Introduction
In our everyday lives, we create and make use of data. "Data is life." Data depicts a lot of information about things that may be events, actions, observations, or objects. There are different ways in which data can be obtained.
First, we need to have an insight into what data is, its definition, and what it involves.
Definition of Data
Data is raw information that can be created, processed, analyzed, modified, stored, and interpreted in a meaningful context.
Data is a collection of information gathered by observations, measurements, research or analysis.
Data can be defined as a single piece of information that, when combined with others and put into an understandable form, can be used to pass a message about an object or something.
Data can further be defined as a representation of facts and notions in a formalized way that can be redefined, transformed, disseminated, and manipulated through various processes or techniques.
Data also exists in a structured format as a result of recorded observations.
What is Data Collection?
Data collection is the process of obtaining, assembling, and exploring data from different relevant sources.
According to Wikipedia, "data collection" is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes.
Data collection can also be referred to as the systematic process by which observations or measurements are collected in the research.
Why is Data Collection Important?
Data collection is an essential research component in all study fields, but the method of approach differs based on specialization, and the intensity of securing factual and credible collection persists.
Data can help organizations optimize the quality of work, draw valuable insights, predict trends, prevent risks, save time, drive profits, make better decisions, and so on. By collecting data, organizations have a treasure trove of valuable information at their disposal that can be utilized to thrive in today’s competitive market.
Data collection can help improve services, understand consumer needs, refine business strategies, grow and retain customers, and even sell the data as second-party data to other businesses at a profit.
Benefits of Data Collection
Generally, data collection aims to attain quality facts that allow analysis to lead to the formulation of assured and reliable answers to the questions that have been suggested.
Data collection Is advantageous for several reasons, both for our personal life and for the work that our business does. Some of these might consist of;
1. Data collection ensures the gathering of high-quality evidence that can be analyzed to come up with convincing and dependable feedback for the questions addressed.
2. Control and standardization of the procedure for high reliability and validity
3. Enhancement of strategic planning
4. Reduction of errors
5. Potential and productive decision-making
6. Organization of data
7. Risk management
8. This is very essential to the scientific research method.
9. Provision of logical and beneficial insights
10. Capturing and prediction of trend patterns
Sources of Data
Data can be collected from two places: internal and external sources.
1. Internal Sources: The information collected from internal sources is referred to as "primary data." It is the data gathered through reasoning, visibility, or survey review in a distinctive setting.
2. External Sources: The information gathered from external references is referred to as "secondary data." This type of data can be procured from discretionary sources like newspapers, magazines, the internet, documents, books, reports, and more.
Method of Data Collection
The following include popular methods of data collection:
1. Observational method:
The observation method involves evaluating things and collecting data about them. This might include observing individual animals or people in their physical places. The observational method involves the use of words, numbers, and reactions. In the observational method, there is no control over the data being collected.
Example: A crop farmer plants a variety of yam species under the same environmental conditions and compares the level of yield at harvest to determine which species are more resistant to pests and diseases.
Experimentation is a standard method of data collection that involves the manipulation of samples by applying some form of the procedure before data collection. It involves carrying out tests under a controlled set of conditions to demonstrate or examine the authenticity of a hypothesis.
Example: A science student tests the concentration of the volume of a solution by slowly adding measured amounts of some other substance or solution (titration).
3. Surveys and Questionnaire Methods:
This is a technique for gathering statistical information about the attributes, attitudes, or actions of a population through a structured set of questions. A survey also involves the collection of information from a group of individuals by asking them certain questions in a structured or unstructured form. This can be conducted through several mediums, such as one-on-one interviews, online forms, or the telephone.
Example: A company plans to employ more employees as staff and therefore seeks the right candidate by making them fill out or answer a questionnaire.
4. Simulation Method:
Simulation is a model that mimics the operation of an existing or proposed system, providing evidence for decision-making by being able to test different outlines or strategies.
It further involves the process of taking a large amount of data and using it to reproduce or mirror real-world conditions to either predict a future instance or determine the best course of action.
For example, a lecturer asks his students for feedback on their completed final-year projects.
Steps Involved in the Collection of Data
The collection of data has to be done in a step-by-step process to ensure the gathering of quality data that is relevant to the related aim of the research.
The steps to collecting data are below:
1. Identify opportunities for data collection:
Recognizing opportunities is the first step in data collection. It involves finding possible alternatives where that process would be useful for everyone involved.
2. Select opportunities and set objectives:
Understanding the available opportunities for effective data collection is important for choosing alternatives on which to focus. It is advisable to pick opportunities with positive impacts. Once you have identified and understood the opportunity, set goals for how you want the data to be used and why that data is important to you or your group.
3. Project Planning:
This step involves the creation of a strategy for executing the project and outlining the methods for gathering data. Create ways to store data and plan how much data you want to gather. Also, determine the scope and type of data to collect.
4. Validate your systems of measurement:
Thereon, ensure that your systems of measurement are factual because accurate measurements are important for data collection to enhance decision-making, reduce errors, and boost risk management.
5. Collect data:
After the project planning, put the strategy into practice. Monitor and verify all data as it is collected. When planning how best to collect data, it is important to be aware of the practical considerations and best practices for addressing the logistical challenges organizations often face at this stage of the process.
6. Analyze and interpret the data:
This step follows after the collection of data. This includes organizing the data in a comprehensive form and then deciphering it to implement productive decision-making. Clean the data properly and prepare the conclusions or results. The analysis stage is vital because it modifies unprocessed data into insightful knowledge that can be applied to improve our business plans and increase productivity.
7. Act based on the data results:
Once you have analyzed and interpreted the results of the data collected, you may decide to act on the data, collect more of the same type of data, or modify its approach.
Data Collection Tools and Techniques
The term "data collection tools" refers to the means or devices used to gather data. And while data collection techniques refer to the skills and practical abilities involved in the process of data collection,
Broadly, "data collection tools and techniques" refer to the creativity and utilization of devices involved in collecting data to perform data-related operations.
These tools and techniques used to collect data include the following:
1. Interview:
The interview tool is one of the most common means of gathering data. It can be conducted over the phone or even in person.
2. Case studies:
A case study is a technique in which in-depth descriptive information about specific individuals, entities, or cases is collected, organized, and presented in a narrative format. Its report is essentially a story.
3. Questionnaire and Survey:
This is used to collect data about characters, opinions, or elements based on responses to questions from a sample of a specified population or entity.
4. Oral histories:
Oral history can be defined as the collection, preservation, and interpretation of historical data that is based on the experiences and thoughts of people who were part of a particular event.
5. Social media monitoring:
This gathers data from measurement systems that capture information about social media through the internet. Social media platforms are not just for brand building alone but for collecting dependable data as well.
6. Records, reports, and documents:
This is a secondary data collection method where a crucial part of the research has been recorded. It involves the use of existing data to gather information.
7. Focus group:
Focus groups can be seen as a combination of interviews, surveys, and observations.
The main objective of this data collection technique is to gather collective opinions rather than individual ones.
8. Observation:
Observation is one of the key data collection methods used to form a hypothesis. The data obtained can't be controlled. Data is gathered by noticing certain attributes.