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What Is Data Collection? A Guide for Aspiring Data Scientists

What Is Data Collection

With billions of active Internet users worldwide, it is no surprise that we generate massive amounts of data daily. This makes it challenging for researchers to find the correct data, collect it, and evaluate it for eventual use. That’s why there are data collectors.

This article explains data collection, including why it’s needed, the methods, tools, challenges, best practices, and how you can better understand how to collect and analyze data through online data science training.

So, before we explore this, let’s establish a definition. What is data collection?

What is Data Collection?

It involves collecting and evaluating information or data from multiple sources to answer questions, find answers to research problems, evaluate outcomes, and forecast probabilities and trends. It plays a considerable role in many types of analysis, research and decision-making, including in the social sciences, business, and healthcare.

Collecting data accurately is vital for making informed business decisions, ensuring quality assurance and maintaining research integrity.

During the data collection process, researchers must identify the different data types, sources of data, and methods being employed since there are many different methods to collect data for analysis. Many fields, including commercial, government and research, rely heavily on data collection.

But before an analyst starts collecting data, they must first answer three questions:

  • What’s the goal or purpose of the research?
  • What sorts of data were they planning on gathering?
  • What procedures and methods will be used to collect, store, and process this information?

In addition, we can divide data into qualitative and quantitative categories. Qualitative data includes descriptions such as color, quality, size and appearance. As the name implies, quantitative data covers numbers, such as poll numbers, statistics, measurements, percentages, etc.

Why Do We Need Data Collection?

Informed decisions are the best decisions you can make. The more information you have, the more insightful your courses of action and the better chance of success. Today’s highly competitive commercial world demands that every enterprise that wants to not only stay afloat but thrive must make as few mistakes as possible.

Data collection helps organizations manage the sheer volumes of big data information and turn it into actionable insights that could prove to be a difference-maker.

So, what are the five methods of collecting data?

Presenting the Five Methods of Collecting Data

There’s a lot of data out there. Fortunately, there are many different types of data collection methods available to choose from. Let’s look into the five most popular methods of collecting data. Although there are additional methods, most industries and sectors rely extensively on these particular five methods.

  • Direct observation. The researcher assumes the passive observer role, taking note of the subject’s behavior, words, and actions.
  • Documents and records. This method involves conducting basic research on the topic in question and seeing what has been learned from past methods.
  • Focus groups. Focus groups are essentially mass interviews. You can tailor group composition to fit a particular demographic.
  • Interviews. One-on-one interviews allow researchers to collect data directly from personal communication with the subject.
  • Surveys, quizzes, and questionnaires. This includes close-ended surveys, open-ended surveys, online questionnaires and quizzes.

Now, let’s look at the steps involved in a typical data collection procedure.

All About the Data Collection Process

It can be broken down into five steps. There’s symmetry here. Here are the steps involved in your standard data collection procedure:

  • Figure out what data you want to collect. You begin the process by deciding what information you want to gather. Pick the subjects the data will cover, the sources used to gather it, and the information needed. For example, gathering information on products customers aged 20-40 searched for.
  • Establish a deadline. Set a deadline at the outset of the planning phase. Although some forms of data may require perpetual collection, tracking the data throughout a given time frame is essential, especially if it’s for a particular campaign.
  • Choose an approach. Select the data technique that will function as your foundation of the data gathering plan. Consider the kind of information you want to gather, the period during which you will receive the data, and any other factors involved.
  • Gather the information. Once the plan is complete, implement the plan and start gathering data. Store and arrange our data, following the plan and monitoring its progress.
  • Examine the information and apply your findings. At last, it’s time to examine the data and arrange the findings. The analysis stage is critical because it changes unprocessed data into insightful, applicable knowledge that benefits product design, marketing plans and business judgments.

The Significance of Guaranteeing Precise and Suitable Data Gathering

Your research insights will only be as good as the data-gathering attempt. You must use the correct data-gathering tools, focus on the right groups, and maintain research accuracy and integrity. If you don’t engage in research correctly, you may experience:

  • Inaccurate conclusions that waste the organization’s resources
  • Decisions that compromise the organization’s public policy
  • Losing the capacity to respond to research inquiries correctly
  • Causing actual harm to participants
  • Misleading other researchers into adopting useless research avenues
  • The inability to replicate and validate the findings makes it difficult to prove your findings

Common Challenges Found While Collecting Data

As you may expect, data collection can be a daunting task. However, forewarned is forearmed, so here’s a list of the typical challenges that data collectors face.

Inconsistent Data

When you work with vastly different data sources, discrepancies may arise. The differences could be with formats, units or even spellings. Inconsistent data might also happen during corporate mergers or relocations. Unfortunately, data inconsistencies accumulate and reduce the data’s overall value if these issues aren’t resolved.

Ambiguous Data

Even if you have implemented strong oversight, some errors can still happen in vast databases or data lakes. Spelling mistakes go unnoticed, formatting difficulties occur, and column heads might be inaccurately displayed. This vague data can cause many problems for reporting and analytics.

Deciding Which Data to Collect

Sometimes, too many choices present a challenge. Deciding what data to collect is one of the most essential factors governing data collection and should be one of the first considerations while collecting data. Researchers must select the subjects the data will cover, the sources used to gather it, and the information needed. Neglecting this issue could lead to duplication of effort, collecting irrelevant data or ruining the entire study.

