There are discussions about the importance of diversity in literature and in the media and minimizing gaps between minorities and majorities. In order to see if a community is making progress in minimizing these gaps and to measure success, there is an interest in being able to predict the diversity of communities given currently prevailing. There are well-designed data forecasting algorithms in data science using large data sets. However, diversity data has only been collected over the last few decades. This paper adopts algorithms formulated by Grey and ARIMA (Auto-Regressive Integrated Moving Average), using small data to predict the likely diversity of a cohort for a time in the near future. Our results demonstrate there is more confident forecasting for “country of birth”, but in terms of predicting linguistic and religious diversity, due to the changeable nature of these factors throughout an individual’s life, we would require further data to make any accurate prediction.

There are discussions about the importance of diversity in literature and in the media and minimizing gaps between minorities and majorities. In order to see if a community is making progress in minimizing these gaps and to measure success, there is an interest in being able to predict the diversity of communities given currently prevailing. There are well-designed data forecasting algorithms in data science using large data sets. However, diversity data has only been collected over the last few decades. This paper adopts algorithms formulated by Grey and ARIMA (Auto-Regressive Integrated Moving Average), using small data to predict the likely diversity of a cohort for a time in the near future. Our results demonstrate there is more confident forecasting for “country of birth”, but in terms of predicting linguistic and religious diversity, due to the changeable nature of these factors throughout an individual’s life, we would require further data to make any accurate prediction.

For diversity to serve as a competitive advantage and for companies to be successful, the diversity of their workforce should be synchronized with the community they are doing business in. This degree of similarity in diversity of an organisation to its customer base is termed as Mutuality. High synchronization builds a healthy relationship between an organisation and its customers leads to better customer service, thus, radically improve their business performance. However, at present, this concept of diversity & mutuality has been analytically neglected and most organisations in both the public and private sectors are grappling with this aspect. Thus, the goal of this research is to design an empirical formula for the Mutuality Index by using the method of cosine similarity which captures the orientation (the angle) of one attribute of diversity in an organisation with its customer base and determines the similarity between them. The research explains how this data-driven approach can help organisations become better at measuring, understanding, tracking and delivering more informed and better diversity strategies which eventually enhances their business performance and induces a stronger bottom line (Profits)