and dashboards that break as soon as requirements change. In most cases, the root issue isn’t the visuals or the calculations. It’s the data model underneath . Why this problem matters A poorly designed data model leads to: Double counted customers KPIs that change unexpectedly when filters are applied Complex DAX written just to “fix” modelling issues Dashboards that are hard to maintain or scale In CRM analytics, where insights often drive engagement strategy, segmentation, and forecasting, unreliable numbers quickly erode trust. A clean data model acts as the foundation that keeps analytics consistent, explainable, and reusable. Modelling concept Fact vs Dimension thinking A reliable...