Data based on one person's opinion is the most subjective in FAIR risk analysis.

Outline you can skim:

• What “subjective data” means in risk work

• The four data types in the question and why they differ

• Why data based on one person’s opinion is the most subjective

• How FAIR-style risk analysis handles subjectivity (triangulation, documentation, transparency)

• Quick real-world examples to keep it grounded

• A gentle takeaway: how to talk about data quality without losing momentum

FAIR, subjectivity, and the human side of risk

Let me ask you something: when you hear “data,” do you think cold numbers, or do you imagine stories, impressions, and personal takes? In the world of risk analysis, data lives on a spectrum. Some data is like a well-lit street—clear, observable, and verifiable. Other data is more like a foggy alley—dependent on who’s looking, what they’ve experienced, and how they’re feeling when they share it. That’s where subjectivity shows up, and in FAIR—Factor Analysis of Information Risk—that distinction matters a lot.

Here’s the thing about the question you’re likely to encounter: which type of data is most subjective in nature? The correct answer is Data based on one person’s opinion. It may sound like a simple quiz item, but it’s tapping into a core reality of risk work: when a single mind provides input, the chances of bias shading the view spike. It’s not that one person’s opinion is worthless—far from it. Expert judgment is a valuable piece of the puzzle. It’s just that one point of view tends to reflect that person’s experiences, assumptions, and blind spots. And in risk, those biases can tilt estimates if they’re not checked.

Subjective vs objective data in the FAIR framework

To ground this a little, think about the four data types in the question:

• Data based on facts (A). This is the most objective kind. Think patch counts, incident timestamps, system configurations, or logs showing when a vulnerability was exploited. Facts come with a timestamp and a source you can verify. They’re the backbone of a defensible risk model because you can point to something observable and repeatable.

• Data that helps inform the estimate of risk (B). This sits in the middle. It blends evidence with judgment. For example, a scenario description that estimates potential losses or frequencies often combines data points from multiple sources, plus some expert interpretation about how those elements fit together. It’s not purely objective or purely subjective; it’s a synthesis.

• Data gathered by multiple interviews (C). This is a smart middle-ground approach. When you collect perspectives from several people—risk owners, security practitioners, operators—you reduce the chance that a single person’s bias dominates. You still need to weigh the inputs, look for convergence or disagreement, and document how you reconcile differences.

• Data based on one person’s opinion (D). This is the one that’s most susceptible to subjectivity. It’s the lone voice, the lone interpretation. It can illuminate nuances others miss, sure, but it can also carry personal bias, selective memory, or an overly optimistic/pessimistic stance.

Why one person’s opinion stands out

Think of it like this: objectivity improves when you can cross-check a claim against independent evidence. When data rests on one mind, you lose that cross-check. The same person might remember details differently, interpret risk through the lens of their department, or assume that yesterday’s incidents look like tomorrow’s. In risk terms, you’re more prone to confirmation bias, anchoring, or simply the way a single experience colors everything that follows.

That’s not a horror story; it’s a practical reminder. In FAIR analyses, we don’t pretend to abolish subjectivity. We manage it. We acknowledge it. We capture it in the model with clear documentation, transparent assumptions, and a plan to test what happens if the inputs shift.

How to manage subjectivity without dulling the insight

Here are some practical ideas that keep the human edge in risk work while reducing blind spots:

• Triangulation. Bring in multiple data sources whenever possible. If a single opinion is essential, pair it with objective facts and with input from other stakeholders. The goal isn’t to erase perspective; it’s to balance it with evidence.

• Document assumptions. If you’re relying on a judgment or estimate, write down what you’re assuming, why you’re assuming it, and what would cause you to change your mind. This creates a trail you or someone else can follow later.

• Use structured elicitation. When expert opinion is needed, use a formal method for gathering it—like a guided interview protocol or a calibrated scoring exercise. Structure helps reduce scatter and keeps the discussion focused.

• Seek counterpoints. Actively look for data that might contradict a favored view. It’s easy to defend a position you like; it’s harder to defend a position you’ve tested against its potential counterpoints.

• Weight inputs by confidence. It’s okay to say, “This data point is high confidence; this other input is exploratory.” Expressing confidence levels helps the model reflect real uncertainty.

• Separate data collection from decision making. Distinguish facts from interpretations, and then show how each feeds the risk picture. People trust analysis more when they can see where numbers end and judgments begin.

• Use scenarios and sensitivity analysis. Play out different possibilities to see how risk estimates shift. If a single opinion could tilt the results, a sensitivity check can reveal how robust the conclusion is.

