Home TechnologyCoding The Role of Diversity and Inclusion in the Technical Sector

The Role of Diversity and Inclusion in the Technical Sector

Using data science to explore gender (in)equality in the technical sector.

by Ivan
Diversity and Inclusion
Gender inequality

Diversity and inclusion are an emergent and significant concern for many organisations across the world.

Studies show that the three most important workplace needs are sociability with colleagues, opportunity to grow, and work-life balance. This article showcases that these needs are not facilitated equally across genders, and that important aspects of inclusivity for women, particularly those returning to work (after maternity leave, and supporting their career while starting a family), people with young children, and those requiring flexibility in their commute are not being recognised — a trend that would otherwise be associated with happier and more productive workers, and characterised as a way forward to promoting inclusion and diversity strategies within organisations.

Diversity and inclusion are an emergent and significant concern for many organisations across the world (Ferdman & Deane, 2014). Through the promotion of workplace diversity and inclusion policies, organisations have been able to reap rewards such as competitive advantage, superior business performance, employee satisfaction and loyalty, a strengthened relationship with multicultural communities, and attracting the best and brightest candidates (McCuiston, Wooldridge, & Pierce, 2004). Organisations which take active steps toward achieving a culture of diversity and inclusion have seen both a boost in employee engagement and productivity, as well as increased customer loyalty (Salesforce, 2017).

On the other hand, failure to promote inclusive practices results in decreased productivity, job satisfaction, and employee retention (Agócs & Burr, 1996). Research suggests that omission of diversity and inclusion in the workforce result in discriminatory perceptions. This has impacts upon the business’ reputation and ability to attract and retain a skilled workforce, as well as a diverse client base, affecting organisations economically (Moss, 1991).

Of relevance to the data sciences practice, diversity facilitates increased creativity in problem solving and better decision-making, which leads to higher economic performance (Bourke, Dillon, Giles, & Barry, 2011). Practices that consider the unique needs of their clients and users are thought to have more effective client communication, facilitate greater levels of empowerment from project stakeholders, and deliver service outcomes which more comprehensively address client and user needs (Hurst, 2000; Spinuzzi, 2005). The profession regards this consideration as a matter of professional responsiveness and responsibility (Collier, 2006; Gifford, 2015).

Job Design

Agócs & Burr (1996) note that the current workforce has been designed by and for staff who are white, male, able-bodied and Christian, and supported by a full-time domestic worker (a wife). This sits in stark contrast with the contemporary Australian demographic; the 2011 Australian Census underscores the increasing cultural diversity in Australia’s population, with over 30% of Australians born overseas and over 46% of the population having at least one parent born abroad (ABS, 2011). Traditional employment practices remain mostly unchanged (Agócs & Burr, 1996), and thus do not reflect the increasing diversity evidenced in the Australian employee landscape. As Australia has progressed to having increased multicultural dimensions, jobs need to be designed and/or refined to cater for the needs and aspirations of a diverse society (Bertone & Leahy, 2001).

Within technical industries, research shows that the field suffers from gender discrimination by: limiting work experience and advancement in opportunities; being inflexible to personal and family commitments; and offering poor work-life balance, which includes long work hours and lack of returnee training.

Barriers and limitations

Within the Australian context, research highlights that the concept of managing diversity is not well understood or appreciated (Bouten-Pinto, 2016). Organisations often display an inability to recognise an operational or strategic advantage added by implementing a diversity plan (Bouten-Pinto, 2015). This is particularly evident in senior management, where lack of understanding of the effectiveness of a diverse workforce is common, and further exacerbated when human resource managers are not involved (Davis, Frolova, & Callahan, 2016).

Diversity management in Australia typically falls to human resource departments to take ownership and agency, manage, support and enhance a diverse workforce (Strachan, Burgess, & French, 2009). Organisations without a human resources department are left without a governing body within the organisation to facilitate diversity and inclusion, and a disparate set of responsibilities falling to staff in a disjointed and poorly integrated fashion (Bouten-Pinto, 2015)

Organisations which then elect to implement diversity plans do so in an ad-hoc basis, where choices are often influenced by organisations’ biased culture and values (Strachan et al., 2009). Given that the culture and work model of technical practices is male-dominated, organisations are at risk of leaders failing to examine biases and consider the possibilities of diversity (Bouten-Pinto, 2015).

