Workplace diversity ⁠⁠ — pivotal for unbiased analytics

By definition, Data is information in raw or unorganized form (such as alphabets, numbers, or symbols). It is a set of quantitative or qualitative variables. If it’s just quantitative and qualitative information then, why is data considered so important today? Why is data considered “the oil of the 21st century?”

Data in essence is just “information: numbers, words or symbols”. What makes it so essential in today’s era is, it’s power to give insightful results, reports, analytical results, etc. that acts as (if not sole) one of the most essential decision drivers in most business today. Data have enabled organizations all over the globe to make evidence-based decisions. But even though analytics is done by building models be it ⁠ — predictive, descriptive or prescriptive, all these models are built by humans and then run by machines thus, exposing them to: biases.

Automation through Machine learning algorithms and data driven-decision making, has eliminated “biases” to a large extent but they exist even today. There’s really no possible way to completely eliminate bias in analytics or data science, the “human touch” will continue exposing analytics and our decisions to biases. Hence, we need to combine analytical results operated through machine learning models with our understanding and comprehension skills to work against such biases.

To eliminate biases and obtain universally applicable solutions we require more diverse and inclusive environments. Maintaining these two things is critical for data, analytics and tech in general. Diversity here doesn’t just imply gender diversity. It also means diversity irrespective of an individual’s ethnicity, religion, age, national origin, cultural heritage, sexual orientation, disability, and size or shape.

Stephen Covey said: “Strength lies in differences, not in similarities.”
We lack diversity in thoughts, skills, experience, gender, etc. And, to deliver seamless, relevant and high-value experiences that our clients expect, we need teams that are able to understand those client’s needs and perspectives. We need diverse workforce to understand their requirements in a true sense and act upon them.

Organizations can reinforce workplace diversity and have a more inclusive environment by taking a some of these actions:

  • Ensuring representation of diverse talent. Doing this by focusing on advancing diverse talent into different executive, technical, management and board roles.
  • Strengthening leadership accountability and capability. Companies should place their leaders and board members at the heart of their “diversity & inclusion” efforts. They should make these leaders responsible and accountable for proper development of inclusive measures within the organization. Additionally, these leaders need to strengthen inclusive leadership capabilities among their managers and executives.
  • Enable equality of opportunity through fairness and transparency. In pursuit of ‘true’ meritocracy, companies should ensure that each employee has a fair chance and access to advancement and opportunity. This can be done by depolying analytical tools which will ensure ‘unbiased’ results or less biased as compared to human decision making process. Doing so will result in more transparency and fairness in promotion, overall hiring and pay processes.
  • Lastly, organizations need to start thinking out-of-the box. By not getting fixated at any specific things is how individuals and organizations move forward and make progress. One of the most common things that sets organization back is focusing on candidates with specific educational qualifications. There needs to be more diversity with degree requirements. For instance, most tech companies look for people with majors in Computer Science, Information Systems, Mathematics, Data science, analytics, economics, and so on. The companies need to look beyond these majors. Like, statistically, about 50% of biomedical engineering students are women and they have the ‘ability to code’. So, why not consider them for CS, data scientists, or data analysts roles?

The future for analytics is promising with some of the enhancements being integrated today and others on the horizon. Analytics is becoming a significant driving force behind almost every business there is. And, with the growing scope of analytics it is important to keep an eye on biases in our algorithms and data-driven decisions. Any solution can be better, faster and cheaper if we have diversity of thought and opinions. Lastly, it’s critical to remember that analytics is a team pursuit ~ a diverse team pursuit.

PS: We are built with biases which generally are there to help us survive and make sense of our surroundings. But, it’s crucial especially at workplaces to disconfirm those biases and constantly check and recheck our lenses to work in the most effective way and, to have results that are free of biases.

Data Analytics and Cloud technologies — Ernst & Young | Master’s in Data Analytics | WomenTech Awards | Medium: AI & Data

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Simran Yadav

Simran Yadav

Data Analytics and Cloud technologies — Ernst & Young | Master’s in Data Analytics | WomenTech Awards | Medium: AI & Data

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