Objective vs. subjective data for insightful analysis

When a tree falls in the forest, and no one is around to hear it, does it make a sound? Objectively, yes. Subjectively, no.

While objective and subjective data are essential for in-depth analysis, they differ drastically. Each data type requires a particular processing, analytical, and implementation approach.

Telling the difference between subjective and objective data is easy. Knowing how to use each one can be more challenging. Let's take a closer look at how these types of information work and how they can contribute to successful data analytics.

Objective data is information you obtain through facts or resources. An example of objective data in business can be the customer churn rate. You can calculate this number using a transparent formula: Lost customers / total customers at the start of the period x 100.

Objective data usually appear the same even when you collect it from different sources. It doesn't vary from person to person or depending on the situation. The churn rate number will stay the same whichever marketing specialist calculates it. 

Since objective data is highly verifiable, it's reliable and consistent. While objective data is integral to research, marketing, sales, and analytics, it doesn't always provide an in-depth understanding of the situation.

That's where subjective data comes in.

Subjective data comes from feelings, experiences, opinions, and thoughts. An example of subjective data is a customer's level of satisfaction with your business.

When you send out a survey asking a customer to rate your services from 1–10, you receive completely subjective data. Another customer could rate identical services entirely differently.

You can’t declare subjective data as truth since you don’t obtain it through established factors. Based on personal feelings and experiences, this data doesn't just vary from person to person. The same person can generate different subjective data daily depending on their mood, workplace environment, and even the weather.

While it may seem that subjective data is less valuable than objective data, it can be highly efficient for understanding your target audience's pain points.

While objective and subjective data don't seem similar, both can be highly useful in marketing, research, healthcare, and other areas. 

The key similarities between these two data types include the following:

• Both provide valuable insight into human behavior

• Both are a form of knowledge

• Both allow you to evaluate a specific occurrence or situation

Even though many experts use objective and subjective data as antonyms, one can complement the other for some purposes. 

When building customer relationships or moving clients down the sales funnel, subjective information is an essential addition to objective metrics.

When it comes to analytics, objective data, and subjective data are different. The main differences between the two are:

Measurements

While you can measure objective and subjective data, only objective data can provide consistent measurement results.

For example, you can measure someone’s temperature with a thermometer and ask them how they feel on a scale of 1–10. Both measurements provide insights into their medical condition. 

However, the first measurement is much more important for evaluating the course of treatment, deciding whether to give medication and comparing temperature over time.

Quantitative vs. qualitative

Objective data is quantitative, while subjective data is qualitative. 

Quantitative data is numerical, so it's easy to evaluate and measure. 

Meanwhile, qualitative data is interpretation-based, which can help you understand why or how something happened.

Objective data allows you to evaluate the situation with specific measurements, and qualitative data can help you figure out the reasons behind this situation.

Practical applications

The practical application of objective data is usually helpful in proving credibility or statement validity. For example, a student attaches SAT results when applying to college as an accurate and objective measure of their knowledge.

Meanwhile, the practical application of subjective data is evaluating a customer's satisfaction and success with a company. For example, an open-ended survey can help you measure the customer's experience with your business. This allows you to adjust your marketing campaign, improve a product, increase sales, and more.

Objective data often applies to the hard sciences (chemistry, geometry, physics), while subjective data is suitable for the soft sciences (sociology, psychology, etc.) It's also highly applicable in nursing.  

Representation of truth

Objective and subjective data can represent the truth in two different ways. Thoughts, opinions, and feelings influence a subjective view of truth.

One person's subjective understanding of a situation can differ from another view. A great example is how two eyewitnesses can have completely different accounts of the same car accident.

Meanwhile, an objective representation of truth is always the same. For example, it can include the speed at which the car was traveling or whether the red traffic light was on.

While drastically different, objective and subjective data can be vital for high-quality data analytics. Insightful analysis usually requires cold hard facts and opinions, thoughts, and feelings.

Whether you’re evaluating customer experience or planning the next year's budget, both types of data can play significant roles in decision-making.

Types of objective data depend on the situation or occurrence you are evaluating. For example, it can be customer churn rate, cost per lead, and click-through rate in marketing.

A subjective data example is a consumer's satisfaction rating of your product. This data type can vary from consumer to consumer or even from day to day.

An example of objective data is a patient's blood pressure, pulse, and body temperature. Meanwhile, subjective data would be the patient's answer to "How are you feeling?"