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...

  Introduction Despite the rise of modern analytics tools, Excel remains one of the most widely used tools in data analysis. What separates an average Excel user from a highly effective data analyst is not the number of functions they know, but how they structure data, solve problems, and support decision-making. In this post, I share a practical approach to building advanced Excel skills that reflect real-world analytics work rather than exam-style knowledge. Why Excel Still Matters for Data Analysts Excel is often the first tool used to: explore unfamiliar datasets validate assumptions perform quick analyses communicate insights to non-technical stakeholders Understanding how to use Excel well improves analytical thinking, regardless of the tools used later. Thinking in Tables, Not Worksheets Experienced analysts treat Excel as a structured data tool, not a canvas. Key habits include: using Excel Tables consistently keeping raw data separate from analy...

 Introduction Many analytics issues are not caused by complex models or incorrect logic. They come from quiet data quality failures that go unnoticed until results are questioned. Missing values, duplicate records, unexpected spikes, or invalid dates can all distort insights. When these checks rely on manual review, they are inconsistent and easy to forget. This is why analysts need automated data quality checks , even for simple datasets. When data quality checks are informal or ad hoc: dashboards lose credibility analysts spend time firefighting instead of analysing errors propagate into forecasts and models trust in analytics declines Automation turns data quality from a reactive task into a governed process . It ensures that datasets meet basic standards before they are used for reporting or decision making. In CRM and similar analytical domains, this consistency is critical. Intermediate technical explanation: what data quality really means At an analy...

  Introduction Dashboards often receive the most attention in analytics projects, but the quality of insights depends far more on the underlying data model than on visual design. Poor modelling choices can lead to misleading KPIs, inconsistent metrics, and a lack of trust in reporting. In this post, I explore how raw CRM data can be transformed into meaningful KPIs through thoughtful data modelling decisions. The focus is on structure, clarity, and alignment with real decision making needs rather than tool specific features. Why KPIs Fail Before They Reach Dashboards Many KPI issues originate long before reporting begins. Common causes include: Ambiguous metric definitions Inconsistent grain across tables Mixing transactional and aggregated data Unclear relationships between entities KPIs derived from poorly structured fields When these issues exist, even well designed dashboards struggle to provide reliable insight. Understanding Raw CRM Data Structure CRM...

Introduction CRM data is often collected over long periods of time across different systems, teams, and processes. As a result, datasets tend to contain inconsistencies, duplicates, missing values, and legacy fields that make analysis unreliable. Manual fixes may work in the short term, but they rarely scale or provide confidence in long term reporting. In this post, I explore how a Python based ETL pipeline can be used to create repeatable, transparent, and reliable data preparation workflows for CRM data. Rather than focusing on tools alone, the emphasis is on structure, validation, and design choices that support analytics and decision making. Common CRM Data Challenges Before designing an ETL pipeline, it is important to understand the types of issues commonly found in CRM datasets: Duplicate or partially duplicated records Inconsistent date and text formats Missing or incomplete key fields Legacy columns no longer in active use Manual data entry errors These ...

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...