September 09, 2021
Data-driven decision-making is perhaps one of the most talked-about financial and business solutions today. The success of such data-driven solutions requires a variety of data types. However, not all data types are the same.
On the one hand, there exists traditional data or internal data produced by a particular company. On the other hand, we have non-traditional or alternative data, which is data collected from numerous external sources. Consequently, this article will focus on alternative data and provide a deeper understanding of the nuances of alternative data types. Let’s glance over the main types of alternative data.
Knowing these main data types as an investor or business professional is crucial. This short guide aims to help you understand what data you can access and how it can be used. If you want to use alternative data to inform your decisions, you need to filter out all the necessary data types to meet your business objectives.
For example, if you want to invest in a retail company, you may want to access qualitative data such as sensor-related information and social media opinions. Similarly, continuous data cannot be analyzed the same as discrete data. This is why knowing what type of data you need is crucial in helping you choose the best type of analysis, integration, or other implementation.
Types of data in statistics and analysis can vary widely – and, as a result, create confusion. Some of them, like quantitative and qualitative data, are different concepts. Other types of data include numerical, discrete, categorical, ordinal or nominal data, ratio, and continuous, among others. However, all data types fall under one of two categories: qualitative and quantitative. Let’s take a look at the highest data classification level – qualitative and quantitative data.
Qualitative data refers to non-statistical information. It is often unstructured or semi-structured, and perhaps one of the easiest ways to identify it is that it is non-numerical. In other words, qualitative data refers to information that describes certain properties, labels, attributes.
Qualitative data is often known as investigative as it can be used to answer the question “why”. Qualitative data helps you create a “story”, develop a hypothesis or obtain an initial understanding of a case or situation.
Because qualitative data is generated via numerous channels, such as company employee review data, investors and businesses can access qualitative data generated by individuals. This type of alternative data often comes from unstructured (one of the main characteristics of qualitative data) and is often difficult to collect and analyze.
Some examples of qualitative alternative data include information collected from social media, search engines, product reviews, comments, or other web interactions.
Interested parties can collect these data directly from the source (i.e., social media platforms), or contact an alternative data provider, such as Coresignal, and access any relevant datasets. The benefit of choosing a data provider is that the information is already selected and presented in an easy-to-understand format, rather than collecting all the data available on all social media platforms or search engines.
Examples of qualitative data that might interest investors and businesses are extremely varied. These depend on your objectives and the purpose of your data collection.
For instance, if you want to invest in a business, you may be interested in the comments on social media that mention the company’s products and whether the review is positive or negative. Alternatively, a company trying to gain an insight into their competitors might seek the same information or may want to find out the socioeconomic status of their clients.
Another source of qualitative data when it comes to alternative data is sensors. This refers to information collected from CCTV, POS, satellites, geo-location, and others. This data collection is facilitated via the interconnectivity of devices. This could indicate, for instance, the foot traffic at the competitor’s business location.
As briefly mentioned above, some data types refer to the same information. In statistics, qualitative data is the same as “categorical data”. This is because qualitative information can be easily “categorized” based on properties or certain characteristics.
The main feature is that qualitative data is not numerical (i.e., it does not come as numbers), but rather as words. In some cases, qualitative data may be assigned numbers (1 or 0, for instance) for analysis purposes.
For instance, if you conduct a questionnaire to find out the native language of your customers, you may note “1” for English and “0” for others. However, these numbers have no meaning from a mathematical perspective; similarly, if you check the postcodes of your clients, the data is still qualitative because the postcode number does not have any mathematical meaning; it rather shows the address of your customers.
Categorical data can be further split into nominal or ordinal data. Nominal data refers to information that cannot be sorted in a given way – you can assign categories to these data, but you cannot order them, for instance, from the highest to the lowest.
For example, information collected through “yes” or “no” questions is a type of nominal data: “would you recommend this product?”. The answers collected can be split into “yes” or “no,” but you cannot further organize it (i.e., it doesn’t matter if “yes” data goes before or after “no” answers since different individuals give them). Nominal data helps you calculate percentages, such as 50% of comments on social media were happy with the company’s after-sale service, proportions, or frequencies.
The opposite type of categorical data is ordinal; in other words, you assign categories to your qualitative data, and then you can order (thus “ordinal”) them in a logical way.
Let’s assume that you have a B2B company and you want to collect information about your clients. You can obtain firmographic data indicating the size of each client company and assign them into “small”, “medium”, or “large” enterprises. This is a type of ordinal data. Unless the information with “yes/no” answers, the categories can be ordered from “small” to “large”.
Ordinal data can also be assigned numbers; however, these have no mathematical meaning. Thus it is still under the qualitative umbrella. For example, if you conduct a questionnaire asking customers to rate the quality of a product from 1 to 5, with one being “poor” and five is “high-quality,” your ordinal data can be categorized and assigned these numbers.
However, from a mathematical perspective, they do not have any meaning. It’s rather just a simple way of sorting the data. Just like nominal data, this can also be used to calculate percentages (i.e., 70% of customers stated that the product is good or better), proportions, and frequencies, among others.
Qualitative data helps you understand the reasons behind certain phenomena. For example, you notice that your competitor’s revenues are 50% higher than yours. The quantitative data (i.e., the revenue numbers) does not help you understand why the company performs much better.
The right qualitative data can help you understand your competitors, helping you adjust your own competitive strategy to stay ahead of your competition. For example, you can use data collected from sensors to identify the foot traffic at your competitor’s location. In this case, you may find out that they have more customers than you do, which explains the revenues; or, alternatively, you may find the same amount or fewer customers, which may mean that they charge a premium for their products and services.
Some other benefits and applications of qualitative alternative data include:
The second major type of data is quantitative. As the name suggests, it is numerical data that indicates quantities of specific aspects. In short, quantitative data come as numbers with mathematical meaning. For example, a company’s financial reports contain quantitative data. The main benefit of quantitative data is that it is easier to collect, analyze, and understand than qualitative data.
One can easily visually represent quantitative data with various charts and graphs, including scatter plots, lines, bar graphs, and others. You can also use quantitative data to calculate ratios, for instance, if you want to compare a company’s performance or study its financial reports to make an investment decision.
Alternative data of this type can also come from a variety of sources. You may use market reports, conduct surveys, or collect web scraped data that can be transposed into numbers with certain values. Some examples include the number of web visitors, a company’s total number of employees, and others.
Some examples of alternative quantitative data include credit card transactions, sales data or data from financial reports, macroeconomic indicators, number of employees or the number of job postings, and many more.
Discrete data refers to certain types of information that cannot be divided into parts. For example, a company cannot have 15.5 employees – it’s either 15 or 16 employees. In simple words, discrete data can take only certain values and cannot be divided.
On the other side, continuous data can be divided into parts and may take nearly any numeric value. For instance, a company’s net profit of $100,593 is continuous data. The same happens with the financial information of a company, such as sales data, credit card transactions, and others.
Numerical data is easy to interpret and can be collected easier because of its form. Some of the main benefits of collecting quantitative data depend on the type of information you seek. For instance, firmographic or firm-specific data allows you to have a quick glance at your competitors’ size, employee numbers, and others.
Although quantitative data is easier to collect and interpret, many professionals appreciate qualitative data more. However, this is primarily due to the scope and details of that data that can help you paint the whole story. Likewise, quantitative data is oftentimes favored due to the ease of processing, collection, and integration. Some of the main benefits of quantitative data include:
This article briefly explored the main data types to help investors and business professionals that might want to use alternative data to inform their decisions. This is the first step that helps you identify the right type of data you need and how to analyze it using the right measurements or visualization methods.
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