Data is the way of life in today’s digital world. It can help you make decisions and provide insight into what’s really going on around us.
However, there are different types of data that serve multiple purposes. It can be quite difficult to navigate their landscape without guidance.
We’ll go over some common types and how they’re used so that you can determine which is most appropriate for you.
Now that you’re ready to learn more about the world of data, keep reading.
Types of Data and Their Use
The three main categories of data are:
- computational (or big) data
Qualitative research focuses on the meaning behind what people say rather than assessing their behavior as surveys do. This information can then inform design decisions for products such as websites or apps that cater to customers’ needs. The quantitative analysis covers statistical measures.
Examples of this are percent change over time, correlation coefficients between variables. This data allows businesses to measure customer satisfaction levels based on feedback scores.
Computational data is the use of a computer program to analyze large sets of data. Primarily data that is generated from a business, medical, or other sources.
When we think about research and analysis for a company to use in their decision-making process, they usually have both types of data as a starting point. From there they will analyze which method (qualitative or quantitative) better suits their needs based on each situation.
How Much Money?
For example, if you wanted to know how much money people had left after spending $100 at your store this week, this would be quantifiable. That’s because it measures exact amounts instead of giving an opinion.
For instance, “I spend more than half my paycheck at our grocery store every month.” Quantitative data is most often analyzed by computers to make statistical predictions about people’s behavior.
Data can be subject to collection through structured surveys. But also unstructured interviews, observations of customers in stores, or on the street. As well as recordings of customer interactions with a company’s products, website functionality.
Another popular source is by monitoring social media sites for sentiment analysis (is this person happy? angry? disappointed?). The data comes from outside sources such as search engine result pages also.
Some types of qualitative data are converted into visual representations. This is done so that we can get insights into the meaning behind them more easily than when reading text excerpts alone.
For example, photos might be of use to identify trends in culture or fashion. Maps might be of use to find clusters of a particular disease in an area. Let’s examine the other types of data.
This includes anything from an email address to a credit card number. Basically, anything that is created when two computers or people exchange information. Every time someone signs up for your newsletter by entering their email address, it creates transactional data about them.
You might also have other transactional data like every time someone clicks through one of your ad campaigns. But also if someone purchases something from your online store. All of those events are subject to logging and categorized as transactional data.
This type includes things like whether someone has visited your website before. But also if they’ve interacted with any of your ads on social media sites.
Most people have some behavioral data stored by online retailers because we search for products and click through to websites when we want to buy something. These little bits of information help companies improve their customer service and keep people coming back.
Big Data is subject to definition as “large data sets that may be analyzed computationally to reveal patterns, associations, and other useful information”. To put it simply: A lot of people (or at least a significant percentage) are working with Big Data.
The companies they work for use this money-saving resource to create smarter products. But also to reduce risk in investments or marketing campaigns. As well as improve customer service by understanding their customers better.
Big Data is useful in many ways. For instance, when creating predictive policing systems that can determine where crime will happen next based on past criminal activity. Or when decreasing wait time times in hospitals by using analytics from previous visits of patients who had similar symptoms but different ailments.
Big Data ranges from anything that can be subject to collection, analysis, and used for future decisions.
It’s not just limited to social media posts and phone calls. But rather also includes machine-generated data like GPS locations of moving vehicles and industrial sensors.
But also bank records (though the latter two are more difficult to use without humans). The key is being able to collect all this information cheaply and quickly.
And then there’s unstructured data which is often text-based. Examples are correspondence between employees about work projects or emails sent by customers with complaints.
If you have a warehouse with hundreds of thousands (or even millions) of products on shelves, and each product has its own barcode or RFID tag that can be scanned by an electronic reader. The data captured from scanning the tags is “Machine Data.”
An example would be if someone walks into a store, scans their phone at the door to get in for free. But then uses it throughout the store as they browse through items.
By analyzing customer paths based on this information (via network algorithms), stores will know where people tend to congregate. This might lead them to add new racks of clothes near those areas or put restaurants nearby so customers don’t have to walk as far. The field of tech is dependent on data driven software engineering.
This information on customers about what they buy and when they come into the store is invaluable. It can help predict sales patterns for the future or even how many people will come in at a certain time.
Users might not realize this, but all of their social media information (such as posts) are actually customer data that stores collect. This occurs to learn more about you and your shopping habits. This includes likes, shares, retweets, which can give insight into products you’re interested in based on these actions.
For example, if someone has been buying new clothes every few weeks from a specific company online that person may start following them on Instagram because it’s become a habit.
User data is valuable because it can be subject to use to understand how someone will behave in the future. So if a user starts buying new clothes from another company, they might predict that this person may not have any interest anymore.
These are characteristics of your customer base such as age range or annual income level. This data helps retailers better tailor their products for them.
For instance, having an understanding of who makes up your audience could lead you to offer more reasonable prices. But also offer certain brands of clothing based on what people typically buy online from you.
Demographics are also essential when running marketing campaigns. Primarily, because vendors need to know where their target audience resides geographically. This way they’ll only run ads within those zones.
One of the most recent advancements in data storage is genomics. It allows for a person’s genes to be sequenced and analyzed from an individual strand of DNA. It then determines what makes that person different or susceptible to a certain disease.
This data is often used to prove that a company has been complying with certain laws or other regulations. Data taken at specific time periods can be subject to comparison against the latest information.
This then shows whether there are any changes in policy, procedure, and more. This way it helps ensure accurate records of production for them.
This is data that contains information on physical space and time. Spatiotemporal data can be used for weather forecasting and the impact on certain populations. For example, if it’s raining heavily in an area with older people who are sensitive to heavy rainfalls, they may need extra help during this time.
Traffic routing or navigation systems take into account where a person is located and what their destination is. This helps plan out how to get there more efficiently.
It’s also used for marketing purposes by knowing when customers are together. Spatiotemporal data allows companies to determine which demographic spends the most money at specific times of day (morning hours versus evening hours).
Now that you understand the various types of data and how they are subject to use, you are well on your way to understanding how this world works better in modern times. The entire commercial world is dependent on it, and understanding data means understanding people.
If you’re interested in learning more about the world of modern data, check out some of the related articles on the sidebar. You will be surprised to see what data does and how it is subject to use even more than what’s outlined in this article.