Applied Data Analytics for Impact

  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...