Technology Blogs by SAP
Learn how to extend and personalize SAP applications. Follow the SAP technology blog for insights into SAP BTP, ABAP, SAP Analytics Cloud, SAP HANA, and more.
Showing results for 
Search instead for 
Did you mean: 
Product and Topic Expert
Product and Topic Expert

It´s not only about female empowerment, but also about the quality of our software to solve problems.

Our tech industry still has a long way ahead towards a diverse mix of genders in its workforce and leading positions. In order to empower women, we need to invest in an attractive education from early on, in career opportunities and in breaking stereotypes. Women in Data Science does all that. And more. It goes beyond being fair towards women and finally getting the percentages right. We need women in data science to ensure the quality of the software we create to solve problems. Here is why.

Within the SAP ecosystem, we know about the power of software and what problems it can solve. But working with data also bears risks and has an impact on society. By the concept of datafication, social action is quantified into digital data. It´s become natural in our world. We see it in our daily life as by how people track their sports or how they share their love with friends on social media. Organizations use this data in predictive analytics, as part of their sense making process, and to make business decisions on how to develop products or build their relationships with customers.

A majority of society perceives data as neutral and not harmful. José van Dijk (1), a well-known scholar from the University of Amsterdam, calls this phenomenon “dataism” and stresses the trust that is put into institutional agents who collect, analyze, and share the data. Dataism is characterized by a “widespread belief in objective quantification and potential tracking of all kinds of human behavior and sociality through online media technologies”. Being part of the tech industry, collecting data, writing algorithms for predictive analytics, making decisions on how results are displayed for business decisions – it brings us all in a very powerful position and trust is put upon us to act responsibly. “With Great Power Comes Great Responsibility” as Uncle Ben Parker would say.

How can we live up to that responsibility and power? How can we ensure that data collection, analyzation and reporting is neutral and not harmful? We can´t fully. Humans are the ones making the decisions on how data structures and codes are set up. Humans are not neutral; we are always biased with our individual perspective and knowledge of the world. But we can thrive to get closer – by integrating diverse perspectives, experiences, and knowledge to those working with the data and in the decision-making processes.

One example: data science goes as far as measuring emotions. There are scenarios for businesses on understanding customer satisfaction based on social media and customer conversation data. There are even scenarios for automated psychological treatment based on emotions, in easy form, think about relaxation trainings via apps. Data by default will not be able to represent the whole complexity of emotions. Not even psychologists understand all. How could data scientists develop the code and data structures to match reality? What is obvious though, is that one perspective is not enough to even try representing the truth. Or do you believe that the stereotype male data scientist working within experience management solutions will be able to also take female perspective´s on issues he might not know from his world. Like how a woman is impacted on her shopping experience if there are no ladies ‘parking spaces available later in the evening. Will software collect and analyze this experiential data correctly, if no one from her perspective was involved in the set-up of the collection and analyzation process?

We need to integrate different perspectives and backgrounds to ensure the software we are producing represents real-world scenarios and can provide relevant solutions. For that and many other reasons we need more women in data science.

What is your opinion on the matter? What are the issues you find most relevant? I would be happy to see them in the comments! Or if you have a longer thought to share, maybe you can create a blog post yourself and link in the comments?

If this matter interests you, please feel invited to join us virtually for Women in Data Science @ SAP on May 25. Female data scientists will share their expertise and views on the most pressing matters of data science. From analytics to anonymization to data engineering. No matter where you are based, we will have relevant sessions in your time zones. Join if you want to educate yourself, if you want to meet inspiring women and above all, join so that we can implement these perspectives in our data structure and software that can make the world run unbiased and improve all people's lives. Join women in data science around the world!


SAP´s Partnership with Women in Data Science

Stanford University’s Women in Data Science (WiDS) Worldwide aims to inspire and educate data scientists worldwide, regardless of gender, and to support women in the field. SAP has been a proud collaborator and sponsor of WiDS since 2016, on the mission to encourage women and girls to pursue careers in STEM.

With devoted programs from SAP University Alliances and Business Process Intelligence, SAP provides students and academia with access and trainings to business applications. Only as a community, we can develop the next generation of diverse talents – to help the world run better and improve people’s lives.


Related content: Lack of Diversity in Data Science Perpetuates AI Bias, (United States) on March 9, 2022


Reference: (1) van Dijk, J. (2014). Datafiction, dataism and dataveillance: Big Data between scientific paradigm and secular belief. Surveillance & Society, 12(2), pp. 197-208.


Final note: this blog posts concentrates on the role of women in data science, but I would like to acknowledge that bringing in diverse backgrounds is not only about gender.