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Why Cultivating A “Data-Driven” Culture Is Worthless

Why Cultivating a Data-Driven Culture is Worthless

They say if you torture data enough, you can get it to confess to anything. 

Everyone wants to be data driven. To cultivate a data-driven culture, understand statistics, make better decisions, and, ultimately, improve their bottom line. “Data-driven” is the hot, new thing; it is the “synergy” of the 2020s. And as a person obsessed with data, I get it. It is my job to love and work with and understand data – and to get others to do the same! So why am I telling you that your organization’s quest for a data-driven culture is worthless? 

Because in isolation, it is. 

Data doesn’t lie. Or does it?

To be “data-driven” means progress is compelled by data rather than personal experience or opinion. However, in the right hands (or the wrong hands), you can easily come up with conclusions first and then craft your data to fit. You can flip the script and drive your data to fit your culture, find statistics to suit your preconceived outcomes, and generally abuse the numbers until they support your narrative. 

Data alone is not enough. You can understand the math, flawlessly execute your analysis, and communicate your results, but without first considering ethics, you are missing an essential part of the equation. Utilizing data without ethics is like driving a car without the brakes – you may initially have a really fun ride, but it is not going to end well. 

The goal is objectivity

As much as we would all love to believe that data is the highest form of unbiased truth, it is necessarily the product of human intervention. Although we continually strive for objectivity, data is impacted by the philosophies, prejudices, and purposes of each person who interacts with it. Whether it be through data collection design, cleaning, analysis, or interpretation, the meaning of data is always influenced by our impartialities. 


We cannot and should not completely disregard data, so our question becomes: what does cultivating a culture of ethical data-driven decisions look like? 


At the organizational level, creating an ethical, data-driven culture looks like clarification of company goals and values. This means not only referencing the importance of ethical actions on your website or briefly bringing them up in performance reviews, but also embodying the principles you hope to see in your organization. When these values are purposefully articulated, clarified, and acted on by those in the C-suite, it promotes adoption and adherence company-wide. 

Foster a data-driven culture built on ethics

Fostering this ethical, data-driven culture also means having frequent discussions about the implications of your work, products, and data usage – conversations that emphasize facts, credibility, and responsibility over opinion and blame-shifting. The scope of these conversations should include clarity of consent when collecting data, the purpose and utilization of data, and how the utilization of this data could impact vulnerable or protected groups. Furthermore, it means lending time and support in developing and considering alternatives and promoting open conversation when evaluating possible strengths and weaknesses. 

When you reflect on past projects, was candid conversation valued? Were there discussions around sample selection, adverse impact, and representation? Was there opportunity for unpunished honesty, dissent, and sincere concerns? While these discussions can be uncomfortable, they allow for ethical review, exploration of alternatives, and the development and evaluation of the rationale behind analysis and decisions. 

On a personal level, infusing ethics into your data-driven culture may mean focused self-reflection regarding biases, assumptions, and gaps in knowledge. We all have unconscious biases. Everyone is fallible. However, we can only begin to confront these problems when we intentionally bring awareness to them. Looking critically into personal biases and knowledge gaps concerning data collection, utilization, and understanding allows us to foster an environment where not knowing or making a mistake is cause for purposeful reflection and inquiry. Everyone will make errors; ethical organizations will address these errors instead of disregarding or concealing them. These personal and organizational weaknesses can then encourage careful consideration, growth, and development, rather than shame, punishment, or obfuscation. 

Clean, organized, and accurate…and representative sampling

When it comes to the data itself, ethical utilization emphasizes the importance of clean, organized data and accurate, representative sampling. It neither undervalues the process of data engineering and data wrangling nor does it value expediency over accuracy. Questions and critique are welcome. Seeking out subject experts, furthering education, and asking for help is encouraged. Every step of the project is considered against best practices. Garbage in, garbage out  and ethical data usage means we throw out the garbage data; we do not reuse or recycle. 

data-driven culture


Lastly, even after decisions are made and products are developed, an ethically data-driven company continually monitors the data input, analysis, results, and utilization — looking for issues, mistakes, and prospects for improvement. This critical analysis allows for the opportunity to resolve issues and improve processes, as well as to feel confident in the data quality and ethical standards of your organization.

Create a culture of curiosity and objectivity

Unethical data usage can lead to false conclusions, squandered resources, and potential liability. Being “data-driven” means nothing if you neither know who is doing the driving nor the methods they are using to steer the car. The meaning we derive from data is subject to the interpretation that we place upon it, and as such, it is affected by our beliefs. Data in isolation can be deceptive! It takes a culture of curiosity and objectivity within ethical boundaries to fully utilize data, leading to more effective decisions and successful outcomes. 

Infuse behavioral data into every talent process to create a personalized approach to talent management! Learn more. 

Brittany Krynicki is a Data Scientist with TalentQuest. She has a background in research and analytics and enjoys making data exciting, understandable, and accessible. Brittany graduated with honors from Berry College and Kennesaw State University, holds certifications in data science through IBM, and is currently pursuing her master’s in data science from the University of Colorado Boulder.

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