Data review, exploratory analysis and visualisation

This article will review Kaggle’s third annual Machine Learning and Data Science Survey.

For the purposes of the exploratory data analysis I have used the file
multiple_choice_responses.csv, and have selected the following features, as they were deemend most relevant for this exploration:

  • What is your age?
  • What is your gender?
  • In which country do you currently reside?
  • What is the highest level of formal education that you have attained or plan to attain?
  • Select the title most similar to your current role
  • Does your current employer incorporate machine learning methods into their business?
  • What is your current yearly compensation (approximate $USD)?

Reading the file:

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns sns.set_style('whitegrid') df=pd.read_csv('multiple_choice_responses.csv',low_memory=False) df = df[['Q1','Q2','Q3','Q4','Q5','Q6','Q7','Q10']] df.columns = ['age','gender','country','education','job title','company Size','data Scientists','salary'] df = df.drop(df.index[0]) df.head()

Visualising distributions

 plt.figure(figsize=(15,6)) sns.countplot(df['gender'],palette='coolwarm',order=df['gender'].value_counts().index) plt.savefig('output.png')

A similar correlation and gender disparity emerges when considering age and gender.

plt.figure(figsize=(15,6)) sns.countplot(df['gender'],palette='coolwarm',order=df['gender'].value_counts().index) plt.savefig('output.png',dpi=400)

This trend continues when visualising education, career growth, and disperity in salary.

plt.figure(figsize=(15,6)) sns.countplot(df['education'],palette='coolwarm',order=df['education'].value_counts().index) plt.xticks(rotation=30,ha='right') plt.savefig('education.png',dpi=400) plt.figure(figsize=(15,6)) sns.countplot(df['education'],order=df['education'].value_counts().index,hue=df['gender']) plt.xticks(rotation=30,ha='right') plt.savefig('education_gender.png',dpi=400) plt.figure(figsize=(15,6)) sns.countplot(df['job title'],order=df['job title'].value_counts().index) plt.xticks(rotation=30,ha='right') plt.savefig('job_title.png',dpi=400) plt.figure(figsize=(15,6)) sns.countplot(df['job title'],order=df['job title'].value_counts().index,hue=df['gender']) plt.xticks(rotation=30,ha='right') plt.legend(loc='upper right') plt.savefig('job_title_gender.png',dpi=400) plt.figure(figsize=(15,6)) sns.countplot(df['salary'],order=df['salary'].value_counts().index) plt.xticks(rotation=30,ha='right') plt.savefig('salary.png',dpi=400) plt.figure(figsize=(15,6)) sns.countplot(df['salary'],order=df['salary'].value_counts().index,hue=df['gender']) plt.xticks(rotation=30,ha='right') plt.legend(loc='upper right') plt.savefig('salary_gender.png',dpi=400)

Conclusion

Kaggle’s research highlights that organisations suffer from lack of diversity and inclusion . This article underscores the potential value of enhanced diversity and inclusion, including: increased employee satisfaction and loyalty; attracting the best and brightest candidates; strengthened relationships with multicultural communities; increased growth, creative thinking and innovation; enhanced competitive advantage; and superior business performance (Finkelstein, 2017; McCuiston et al., 2004).

In particular, job design can be redeveloped in order to facilitate meaningful diversity and inclusion policies and procedures that : (1) are rooted in organisations’ culture, demographic and employee’s experiences, (2) are co-produced with employees to ensure greater commitment and empowerment, effective job design and enhanced personal motivation, and (3) facilitates ongoing reflective practice and review of procedures, systems and their measurements, ensuring a dynamic approach is employed when measuring objectives to ensure that diversity and inclusion grows and develops with the organisation, and that diversity and inclusion objectives are being achieved as anticipated.

Data sciences should be a reflection of the society that it researches. This means we need to recruit, retain and promote employees who can respond to the different needs and values of all sections of the community. In the past, engineers were drawn from a narrow sector of society, but now it is essential we ensure that the profession represents every demographic, social and cultural background.

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