A quick, grounded example

Let’s anchor this with a concrete, everyday scenario (keeping it simple on purpose). Suppose a mid-sized company is evaluating the risk of a data breach affecting customer records.

• Facts (A): The company has 1,200 customer records exposed in a prior incident, with a known compromised patch level and a measurable breach timeline. This is verifiable data.

• Data that informs risk (B): Analysts estimate potential loss from a breach using historical loss data, regulatory fines ranges, and the business’s revenue. They blend those inputs with an understanding of the company’s customer mix and data sensitivity.

• Data from multiple interviews (C): Security, IT operations, and legal teams each share their perspectives on likely attack vectors and containment costs. The inputs are diverse, and where they disagree, the team documents the points of divergence.

• Data from one person’s opinion (D): A single security lead offers a scenario stating, “In our experience, this kind of incident would cost about X,” based on what they’ve seen in a couple of shops. If kept unchallenged, that view could unduly shape the risk estimate unless you test it against richer evidence.

In a FAIR approach, the last input isn’t discarded, but it isn’t allowed to stand alone. It’s weighed against facts, corroborated inputs, and other expertise. The result is a more nuanced risk picture, not a single line on a chart that sounds confident but isn’t.

Beyond the quiz: what makes this topic useful

You might wonder why this matters beyond grading or exam-style questions. The truth is, data quality shapes every decision in risk management. When you understand where data comes from and how it might bias outcomes, you become better at communicating risk to stakeholders. You can explain why a given number is trustworthy, or why a scenario needs more evidence. You can justify budget requests, policies, or controls with a transparent trail of inputs and assumptions.

And yes, there’s a human angle here. People make risk calls every day—risk owners, cybersecurity analysts, executives, and auditors. It’s natural to lean on expertise. The key is to combine that expertise with verifiable data and a clear method for handling uncertainty. That blend is what FAIR aims for: a disciplined way to translate fuzzy human insight into actionable, defendable risk assessments.

Relatable digressions—the part that helps you remember

If you’ve ever planned a vacation with friends, you’ve seen this dynamic in action. Some people want a hard itinerary with exact times; others prefer a loose plan with room to wander. The sunlit beach you all imagine becomes a mosaic of memories, preferences, and compromises. In risk work, the same mosaic becomes a model. The “subjective” pieces—the personal anecdotes, the hunches—aren’t junk; they’re signals. You just need to map them, tag them, and test how they influence the bigger picture.

Or think about a sports analogy. A coach might lean on a veteran player’s read of the opponent. That input can be incredibly insightful, but if the coach only listens to that one view, plans might miss other tactics the team should consider. In risk analysis, the solution isn’t to throw away the veteran’s perspective; it’s to pair it with scouting reports, opponent trends, and even weather conditions that could affect the game. The best teams use all that information to build a stronger strategy.

Putting it into words that land

As you work with FAIR-style thinking, you’ll notice two things: the data landscape is diverse, and your job is to weave it into a coherent narrative. You’ll speak in probabilities, not absolutes. You’ll acknowledge the limits of what you can claim, and you’ll spell out what would shift the numbers if new facts came in. That honesty is the backbone of credible risk communication.

If you’re studying this material or simply curious about risk in information environments, here are some practical takeaways to keep handy:

• Always ask where a data point comes from and what it implies about reliability.

• Favor data triangulation—facts plus corroborated inputs from several sources.

• Document judgments and the rationale behind them; be explicit about uncertainties.

• Use scenario-based thinking to explore how changes in input affect outcomes.

• Recognize that subjectivity isn’t a bug; it’s part of the human lens we bring to complex problems. The aim is to manage and illuminate that lens, not pretend it doesn’t exist.

A final thought that ties it together

The question of which data type is most subjective isn’t just a quiz curiosity. It’s a reminder that risk analysis sits at the intersection of science and storytelling. Facts anchor us; diversified inputs give texture; and a single opinion, while potentially valuable, needs context and scrutiny to keep the risk narrative honest. In the end, FAIR isn’t about pretending certainty where there isn’t any. It’s about constructing transparent, evidence-informed risk pictures—where each data type has its place, its caveats, and its courage to be challenged.

If you’re reflecting on this topic, here’s a gentle prompt: when you think about a risk scenario, which input would you seek more of—another factual datapoint, a broader set of interview insights, or a well-documented opinion from an expert? Your answer reveals where you stand on balancing objectivity with expert judgment. And that balance—the art of combining the right kinds of data—makes risk analysis both robust and human.

Would you like to explore concrete templates for documenting assumptions or a simple checklist to assess data sources in a FAIR-style assessment? I’m happy to tailor practical, easy-to-use tools that fit your work or study rhythm.