In this day and age, it seems we do everything digitally—whether it be ordering food, filing taxes, or meeting new people. This especially applies to businesses, who now use digital means to pay their employees, track performance, and find new hires.
When it comes to data collection methods, however, there are still some uses for non-digital formats. Yes, it may come as a surprise, but not all data harvesting happens through digital means.
First, let’s take a look at a few different types of data, then we’ll dive into the methods of data collection businesses use every day.
4 Types of Data
Spatiotemporal data are data that relate to both space and time. The spatiotemporal model arises when data are collected across time as well as space and has at least one spatial and one temporal property.
According to the Columbia University Mailman School of Public Health, even though this approach to data can provide new dimensions for data interpretation, it’s still in its infancy:
Assessing both temporal and spatial dimensions of data adds significant complexity to the data analysis process for two major reasons: 1) Continuous and discrete changes of spatial and non-spatial properties of spatiotemporal objects and 2) the influence of collocated neighboring spatiotemporal objects on one another.
Other complexities arise due to the inherent natures of both time and space. For example, while space is two-dimensional with unlimited directionality, time is unidimensional and can only move in one direction, forward.
Real Time Data
Real time data describes behavior as it happens in the moment. This allows an analysis to take place as soon as data becomes available, allowing users to get insights and/or draw conclusions immediately or very soon after the data enters a system. For businesses, this means they can react without delay, preventing problems before they happen and seizing opportunities in real time.
Operational data is one type of strategic data. It describes numbers and statistics that become action steps, including internal control and operational environment information such as data on a company’s workforce, suppliers, direct competitors, creditors, and customers. According to Gartner, an operational data store (ODS) is an alternative to having operational decision support system (DSS) applications access data directly from the database that supports transaction processing (TP):
While both require a significant amount of planning, the ODS tends to focus on the operational requirements of a particular business process (for example, customer service), and on the need to allow updates and propagate those updates back to the source operational system from which the data elements were obtained. The data warehouse, on the other hand, provides an architecture for decision makers to access data to perform strategic analysis, which often involves historical and cross-functional data and the need to support many applications.
The name “high-dimensional” means that the number of dimensions of this type of data are staggeringly high — so high, in fact, that calculations can become extremely difficult. This means that the number of features can exceed the number of observations.
One form of high-dimensional data is artificial intelligence (AI) data that measures facial expressions. According to IntechOpen, a face recognition system is expected to identify faces present in images and videos automatically:
Face verification involves a one-to-one match that compares a query face image against a template face image whose identity is being claimed. Face identification involves one-to-many matches that compare a query face image against all the template images in the database to determine the identity of the query face. Another face recognition scenario involves a watch-list check, where a query face is matched to a list of suspects (one-to-few matches).
Face detection and recognition can then be used for personal identity verification, gender classification, video-surveillance, facial expression extraction, and advanced human and computer interaction.
4 Data Collection Methods
Now that we know a little bit about four types of data that are used, let’s look at four different data collection methods businesses regularly employ.
Surveys & Interviews
This is one of the non-digital, direct methods that companies use to gather actionable information. During this type of data collection, subjects are aware that they are producing data. Some advantages of this type of data collection include convenience, low costs, high representativeness, little or no observer subjectivity, good statistical significance, and precise results.
This type of collection method tracks where users click on a web page, which might be on a CTA or somewhere in the web page’s margins. This initiative reveals how users engage with content so that businesses can better optimize what they’re publishing on their website(s) and other owned digital media, like social media pages, profiles, etc. Advantages of click-tracking include a better understanding of a given audience, comparison of pieces of content against one another, conversion tracking, tracking of ad-blocking users, and measuring overall marketing success.
UX tracking uses IP addresses, cookies/super cookies, recording software, and other tools to evaluate how users behave on a web domain. Companies can test out different language, color schemes, images, and more in order to make the best choices based on effectiveness. They can use UX tracking to decrease abandonment rate, increase pageviews, improve website usability, brand credibility, and more.
This is another non-digital, direct method that is still effective today. Focus groups put individuals together in a group and stakeholders observe how those individuals communicate about a product. These individuals know that they are providing information to the company. Advantages of this type of data collection include low costs, a small time investment, understanding of personal as well as group perceptions, opinions, and feelings, a broad range of information, clarification, and more.
A World of Data at Your Fingertips
In today’s information age, businesses have access to an alarming amount of information on their customers and their own performance metrics. It’s important to not only leverage different data types, but to employ different data collection methods—both digital and analog—in order to truly understand the numbers and improve overall business performance.
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