Before creating any new product, organizations need to collect data to research the demand, customer preferences, competitors, etc. In case these data are not collected in advance, the rate of failure for the new product is 80 percent or even higher. Even after the product is launched, many companies continue to collect their customers’ data to get feedback and identify ways to improve their overall customer experience.
This is where data scientists shine, they are responsible for helping companies not only collect data, but also organize it and derive results from it for shareholders to make decisions. Let's take a closer look at data collection.
What is Data Collection?
Data collection is the process of collecting, measuring and analyzing different types of information using a set of standard validated techniques. The main objective of data collection is to gather information-rich and reliable data, and analyze them to make critical business decisions. Once the data is collected, it goes through a rigorous process of data cleaning and data processing to make this data truly useful for businesses. There are two main methods of data collection in research based on the information that is required, namely:
- Primary Data Collection
- Secondary Data Collection
Primary Data Collection Methods
Primary data refers to data collected from first-hand experience directly from the main source. It refers to data that has never been used in the past. The data gathered by primary data collection methods are generally regarded as the best kind of data in research.
The methods of collecting primary data can be further divided into quantitative data collection methods (deals with factors that can be counted) and qualitative data collection methods (deals with factors that are not necessarily numerical in nature).
Here are some of the most common primary data collection methods:
Interviews are a direct method of data collection. It is simply a process in which the interviewer asks questions and the interviewee responds to them. It provides a high degree of flexibility because questions can be adjusted and changed anytime according to the situation.
In this method, researchers observe a situation around them and record the findings. It can be used to evaluate the behaviour of different people in controlled (everyone knows they are being observed) and uncontrolled (no one knows they are being observed) situations. This method is highly effective because it is straightforward and not directly dependent on other participants.
For example, a person looks at random people that walk their pets on a busy street, and then uses this data to decide whether or not to open a pet food store in that area.
3. Surveys and Questionnaires
Surveys and questionnaires provide a broad perspective from large groups of people. They can be conducted face-to-face, mailed, or even posted on the Internet to get respondents from anywhere in the world. The answers can be yes or no, true or false, multiple choice, and even open-ended questions. However, a drawback of surveys and questionnaires is delayed response and the possibility of ambiguous answers.
4. Focus Groups
A focus group is similar to an interview, but it is conducted with a group of people who all have something in common. The data collected is similar to in-person interviews, but they offer a better understanding of why a certain group of people thinks in a particular way. However, some drawbacks of this method are lack of privacy and domination of the interview by one or two participants. Focus groups can also be time-consuming and challenging, but they help reveal some of the best information for complex situations.
5. Oral Histories
Oral histories also involve asking questions like interviews and focus groups. However, it is defined more precisely and the data collected is linked to a single phenomenon. It involves collecting the opinions and personal experiences of people in a particular event that they were involved in. For example, it can help in studying the effect of a new product in a particular community.
Secondary Data Collection Methods
Secondary data refers to data that has already been collected by someone else. It is much more inexpensive and easier to collect than primary data. While primary data collection provides more authentic and original data, there are numerous instances where secondary data collection provides great value to organizations.
Here are some of the most common secondary data collection methods:
The use of the Internet has become one of the most popular secondary data collection methods in recent times. There is a large pool of free and paid research resources that can be easily accessed on the Internet. While this method is a fast and easy way of data collection, you should only source from authentic sites while collecting information.
2. Government Archives
There is lots of data available from government archives that you can make use of. The most important advantage is that the data in government archives are authentic and verifiable. The challenge, however, is that data is not always readily available due to a number of factors. For example, criminal records can come under classified information and are difficult for anyone to have access to them.
Most researchers donate several copies of their academic research to libraries. You can collect important and authentic information based on different research contexts. Libraries also serve as a storehouse for business directories, annual reports and other similar documents that help businesses in their research.
Use Case: Conducting Customer Surveys to Multiply Sales
A research study was conducted by Rice University Professor Dr. Paul Dholakia and Dr. Vicki Morwitz to see whether a company could influence customers’ loyalty or buying habits. The research study was conducted over the course of a year. One group of customers were surveyed and the other set was not surveyed about customer satisfaction. In the next year, the group that took the survey were thrice as likely to renew their loyalty towards the organization than the other group.
Choose the Right Program
Are you interested in pursuing a career in the dynamic field of data analytics? Our courses on Data Analytics and Science are crafted to equip you with the expertise and insights required to thrive in this burgeoning sector. Here's a detailed view for your clarity:
Program Name Data Science Master's Program Post Graduate Program In Data Analytics Post Graduate Program In Data Science Geo All Geos All Geos Not applicable in US University Simplilearn Purdue Caltech Course Duration 11 Months 8 Months 11 Months Coding Experience Required Basic Basic No Skills You Will Learn 10+ skills including data structure, data manipulation, NumPy, Scikit-Learn, Tableau and more
Data Analytics, Statistical Analysis using Excel, Data Analysis Python and R, and more
8+ skills including
Supervised & Unsupervised Learning
Data Visualization, and more
Additional Benefits Applied Learning via Capstone and 25+ Data Science Projects Purdue Alumni Association Membership
Free IIMJobs Pro-Membership of 6 months
Upto 14 CEU Credits Caltech CTME Circle Membership Cost $$ $$$$ $$$$ Explore Program Explore Program Explore Program
Here’s What You Can Do Next
If you want to pursue a successful career in data analytics, you need to be an expert in different data collection techniques. Now that you know the most common methods of data collection, it’s time to take a step further and learn each one of these and more in detail. Simplilearn’s Caltech Post Graduate Program In Data Science in partnership with Caltech University will give you broad exposure to key technologies and skills currently used in Data Science. This program also provides a hands-on approach with case studies and industry-aligned projects to bring the relevant concepts to live. Sign up for this course today and get started on a new, brighter future in data science.