Is your company speaking the same Language?
- Angelika Klidas
- Feb 2
- 5 min read
In the world of data and analytics, we are super proud, how we work with precision, clarity, and objectivity. We mostly speak universal languages of SQL, Python, R, architectures, within our department with our own jargon, or we even talk through (data)visualizations. But what if the most significant language barrier in our field isn't technical at all? What if it's woven into the very words we use every day, creating invisible hurdles for women (and even men) and a costly disconnect between our data teams and business leaders?
Discussions with amazing groups like “Women Who Qlik”, Women in Tech and the Dutch initiated group “Women in AI”, panel sessions and research have brought a critical issue to the forefront: the subtle, yet powerful, impact of language in the workplace. I’m not talking about language that is received emotionally resulting in hurt feelings or even using language to be politically correct. It's about language for talent acquisition, performance evaluation, working in siloed organizations and the bottom-line effectiveness of our data initiatives. As I’ve learned over the last decades speaking the same language so that we can understand each other is actually very difficult and challenging. However, I find there is another layer as we also experience gender-coded issues within organizations.
The coded language that holds women back
While reading the book Invisible Women from Carolina Cruado Perez and related articles, it highlights a phenomenon of gender-coded language that quietly shapes our perceptions. Men are often described as people who take action and take the lead, or they are mentioned as "dominant," "confident," and "competitive", words we frequently associate with leadership. In contrast however, women are more often described with shared terms, such as "helpful," "warm," and "interpersonal."
Although these experiences are positive, they often don’t lead to chances to take on leadership roles. Studies & research showed that women were praised for being helpful, but "taking charge" was the behavior that was truly valued and rewarded. When women did take charge, they risked being labeled as "bossy" or "aggressive”, a classical double message. This language bias is a direct consequence of a world where leadership has been historically defined by male centric data.
This coded language even impacts who walks through the door when you apply for a job. Job descriptions in male-dominated fields like finance and tech are often filled with assertive language, which might accidentally stop qualified women from applying. By simply rephrasing job postings to be more inclusive, organizations can significantly widen their talent pool. One occurrence stood out as I was reading the book “Invisible Women from Caroline Cruado Perez which was a story about a commander of the fire brigade in the U.S.A., that really blew my mind.
What I learned was that the change from “fireman” to “firefighter” was meant to include women, but because workplaces, equipment, standards, and culture stayed male‑focused, it led to resistance, practical problems, and sometimes made inequality more visible instead of solving it. However, the purpose of this change was to reach a state of gender-neutral language and encourage more women to join the fire brigade, this amazing opportunity led to various problems and resistance. This occurred within the fire bigrade as well as outside the fire bigrade!
This example from the book Invisible Women took place in the U.S.A. but the conversations about gender-neutrality in the fire brigade organizations is internationally there. As well in the U.S.A. as in the United Kingdom change led to controversy, resistance and cultural issues, but thank God, also to more awareness and inclusion.
The business cost of a divided language
But the language gap extends beyond gender. Technical and business teams are drifting apart, and it’s hurting our ability to really work with data. A survey found that while 76% of companies consider themselves data-driven, but 39% of business users do not actually understand what that means.
Data teams speak of dimensions, measures and sometimes of ETL pipelines or data-products, while business leaders want to know about revenue growth and market share. The result? The average time for a data request can be as long as four weeks or more, an eternity in today's business world. This is why a common language, a business language, is mega important!
From my opinion, when things get misunderstood, it wastes time and money, slows down projects, and we miss out on useful insights from our data. To bridge this gap, we need to move beyond raw data and embrace data storytelling, using clear, compelling narratives and visualizations to communicate the business impact of our findings.
The "Invisible Women" effect in AI
The problem becomes more serious in the age of AI. Algorithms are only as good as the data they're trained on, and historically, that data has been massively male. Did you ever searched on Google for “the greatest leaders of all time”? Well how many women are in that list? This is the what is called "Invisible Women" effect: a systemic failure to collect data on women that leads to biased AI. Did you know that in offices around the world, the temperature is based upon a white male of 40 years old? That is one of the reasons that women are most of the time “cold” within office environments.
Another example, voice recognition algorithms are up to 70% less likely to accurately understand female voices because they are trained on predominantly male data sets and there are many more examples to mention. As we increasingly rely on AI for everything, from hiring employees to medical diagnosis, this data gap isn't just a flaw; it.s a significant threat to fairness and equitiy let alone correctness and effectiveness.
How to build a more “inclusive and effective” vocabulary
“It’s about bridging the gap between diverse worldviews, traditions, and ways of thinking”(https://www.talaera.com/culture/9-signs-of-cross-cultural-communication-challenges-in-your-team/)
So, how do we move forward? The journey to a more inclusive and effective workplace begins with a conscious effort to change our language. We as individuals can start from here and help our organizations to grow in a more inclusive and effective world.
Here are some actionable strategies that can be used:
Strategy | For individuals | For organizations |
Language use | Use gender-neutral terms like "team" or "everyone" instead of "guys." Ask for and respect personal words. | Create a company-wide style guide for inclusive language. Audit job descriptions and internal documents for coded language. |
Performance feedback | When you give feedback, its important to focus on specific, observable behaviors and their business impact, not on personality behavior or characteristics. | Implement clear, consistent criteria for evaluations to reduce manager judgement and unconscious bias. Train managers on how to give actionable, unbiased feedback. |
Bridging the data gap | Practice data storytelling. Translate technical findings into clear business outcomes. | Establish a shared vocabulary or a centralized glossary of key terms. Create cross-functional teams to act as a "translation layer" between technical and business units. |
Awareness & education | Be aware of your own communication style and how it might be perceived by others. | Invest in unconscious bias and cross-cultural communication training. Foster a culture where open and respectful dialogue is encouraged. |
Ultimately, closing the language gap is not just a diversity and inclusion initiative; it's a business imperative. By becoming more mindful of the words we use, we can unlock the full potential of our teams, build more effective data cultures, and create a more equitable and innovative future for the data and analytics field.
Hope you’ve enjoyed my first article in 2026.
Let’s start our new journey where we have a world that has less bias, and more inclusiveness and equality.
Angelika Klidas
Advisory & Data & AI Literacy trainer & Data Voyager
Business Data Challengers (the Netherlands)
Releated links/books
This article was created based on insights from articles by Christine Ro, Amelia Reigstad, and research on inclusive language and the gender data gap and the book Invisible Women by Caroline Criado Perez.
Invisible Women by Caroline Criado Perez, her book is available via: https://www.amazon.nl/Invisible-Women-Exposing-Designed-bestseller/dp/1784706280
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