What Is Data Collection? Data Collection Methods, Types, and Errors Explained

By the time you’ve finished reading this blog post, you’ll have learned more about what data collecting is, data types, common data collection methods, and more. You’ll also have learned about potential pitfalls along the way, as well as collecting, storing, and removing personal data with Brosix.

Let’s dig in.

Collecting Data (Key Takeaways)

• Data collection is the process of gathering and analyzing information for business, operations, or research.
• Data can be divided into two main types: qualitative and quantitative. The latter is raw, context-free information, while the qualitative data is contextual, explaining the “why” behind the numbers.
• Data can also be classified as primary (collected first-hand) or secondary (collected by others earlier).
• The five key data collection methods include document review, questionnaires/surveys, interviews, focus groups, and observations.
• Effective data collection ensures that businesses gather accurate, relevant data for analytics and decision-making. Using proper data collection tools and methods can minimize errors and improve research outcomes.
• Common mistakes in data collection include a population specification error, a sample frame error, a selection error,a  non-response error, and a measurement error. These can lead to inaccurate conclusions if not carefully avoided.
• Laws like HIPAA (for healthcare in the US) and GDPR (for personal data in the EU) govern how data is collected and stored.

What Is Data Collection? Definition

Data collection is the process of gathering and assessing vital information for your business, operation, or research. In other words, some data collection is inevitable and some is voluntary, as part of a goal-oriented task.

In the healthcare industry, for example, you have to collect and store data of your clients in order to provide the best healthcare service possible. The same applies to finance, where you have to have a social security number in order to open a bank account.

On the other hand, you could be collecting customer data in order to learn more about your clients’ behavior, predict trends in the market, and test those predictions. For example, you might want to know the number of clients who have downloaded your banking app, are actually using it, and if that statistic is close to your target.

You can also carry out data collection as part of a prevention strategy. In medicine, this would be reflected in patient monitoring with the goal of preventing further health issues.

Either way, data collection is happening, and in order for data to safely flow from one end to the other, it must be collected in a way that doesn’t harm the people whose information is shared online.

Key Steps in the Data Collection Process

Regardless of what data you want to collect and what data collection procedures you use, the process itself is always the same:

1. Setting a goal for data collection (example: find out how many gen x and baby boomers use your banking app)
2. Setting a schedule for data collection
3. Settling on data collection methods (example: will you only conduct a survey or will you also do in-person interviews)
4. Collecting and analyzing the data

Main Types of Data

Even though there are many ways to segment data, the two main types are quantitative data and qualitative data. The first gives you raw information, while the latter gives you context.

When you classify data according to how it was collected, or better yet, who collected it, there are two types: primary and secondary. All the data you gathered is primary, and all the data that someone else gathered earlier is secondary data. Both sets have their place in your research.

Let us tell you more.

Quantitative data

Questions usually asked when collecting qualitative data are closed-ended questions that have to do with amounts of things and frequencies of events (e.g., have you downloaded our banking app?).

Quantitative data is context-free and raw. You’ll need to accompany it with qualitative data that can shed some light on the gathered information. Otherwise, you might end up believing that eating ice cream causes sunburn (the legendary “correlation doesn’t imply causation” argument that depicts how a lack of data might lead to false conclusions).

Qualitative data tells you why the results of quantitative data collection are the way they are. For instance, by gathering survey results about the number of clients actively using your banking app, you can set yourself a mission to find out why the number is lower than expected—mission qualitative data collection.

Qualitative data

Collecting qualitative data gets you closer to understanding the reasons behind the numbers. In the case of our imaginary underused banking app, you might conclude that the demographic that is underperforming has low trust in technology when it comes to business transactions.

This conclusion might’ve begun as an assumption that you decided to test out. To test your theory, you’ve devised an interview that clearly addresses the possibility of your clients’ distrust of online transactions.

Both quantitative and qualitative data can be collected either by you during your research or by another organization at an earlier time.

Primary data

This type of data is first-hand data collected in real time. This entire blog post revolves more or less around it. It is costlier, takes longer to gather data, and requires greater involvement and a lot of planning. However, there’s also a higher data accuracy and relevance to your research goals.

Secondary data

Secondary data is data collected by someone else earlier. It is actually very important for budgeting your research. That’s because some of the data might already be publicly available, which is the case more often than not. It can even be data from your earlier research.

The most common secondary data collection methods are studying government statistics, publications from trade associations, journals, articles, university research reports, and financial and sales reports. These methods allow for fast and effective collection and analysis of reliable data.

Now that you and data are on a first-name basis, let’s learn the ways you can obtain it.

