Whether you’re in the commercial sector, a research agency, or in the government, you need data collection to help you make better choices. The data collection process has changed and is growing with the times to accommodate different formats and state-of-the-art technologies. As data has become the new oil, the significance of data collection and various data collection methods is ever-increasing.
What is Data Collection?
Data collection is an essential step in Data Science and Machine Learning. It is a systematic process of gathering relevant and quality data. From gaining consumer insights to developing and improving AI/ML models for business use cases, fresh data is regularly required. The data collection concept is not new, but the form of data and its easy availability were not there a century ago.
Why is data collection required?
Data is the new oil. It empowers you to make informed decisions. It will help you to identify problems and will provide backup to your arguments to develop accurate theories.
Key Steps in the Data Collection Process
Data collection may seem an easy step, but it can be a time-consuming process. Also, the quality of data you gather at this stage will decide your overall model quality. So, before you rush into data collection, you need to understand a few points:
- Know the objective of the business
- Find the source of data and whom to contact
- What are the types of data that you collect
- What are data collection procedure
- Examine the Information and Apply Your Findings
- Establish a Deadline for Data Collection
Define: The first and utmost important step is to identify business objectives. Business objectives will help you to get a clear picture of the project. When you start defining the problem statement, you can understand what the business wants to address and why it matters.
Formulate questions: Once you understand the business objective, formulate the questions in a way that helps you to define what the business wants to achieve precisely.
- Source of Data
The source of data can be divided into two parts:
- Primary source – As the name suggested, it is the first-hand source of data collection usually done by the analyst/researchers. It is time-consuming and expensive, but the data would be highly accurate as compared to secondary sources as it is first-handed information. Examples of primary sources: Experiments, surveys, interviews, etc.
- Secondary source – Secondary source is the data that already exists in the system or is collected by some other parties/researchers. In the business scenarios, we mostly utilized the existing secondary data sources as it is already existing data which is less time-consuming. However, the data might not be as accurate as compared to a primary source. Examples of secondary sources are Financial statements, customer data, Government records, feedback, etc.
Data collected can be of two types:
- Quantitative data is necessary for calculations and statistical analysis. It can be expressed in numbers and can be analyzed through statistical methods or graphs. As a result, it can be represented in order or rank.
- Qualitative data is textual or non-numerical. It can be expressed in words, images, videos, etc., and can be analyzed through interpretations and categorizations. The qualitative data is analyzed by grouping it in terms of meaningful categories or themes.
The range of qualitative data has increased from structured data to accommodate unstructured data as well, such as reviews of movies, posts, and comments on social media, parsing of resume applications to get the best candidate according to a job description, etc.
Most of the business use cases are Mixed methods research. It is a combination of quantitative and qualitative research to answer the business question. Mixed methods help you gain a more complete picture than using a standalone quantitative or qualitative study.
- Data Collection Approach and Procedures
There are different methods to collect data, but you need to keep in mind what procedures will make accurate observations or measurements for the variables you are looking for. Different methods are:
Experiments – These involve the manipulation of the samples by applying some form of treatment before data collection. It refers to manipulating one variable to determine its changes on another variable. It is used to test a causal relationship. An example of the experimental method is a public clinical trial of a drug or an effect of a particular fertilizer on the plant's growth.
Surveys – Surveys are to understand or know the general characteristics or opinions of a group of people. It can be collected by distributing a list of questions in-person, online, or over the phone.
Focus group/Interview – In a Focus group, data is collected through a semi-structured group interview process. It is used for the in-depth understanding of opinions or perceptions on a particular topic.
Observation – Observations are used to understand something in its natural setting. Observation is a natural process - we do it regularly in our day-to-day activities. However, all these observations are not scientific. It becomes a method of data collection when it is systematically planned with the purpose of research. The validity and reliability of observed data should always be kept in mind while collecting data for any research or business use case. Example: To understand the behavior of children who are unable to speak, a systematically planned setup is done to conduct the research. Unlike the survey or the experiment, Observation is not artificial or limiting in nature.
Archival research – It is used when it is required to understand historical events, conditions, or practices. Originally it is generated for reporting or research purposes and often kept because of internal records. Some most common sources are Public records from governmental agencies, Research organizations, schools, educational institutes, Businesses and Industries, etc.
Social media Monitoring - Social media monitoring is quite famous nowadays. It helps to understand our existing customers' requirements or feedback and to understand who can be our next target audience. It is usually done by customers’ sentiment analysis and gathering information regarding the brand mentions. The sample size is massive for such data collection.
- Examine the Information and Apply Your Findings
It's important to examine the data and arrange the findings once we have gathered the information. The analysis stage is essential because it helps to find out any gap in data and to know how to fix the gap. At this stage, the data will be processed into insightful knowledge that can be applied to identify problems and make the decisions/business judgments better.
- Establish A Deadline for Data Collection
The process of data collection can be very lengthy and time-consuming. Collecting the information, cleaning, and processing it takes lots of effort, and we indulge ourselves in it to the extent that sometimes the process of data collection and cleaning takes most of the time of the project. So, we should set a deadline for the data collection phase at the beginning of the planning phase. It will help us to manage our time better and focus on other parts/activities of the project. There can be some forms of data that need to be continuously collected, so we can build up a technique for tracking transactional data, which we require continuously. In these situations, we should have a plan for when to gather the data and when to stop.
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Common Challenges in Data Collection and How to Overcome Them
Some predominant challenges can be faced while collecting data; let's explore a few of them and the steps to overcome these:
Data Quality Issues
The main threat to any model is poor data quality. The quality of data will decide the quality of the model/application. It is important to ensure accurate and appropriate data collection. The effects of bad-quality data collection can be an erroneous conclusion that leads to a waste of resources and the incapacity to respond to inquiries correctly.
How to overcome the quality issues:
- Fix data in the source system - Often, the issues of data quality can be solved by cleaning the source data.
- Fix data quality issues during the ETL/cleaning phase – If data quality issues cannot be solved at the source system, accept the bad quality data and fix it during the ETL/cleaning phase with the help of Subject Matter Experts (SMEs).
Data can be inconsistent when there are multiple data sources available. It's apparent that in some cases, the information from different sources will have discrepancies. The differences could be in the form of human errors, such as typing mistakes, or the form of formats, units, etc.
How to overcome the quality issues:
- Removing unwanted data
- Correcting formatting errors
- Validating data - Data validation often requires the support of professional data cleansing services to validate information accurately.
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