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So you’ve heard about data points, but what exactly are they? How and when are they used?
In this blog post, I’ll cover all those questions on what data points are, give some examples, talk about when they should be used, and more.
Read on for a full detailed explanation!
What are Data Points?
Data points are elements that provide information or insights about a particular subject. They can come in many different forms, such as numbers, words, images, and audio clips.
In fact, you even use and encounter them in everyday life!
Such points may be collected from surveys, observations, measurements, or other data sources. Once data points are gathered, they are then compiled, analyzed, and used to draw conclusions or make predictions about the data.
A single data point may also refer to one point of data, like a discrete unit of data.
Now that you understand the data point definition, here are some examples to help you understand better.
What Are Some Examples of Data Points?
Numbers are typically used for quantitative data and can include anything from sales data to population data.
Most of us think of numbers when we describe data points. Numbers are one of the easiest and most commonly identified data points.
However, there’s more!
Words can be used for both qualitative data, such as customer feedback forms or surveys, and quantitative data, like survey responses or ticket sales data.
Words are text-based data points that many of us are not actually aware of!
For example, such text-based data points can come from survey responses to newspaper articles.
Photos, videos, and other visuals can provide data points showing trends, patterns, or perspectives.
This data point type can be used to gain insights into the subject and draw meaningful conclusions. For example, data points from images could include facial expressions or clothing styles.
Although not entirely tangible, certain information can be derived from image data points.
4. Audio Clips
Recorded audio data points can provide insight into public opinion, customer satisfaction data, and more.
These data points can also be used to create data-driven reports or to identify correlations between data points.
Audio data points tend to provide data that quantitative data cannot, such as the tones of voice being used and the emotion being expressed.
However, audio data points have also recently been made quantifiable through artificial intelligence, where the tempo, mood, and genre can be defined through audio features.
What Are the Types of Data Points?
Data points can be divided into two main categories: qualitative data and quantitative data.
1. Qualitative Data Points
Qualitative data points are data that is collected through observation, surveys, or experiments. This data type is usually non-numerical in nature and consists of descriptors such as colors, words, emotions, etc. They can also come in the form of binary data.
This data type is useful for providing insights into a particular topic, such as customer satisfaction data or product feedback data.
Some examples include:
- Sentiment data
- User feedback surveys
- Interview results
As you can see, data collection of such qualitative data is mostly through human input.
2. Quantitative Data Points
Quantitative data points are data that are collected through more structured methods and can be represented numerically. This data type usually consists of numbers and statistics, such as sales, population, or weather data.
Quantitative data points are usually used to identify data trends, correlations, and predictions.
Some examples include:
- Sales data
- Population data
- Weather data
When Should Data Points Be Used?
Data points should be used whenever data needs to be collected, analyzed, and reported on. This could include a variety of situations, such as market research, customer surveys, data analysis, and more.
Data points can be used to identify trends, measure performance, compare data sets, and make predictions. This information is then used to inform decisions and strategies.
For example, data point analysis can be done to extract information from customer data through common data analysis software or BI tools.
Who Uses Data Points?
Data points are used by data analysts, data scientists, market researchers, and data-driven decision-makers. These data professionals use data points to understand their data better and make informed decisions.
For example, the usage of data points by data scientists includes:
- Identifying correlations and data trends
- Making predictions based on data
- Determining the statistical significance of an outlier data point
These data points can also be used by businesses to measure customer satisfaction, identify areas for improvement, or develop marketing strategies.
Such points are an important tool for data-driven decision-makers that need data to make informed decisions.
Data Point vs Data Field
A data point refers to data that is used to provide information or insights but a data field refers to data stored at a specific location.
Although they tend to be used interchangeably, data points should only be used to describe data that is used in data analysis, such as data points for customer data or data points for market research. They also usually provide some statistical or analytical context.
Data fields, on the other hand, are used to refer to data stored specifically within a database, data table, data objects, or data structure.
For example, data fields could be used to store customer information within data tables stored in a customer database.
Data Point vs Data Attribute
Data points and data attributes are related but slightly different concepts.
A data point is data that is used to provide information or insights while data attributes are pieces of information associated with a certain data point.
For example, customer data can have multiple data points associated with it such as age, gender, location, etc. These data points can then have data attributes associated with them such as age range, gender identification, zip code, etc.
With a data attribute, you can better understand and appreciate the context behind the data points represented on a trendline. A data attribute is also similar in meaning to a data dimension.
In conclusion, data points are data used to provide insights and information about a particular subject. They are into two main categories: qualitative and quantitative.
I hope this article has been useful in understanding data points! Thanks for reading!