Primary Data Collection Tools and Methods

The five primary data collection methods are:

1. Document review
2. Questionnaires and surveys
3. Interviews
4. Focus groups
5. Observations

We can group them according to the type of data we need:

• Quantitative data (document review, questionnaires, and surveys)
• Qualitative data (interviews, focus groups, and observations)

You can also rely on the so-called mixed methods research, which combines elements from both qualitative and quantitative research methods.

Although data collection is undoubtedly valuable for business analytics, keep in mind that it can be unwanted and stressful for your current loyal customers. You should use any of the different methods listed below tactfully and carefully.

Quantitative data collection methods

Raw ingredients are always the starting point of any recipe; the same goes for data collection, and quantitative data is very much a raw ingredient. Below are the best quantitative methods used to collect raw data.

Document review

Document review is the process of assessing secondary data you obtained from various data sources like government statistics or a publicly available independent study. This should be the first step you take when conducting your research because you don’t want to waste resources doing work that someone else has already done.

During the document review, you’ll learn the objective of the study, how the data was collected, what the population of the study was, and what response categories were used in questionnaires (i.e., strongly agree, agree, not sure, disagree, strongly disagree).

Questionnaires and surveys

Think of questionnaires and surveys as Lego blocks and Lego figures.

Questionnaires are Lego blocks; when you create forms with closed-ended questions and send them to your target audience, who fill them out and return them, you’re left with a lot of Lego blocks that you might not know what to do with.

Surveys are Lego figures; they give the “blocks” of questionnaires meaning, and depending on the data quality, your survey “Lego figure” could look amazing. A survey is the entire process of handling raw data gathered from conducting questionnaires.

Finally, questionnaires and surveys might produce qualitative data as well. All it takes is asking open-ended questions.

Qualitative data collection methods

Collecting this data gives more insight into the quantitative data as well as opens opportunities for further research. Here’s what to do:

Conduct interviews

Interviews are probably the first thing that springs to mind when you think about data collection, and for good reason. A thoughtfully planned interview can reveal a wealth of precious information about a problem you’re trying to solve or a goal you’re trying to achieve.

There are two types of interviews or two stages if you’d like—structured and unstructured.

You would conduct structured interviews in the initial stages of interviewing job candidates and unstructured interviews in the later stages. Let’s see why.

Both can be carried out as in-person interviews or through various methods (phone, IM chat, video meetings).

[highlight_block title=”Learn how video meetings work.” link1=”https://www.brosix.com/blog/virtual-meetings/” link2=”” link3=”” ]

Structured interviews

These interviews are no-nonsense; here, you ask interviewees the same set of closed-ended questions in the same order with no additional ones.

They’re great because they’re easily quantifiable, they’re quick to conduct, and therefore easily applicable to a broader population. Another name for them is formal interviews, and they are categorized as quantitative data because of the lack of context they provide.

Unstructured interviews

These interviews consist of open-ended questions you would ask a candidate you’re seriously considering hiring for a job. Questions like “What would you change in our current marketing campaign?” or “What sustainability policies do you believe our company could improve on?”. In addition to asking open-ended questions, during an unstructured interview, you can steer the conversation wherever you think you’d gain more context and learn more about the interviewee.

The problem with unstructured interviews is that only trained interviewers can conduct them successfully, so you’ll need an interviewing expert on your team if you’re not already one.

Devise focus group interviews

Focus group interviews are a great tool for market research or for gaining a deeper understanding of social issues. The ingredients of a good focus group are five to eight participants from a deliberately selected group (e.g., Gen Zers) and a moderator.

You can think of focus groups as discussion panels. Everybody, including the moderator, has knowledge on the selected topic. The moderator’s job is to keep the conversation on topic and to politely manage participants who are dominating the conversation and motivate the ones who are yet to contribute their points of view.

Focus groups are best conducted as in-person interviews because a lot of observational data can be missed during a video conference, which brings us to the next qualitative method: observation.

Perform observation

One method of collecting qualitative data is observation. The outcome of this method is more information than statistical data. It depends heavily on the researcher’s ability to interpret the collected data, so it’s susceptible to bias. However, when conducted correctly, it’s a great way to determine pain points within your target group.

Two common examples of observational data collection are measurable observations and direct observations. An example of measurable observations in data science is the use of sensors to measure noise levels at an airport with the goal of determining which areas need to be soundproofed.

On the other hand, direct observation would be when Superintendent Chalmers watches Edna teach a class to Bart Simpson and other kids from Springfield Elementary to determine how well she delivers the curriculum.

Being aware of potential pitfalls can help steer you toward success. Here are some common data collection problems you might face during your research.

