Sample Business Studies Paper on Quantitative Skills

Q 1

Business environment and all spheres of life in general is transforming with the infiltration of information technology. Businesses are increasingly becoming reliant on data and metadata. Therefore, employees with quantitative skills which are increasingly becoming highly valued as they help businesses in data collection and making sense of the data through analysis and presentation. Quantitative skills are critical when it comes to identify patterns in the data. Pattern identification is vital in making decisions, product development, understanding market movements and strategic brand positioning. Through quantitative skills, businesses are able to make future projections in key areas including consumer behavior, investor behavior, movements in the labor markets, global business trends and future government policy trends. These are important micro and macroeconomic factors that influence the success of any business.

Q 2

Qualitative skills will help in my career progression through promotions and accomplishment of new and challenging tasks; a vital step towards gaining the much-needed professional experience. By developing quantitative skills, I believe I can take new career challenges including leadership roles since I will be better placed to make strategic and effective decisions based on verifiable evidence. Evidence-based decision making and strategic realignment of organizational goals and approaches will be on the right path towards excelling in my career.

Q 3

My strongest areas in terms of quantitative skills are data analysis, pattern identification and ability to make projections based on the analyzed data. I have excellent ability to shift through huge amounts of data and use analytical tools to identify critical patterns by highlighting the data outliers using a line of best fit. However, I have a weakness in data collection and presentation. I find the use of narrative to present empirical and sometimes complex data challenging. It sometimes leads to oversimplification and loss of meaning during interpretation of data into a narrative.