Businesses and Individuals all over the world and surprisingly in Ghana (and the rest of Africa), generate a lot of data as part of their daily business and social life. These data generated over a long period of time tell a story that is hard to be untrue. The truth assumption in the long-term data is based on the fact that, the entities (individuals and businesses) are not aware that they are writing these stories and hence there is no motivation to put out false stories over a long time. (this does not mean all data are true). It must be stated that all these data are of business relevance depending on how the data is analysed and for what purpose it is to be used. This explains why facebook data was important to Cambridge Analytica.

Accountants find themselves practicing in different functions within the value chains of companies in different sectors as cost accountants, management accountants, financial analysts, credits analysts, valuation experts, strategists, Finance business partners, finance and economics researchers etc. All these functions require the use of data to inform Business Strategies and actions. In fact, I can comfortably say that data is the friend of every Accountant or Finance Professional.

Therefore, as Accountants and financial analyst or finance professionals as a whole, we need to understand the processes, methods and tools for analysing data for the purpose of creating value for our organisations.

In my few years of working as a data analysts either as part of my academic work, professional career (as a research analyst and a Finance Business Partner in a bank), or as a hobby and as part of my Continuous Professional Development (CPD) exercises, I have come to the conclusion that, for finance professionals and in deed for all social scientist, we need to follow the following steps to understand and find the hidden stories in data (as nicely listed by Chandoo.org).

  1. Collect and clean data
  2. Structure data
  3. Model (business) problems
  4. Analyse data
  5. Visualize the results
  6. Find connections
  7. Add layers of complexities to the problem
  8. Build what if scenarios
  9. Reach conclusions
  10. Take Action

At any level of this data analysis process, a good analyst must have at the figure tips certain analytical techniques and tools available to make these very easy. It is the aim of this writer to make known to you some of these tools as a finance professional to make you confident in your practice over the series of writings and demonstrating videos that will follow this article. For the purpose of this lecture series, Microsoft excel will be employed. However, interested readers and viewers can request for the same procedures in other tools like SPSS, R – programming, Python etc.

Pre-requisite for this series is a simple personal computer, installed Microsoft excel and your time. No fees required, no centralized meeting point.

To ensure effective use of time and resources, the main lectures will start posting after we get 50 interested readers indicating below that they are interested and it will be worthwhile. Please add your comments and expectations from the course.

Next post will be on introduction to data collection and cleaning.