Data Downtime

Data is critical for the decisions and operations of a data-driven business. However, short periods of inaccessibility or unreliability may result in poor analytical outcomes and customer complaints. Data engineers spend about 80% of their time updating, maintaining, and guaranteeing data integrity in the pipeline. Much of the data downtime stems from migration issues or schema modifications. Thus, data downtime must be continuously monitored and reduced via automation.

Overabundant Data

Alternately known as “too much of a good thing,” there is a risk of getting lost in the abundance of data when looking for information relevant to your analytical efforts. Data analysts, data scientists and business users devote much of their work to finding and organizing appropriate data. Other data quality problems escalate when data volume increases, especially when working with streaming data and large files or databases.

Dealing with Big Data

Big data describes massive data sets with more intricate and diversified structures, resulting in increased challenges in storing, analyzing and extracting methods. Big data’s data sets are so large that more than conventional data processing tools are required. The amount of data generated by the Internet, healthcare applications, social media sites, the Internet of Things, technological advancements and increasingly larger organizations is rapidly growing.

Duplicate Data

Local databases, streaming data and cloud data lakes are just a couple of the data sources that modern enterprises deal with. Such sources are likely to duplicate and overlap with each other often. For example, duplicate contact information can adversely affect the customer’s experience. Additionally, the chance of biased analytical outcomes increases when duplicate data is involved. It can also result in ruining machine learning models with biased training data.

Inaccurate Data

Data accuracy is vital for highly regulated businesses such as healthcare. Inaccurate information doesn’t give organizations an accurate picture of the situation and thus can’t be used to plan the ideal course of action. Personalized customer experiences and marketing strategies underperform if the data is inaccurate. Causes of data inaccuracies include data degradation, human error and data drift. Global data decay happens at a rate of about 3% per month. Data integrity can also be compromised while transferred between different systems, and data quality may deteriorate over time.

Hidden Data

Most businesses only utilize a fraction of their data, with the rest often lost in data silos or exiled to data graveyards. Hidden data reduces the chances of developing exciting new products, improves service and streamlines organizational procedures.

Finding the Relevant Data

Finding relevant data isn’t always easy. There are several circumstances that we need to account for while trying to find relevant data, including:

  • Relevant domain
  • Relevant demographics
  • Relevant time

Irrelevant data in any factor renders it obsolete and unsuitable for analysis. This may lead to incomplete research or analysis, multiple repetitive attempts or the halt of the study.

Low Response and Poor Design

Finally, poor design and low response rates occur during the data collection process, especially in health surveys that use questionnaires. These factors may lead to insufficient or inadequate data supplies for the study. Creating an incentivized program could mitigate these issues and generate more responses.

So, how do we handle this formidable list of challenges? By instituting best practices, of course!

Key Considerations and Best Practices

Here are some of data collection’s best practices that can lead to better results:

Carefully Consider What Data to Collect

It’s too easy to get data about anything and everything, but it’s critical only to collect the required information. Consider these three questions:

  • What specific details do you need?
  • What details are available?
  • What details will be most useful?

Plan How to Collect Each Data Point

There is a lack of freely accessible data. Consider how much time and effort gathering each piece of information requires as you decide what data to acquire.

Consider the Price of Each Extra Data Point

Once you decide what data to gather, factor in the expense. Surveyors and respondents incur additional costs for every extra data point or survey question.

Consider Available Data Collection Options from Mobile Devices

Mobile-based data collecting can be split into three distinct categories:

  • Field surveyors. Thanks to smartphone apps, these surveyors directly enter data into interactive questionnaires while speaking to each respondent.
  • IVRS (interactive voice response technology). This method calls potential respondents and asks them pre-recorded questions.
  • SMS. This method sends a text message containing questions to the customer, who can then respond by text on their smartphone.

And while we’re talking about mobile devices…

  • Data collection via mobile devices is a big thing. Modern technology is increasingly relying on mobile devices. Collecting data from mobile devices is an easy, cost-effective tactic.
  • Don’t forget identifiers. Identifiers, or details that describe the source and context of a survey response, are just as important as the program or subject information being researched. Adding more identifiers lets you pinpoint the program’s successes and failures with greater accuracy.

Do You Want to Become a Data Scientist?

If you want to become a data scientist or just collect those skills, check out this 44-week data science bootcamp. You will learn the essential data science, machine learning, and analytical skills needed for a solid career in the field. shows that data scientists in the United States make an average yearly salary of $129,127. So, check out the bootcamp and enhance your critical data science skills!


Q: What do you mean by data collection?
A: It is the act of collecting and evaluating information or data from many sources to answer questions, find answers to research problems, evaluate outcomes, and forecast probabilities and trends.

Q: What are the five methods of collecting data?
A: The five data collection methods are:

  • Direct observation
  • Documents and records
  • Focus groups
  • Interviews
  • Surveys, quizzes, and questionnaires

Q: What are the benefits of data collection?
A: The benefits include:

  • Knowledge sharing and collaboration
  • Policy development
  • Evidence-based decision making
  • Problem identification and solutions
  • Validation and evaluation
  • Personalization and targeting
  • Identifying trends and predictions
  • Support for research and development
  • Quality improvement

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