DataAnalysis

Impactful applications of IoT are certain to emerge in the near future. We explore how finance professionals should think of the value it can generate for their business by creating a thought framework.

Share buyback schemes, if executed poorly, can be disastrous for shareholders and company stakeholders. Using three examples from the UK market, reasons for failure are explored with some suggested remedies for managers making capital allocation decisions

Share buybacks have become a popular financial tool for cash-rich businesses that are running short on sufficient projects to invest in. Three case studies are presented here that demonstrate when share buyback schemes are successes.

The Python language has the functionality to consign many a VBA-filled spreadsheet, held together with sticky tape, to the recycle bin. Its ease of use and suitability for iterative development lends well to finance-based workflows. This article delves into this and provides a brief tutorial.

Hidden in Excel and Power BI is a powerful data analysis pack named Get and Transform (previously known as Power Query). It is an ETL pack for cleaning and sorting raw data from a range of input sources, such as CSV and text files. This tutorial demonstrates how Get & Transform can help the data analyst both save time and uncover more insight.

Budgeting can be a drawn-out process, yet oftentimes once the year starts and results diverge, they become forgotten. Instead of budgeting just with qualitative methods, managers should combine them with quantitative tools. Comparing regressed historic trends with future expectations can be more accurate and ultimately ensure the budget survives the year.

Finding out your return on investment from a project can become a subjective process with the myriad of returns measures and formulas that exist. This overview provides a breakdown and explanation of what type of return you need to measure and how to calculate it. With further guidance on how to use returns data to guide objective decision making.

Eyeballing the top-line figures of company growth is unlikely to uncover anything particular radical. To understand the trends that lead to user growth, retention, and/or engagement, statistical tools should be employed to test the underlying drivers behind performance.

To unlock growth in a company, you must first find the KPI that is core towards increasing company value. More effective knowledge of growth comes from a deeper understanding of the characteristics of a "perfect client," the trends between user cohorts, and finding the tipping point that solidifies retention and engagement.