Data collection involves gathering, gauging, and analyzing relevant information from various sources for evaluating results and predicting trends from the gathered data.
Once we can draw inferences about what kind of outcome we can expect from the collated data, we can align our decisions accordingly. There are more chances that, with these computed decisions, we will be able to reach the goal we had aspired for.
Most large enterprises depend upon the most advanced data collection methods to arrive at their judgments. It comes as no surprise why these corporations pay their data scientists and analysts so handsomely as their task is crucial for strategic decision-making in the company.
Why is Data Collection Needed?
Imagine that an army general is planning to strike his enemies. Before proceeding with the attack, he needs as much relevant information as possible.This will ensure a higher probability of success because the general would be able to develop the best course of action.
Likewise, if you are an organization, you will require data collection for analyzing and recording important information about your present and prospective clients. If this data is collected appropriately, it will also save you money by building a comprehensive database. s.
Before you collect data, you need to answer some questions first –
- What is the purpose of your research?
- What types of data do you intend to collect?
- What methods will you use to (a) collect, (b) store, and (c) process the data you have collected?
Types of data collection methods
The categorization of data collection depends on the sources from where it is collected. You could either collect the information directly from the primary source or through third parties. So, there are two types of data collection –
1. Primary Data Collection
When you collect data from the source first-hand, it is called primary data collection. This is among the common data collection methods in research, and is done through surveys, interviews, or experiments.
Primary data is considered the best type of data because it is highly accurate. If researchers can collect the information by themselves, they will be able to extract a lot of information from it. But primary data collection methods are extremely time-consuming and expensive.
2. Secondary Data Collection
When you collect data from third parties or sources like government data and records, online sources or magazines or journals, it is considered secondary data collection.. The second-hand data has already been statistically analyzed andis cheaper and easier to procure than primary data. But, this data might not be accurate or original. A big chunk of secondary data is usually numerical.
Data collection is not a new practice. But due to the sheer volume available in the present day, the data collection and data gathering processes have had to evolve over time.
Here are some common data collection methods –
- Through surveys
- Tracking through transactions
- Through focus groups and interviews
- Through observation
- Tracking online
- Through forms
- Monitoring through social media
Most common data collection tools
Now you have understood the basics of data collection. So, it is time to look at some specific data collection tools.
3. Association of Words:
The researcher gives respondents a set of words and asks what promptly comes to their minds upon hearing these words.
1. Completion of Sentences:
Researchers give respondents an incomplete sentence and see how they complete it.
2. Role Playing:
Respondents are given an imaginary situation and their actions or reactions are observed as if the situations are real.
3. In-person Surveys:
The researcher asks questions in person.
4. Online Web Surveys:
These are easy surveys, but all the questions are not truthfully answered.
5. Mobile and Phone Surveys:
Mobile data collection surveys rely on the use of mobile tablets or smartphones.. You can conduct the same survey on the phone, but many people would not pick up the phone.
It is the simplest way to collect and quantify data easily.. But, this method is only useful for small-scale situations.
Data Collection Techniques
Where do you use the primary or secondary methods of collecting data? Here are some of the techniques -
A large sample of people is picked by the researcher who asks people questions by a direct interview by phone or mail. It is the most common means of gathering data.
It is an indirect interview that gives the users the option to choose from options, and associate responses to images, words, ordering of data, etc.
The Delphi technique offers insights by gathering data from an expert panel. It is one of the popular data collection methods in research.
Focus groups consisting of 6 to 12 people are administered by a moderator where they discuss the issue before them.
In questionnaires, respondents get a series of open and close-ended questions that were relevant to an issue for which data was collected.
- Secondary Data Collection
In the secondary method of data collection, there are no specific methods. In this case, the data was added and stored from several sources of data like:
- Personal information of the customer
- Journals of business
- Government information like tax details and census survey
- Trade or business magazines
- From the internet
- Financial statements
- Reports of sales
- Retailer or distributor, or deal feedback
Use Case #1: Customer surveys were conducted to increase sales
Rice University Professors Dr Vicki Morwitz and Dr Paul Dholakia conducted surveys to analyze if an organization can manipulate customers’ faithfulness or purchasing habits. This survey spread over a year. One customer group was surveyed, while the other one was not surveyed. Next year, the group that participated in the survey was found to be three times more loyal than the group that was never surveyed.
Use Case #2: Improved customer acquisition and retention
Through the means of big data, organizations can understand customers’ interests and behavior in preferred services and products. After understanding these trends, they can try various ways to achieve overall customer satisfaction. The best example of this is Amazon. It applied data-driven decision-making in the 1990s only. Today, Amazon is one of the largest online e-commerce stores that use insights provided by big data to retain customers and increase the span of the business.
There are several applications of data collection for conducting online and offline surveys.While most of us are aware of the common applications, there are some which need a mention.
1. For customer satisfaction survey
There are many customer satisfaction survey forms which you see near and around you. A basic example of this type of survey is the form provided by the Google Play Store after you have installed an app.
2. For surveying demographics
You can conduct a quantitative survey of things like the ratio of males to females, range of age or number of unemployed individuals in a particular place with their details like addresses and names. Data collection is useful here.
3. For feedback from students in schools
This system is present in the school system of many countries. Here, students have to give feedback about their teachers. This helps schools to identify the pain points and individuals responsible for it.
4. How does it fit in the data science and data analysis process?
The chief goal of data collection is to gather reliable information and analyze it to make critical decisions for the business. After the data collection is complete, it is processed to make it useful to the companies that have acquired it.
A researcher can assess his/her theory based on the collated data. In most cases, the collection of data is the most important step of exploration, regardless of the research field.
Do you wish to have a thriving career in data analytics and data science? Then, you need to know various data collection techniques, which we have discussed in this blog. Now, you need to climb a notch higher. The PG Certificate Program in Business Analytics and Data Science, in collaboration with edX and Georgia Tech, will give you a lot of exposure used in data analytics and data science.
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