Sign In

Can Data Ever Tell the Truth?

2–3 minutes

It’s often said that data-driven results are factual, accurate, and unbiased. But is that really true? Let’s explore whether data itself can ever be truly objective and, by extension, whether anything derived from it can be.

Examples of different professions claiming to be objective and fair based on their usage of data and information. Who can we trust?

Can Content Ever Be Objective?

Defining content ultimately depends on the user’s perspective. Transforming content into alternative forms requires applying preconceptions to interpret it and expertise to reshape it into something more useful.

From a user's perspective content loses objectivity as usability increases. This is due to content transformations requiring the application of preconceptions.

This brings us to the first hard truth: data can never be truly objective unless it lists every possible preconception and each one, rather paradoxically, aligns with our subjective idea of objectivity. This is impossible, as even the simplest dataset could hold infinite potential preconceptions.

Data can't be objective due to the requirement of using preconceptions in the transformation process that aren't listed alongside the data itself.

This leads to an even harder truth: any information derived from data rests on even more unlisted preconceptions introduced during the transformation process.

Information isn't objective either since it requires applying even more preconceptions when transforming from data. Facts, truths, insights are all shaped by preconceptions.

How Content Moves and Changes Form

In practical terms, as content becomes more usable, its objectivity decreases since transformation requires additional preconceptions. These added layers make it structurally more complex, even if it seems simpler to the user, and therefore less readily available.

As content becomes more usable, objectivity declines due to the use of preconceptions in the transformation process. This corresponds to increasing complexity of the content which reduces its availability. we should aim to require the least complex content possible.

Harnessing Content Dynamics

Advanata leverages these dynamics to increase the availability of suitable content. The key insight is that simpler problems rely on simpler content, which is easier to obtain and also happens to be more objective.

Intuition is that simple problems require content with less preconceptions compared to complex problems. Advanata deconstructs complex problems into a set of simple problems. Thus, instead of requiring content with more preconceptions we require a set of content with less preconceptions.

The following example shows how a typical data-focused analytics problem can be greatly simplified by first defining the problem’s scope and what needs to be solved, rather than rushing into data gathering for an open-ended problem.

A dean of a teacher's college requests a complex data set. The response is that they should first find out what they really need in order to determine the actual problem then solve suing Advanata. As shown, the problem can be simplified based on actual need, thus reducing the required preconceptions and can be solved using Advanta.

Don’t Trust Claims of Objectivity

Data is never truly objective, nor is the information derived from it; that is simply the nature of content dynamics. The key is to simplify problems to reduce preconceptions, thus increasing potential content availability, and to involve problem experts in a structured way throughout the process in order to help navigate biases and assumptions and reach the best possible solutions.

Sign up to get the latest articles and product updates.

Ready to see Advanata in action? Explore more resources, read related articles, and access the full application at advanata.com.

Want a personalized walkthrough? Contact info@advanata.com to get application access and schedule a demo.

Leave a comment