The Hidden Cost of Vanity KPIs: Why Data Dividends Protect More Than Just Privacy

After researching data governance recently, I started thinking about how organizations can embed it into both systems and culture. Not just to build trust, but to make it easier to recognize when a system is broken or outdated.

Hear Me Out: What If We Got Paid For the Data We Share?

It’s not like corporations aren’t already extracting it for free. Take job applications, for example. Resumes are incredibly data-rich, companies can store that information in massive databases that become valuable over time.The system has normalized large-scale data extraction without direct compensation. So why shouldn’t the data we share carry a price tag? It clearly has economic value.

Data as Labour, Not Capital

When data is treated as capital inside organizations, it encourages hoarding and leads to bloated dashboards tracking dozens of metrics. More data starts to look like more value, even when it isn’t.

However, if data were treated as labor, the people generating it (clicks, uploads, location signals, etc.) would be seen more like workers contributing active effort. There would be an immediate expectation of compensation, rights, or bargaining power.

Can Data Monetization Change Organizational Behaviour?

There have actually been real-world economic experiments tracking what happens when you monetize data within an organization. Research on privacy and data monetization shows that data has a measurable economic value, and organizations change their behaviour when data is treated as an asset with an actual price.

This makes intuitive sense. When data carries a price, organizations are forced to justify what they collect and track. Employees can no longer rely on vanity KPIs that are disconnected from actual outcomes.

We’ve all read the stories (or lived them) of teams tracking dozens of meaningless metrics, when in reality, only 4 or 5 actually move the needle and generate revenue. The rest is just "good to know" noise.

So Why Do These Broken Systems Persist?

The hard part isn’t designing the system. It’s maintaining it when ambiguity is more convenient than clarity.

Complex KPI systems often persist because they diffuse accountability and protect the status quo. In these environments, influence can shift toward those who are skilled at navigating metrics and internal politics rather than those closest to real impact.

For neurodivergent individuals in particular, this can be exhausting. You may end up over-investing effort into fixing systems that others are incentivized to leave unchanged, only to be made to feel like the problem lies with your interpretation rather than the system itself.

When a whole room full of people pretends a useless KPI is a raging success, or that a broken process is perfectly efficient, it can feel like a living version of the Asch conformity experiment: you’re forced to deny your own objective reality just to fit into the group.

Trust Is Built in Systems, Not Just in Relationships

Organizational research explicitly distinguishes between interpersonal trust (who you know, how smoothly you communicate) and system-based trust, which is rooted in clear rules, processes, and institutional safeguards. Many scholars argue that sustainable trust in modern organizations depends as much on processes, contracts, and institutions as it does on personal chemistry.

Of course, relationships matter—especially when it comes to negotiation, alignment, and building buy‑in. But trust should never depend solely on how well you communicate or how confident you sound.

Shifting toward an objective, priced‑data framework grounds system‑based trust in economic reality. In theory, this could also reduce the advantage of purely social or political fluency and reward actual contribution more directly. This could be good news for neurodivergent people who prove their worth through work, not through social performance.

This also raises a question for future research: how would explicit data valuation reshape the design and prioritization of organizational KPIs and decision-making frameworks?

Just like the insights and data analysis I create and share for free 😉 (though maybe I should start invoicing for it!).

References:

Allen-Hardisty, L. (2020, February 13). Levels of trust in workplace relationships: The starting point for building a trust plan. Queen’s IRC. https://irc.queensu.ca

Arrieta-Ibarra, I., Goff, L., Jiménez Hernández, D., Lanier, J., & Weyl, E. G. (2018, February 21). Should we treat data as labor? Let’s open up the discussion. https://www.brookings.edu/articles/should-we-treat-data-as-labor-lets-open-up-the-discussion/

Canayaz, M., Kantorovitch, I., & Mihet, R. (2021, December 15). Privacy laws and value of personal data (Swiss Finance Institute Research Paper No. 21-92). Swiss Finance Institute. https://ssrn.com/abstract=3986562

Cruz, B. (2026). Data governance to data sovereign. Infogram. https://infogram.com/data-governance-to-data-sovereign-1h0r6rzoyg53w4e

Stilwell, D. (n.d.). Trust as a systemic structure in our organizations. Retrieved May 23, 2026, from https://thesystemsthinker.com/trust-as-a-systemic-structure-in-our-organizations/

Zanini, M. T. F., & Migueles, C. P. (2014). Trust as an element of informal coordination and its relationship with organizational performance. EconomiA, 15(1), 1–22. https://doi.org/10.1016/j.econ.2013.08.005