Faulty Data Collection Practices: Five Common Mistakes

The importance of data collection in data science lies in finding relevant data that can be analyzed to inform decisions. Effective data collection ensures that the right data is gathered, which is critical for accurate data analytics and insights.
Data collection is hard, but accurate data collection is even harder. Your goals and understanding of your audience/target market have to be clear. If not, your data collection plan could well be riddled with errors. The five most common are:

1. Population specification error
2. Sample frame error
3. Selection error
4. Non-responsive error
5. Measurement (observational) error

Population specification error

This happens when you wrongly assume a certain group is the target of your research. For instance, asking moms to rate ice cream flavors and designing your ice cream “lineup” for the following summer with their feedback in mind could drag your company into bankruptcy. Why? Because it’s not the moms who eat the most ice cream; it’s the kids who do.

Sample frame error

This is an error of covering a smaller sample of the target demographics than required for the research to be accurate.

A good example of this is a national survey conducted only via landlines. You’d be missing out on all the mobile users, leading to very skewed results.

Selection error

This is more of an on-field error that interviewers are susceptible to. Selection errors often happen during mall intercept interviews, where untrained interviewers tend to approach people belonging to a group they’re comfortable communicating with rather than approaching a wide range of passersby randomly.

Nonresponse error

This error happens when a significant number of participants fail to fill out the questionnaires and send them back. It could also mean that you haven’t sent the questionnaires to 100% of your sample group.

Measurement error

These can be systematic and random errors. There are four systematic errors: instrumental, environmental, observational, and theoretical. Let’s take the aforementioned example of measuring noise levels at the airport to explain them.

• An instrumental error would mean the airport was using faulty sensors to measure the noise level
• An environmental error would be that noise levels were measured both during airport downtime and at active time
• An observational error would be that airport staff had drawn wrong conclusions from the sensors’ data
• A theoretical error would be that the airport conducted the research believing that noise was causing the lack of air travel during the last two quarters. In fact, in the last two quarters, there were fewer travelers due to the pandemic. Therefore, attributing noise as the main factor would be a theoretical error (the wrong theory behind the assumption)
• A random error example would be airport staff experiencing burnout due to being understaffed, thus increasing human error

Data Collection Regulations

More and more regulations are being enforced over data collection, and rightly so. As a result, healthcare and pharmaceutical companies in the US have to adhere to the HIPAA (Health Insurance Portability and Accountability Act) federal law, while all EU-based businesses that handle their users’ personal data have to adhere to the GDPR (General Data Protection Regulation) law when they obtain data.

HIPAA is, as stated by the Centers for Disease Control and Prevention, ”a federal law that required the creation of national standards to protect sensitive patient health information from being disclosed without the patient’s consent or knowledge.”

GDPR is a regulation that allows individuals to disclose as little personal information as they wish when online. At the same time, it holds companies accountable for their data collection malpractices, and the EU’s data protection authorities can fine them up to almost $21 million.

When using Brosix to share client information internally, you are never in breach of HIPAA or GDPR regulations. If you are in the patient care industry, Brosix is your go-to healthcare communication app since it’s HIPAA-compliant.

Now that you know a thing or two about how data collection is regulated, let’s learn how to conduct it.

[highlight_block title=”If you’d like to dive deeper into data privacy issues, check out some of our other articles:” link1=”https://www.brosix.com/blog/remote-work-best-practices/” link2=”https://www.brosix.com/blog/wire-vs-signal/” link3=”https://www.brosix.com/blog/upward-communication/” ]

Data Collection and Data Security With Brosix

As far as user data is concerned, Brosix never stores user data on its servers unless explicitly requested. Also, users can delete or change their information at any time. Basically, you can have a Brosix account with zero personal information on it. That’s why we don’t purge unused accounts.

One of our data security features is our Web Control Panel, which allows you to restrict access to specific users’ data. Another feature is the end-user authentication process, which makes it impossible for third-party users to hack into your network, maintaining data integrity.

Thanks to Brosix’s safety features like peer-to-peer file transfer and heavy-duty data encryption, companies can share client information internally without fear of a third-party data breach. Even Brosix can’t access the data, as it’s encrypted on the sender’s end and decrypted on the receiver’s end.

Conclusion

Data collection is a one thousand-piece jigsaw puzzle of a painting from the impressionist era, and each puzzle piece represents a valuable piece of information. It takes planning, separating the puzzle pieces into groups, assembling patches, and connecting them to slowly reveal the big picture.

Essentially, the better you prepare your research, the better the outcome of your data collection will be. If you set your goals well and target interviews, surveys, and observations accordingly and avoid bias, the data produced will be able to answer your research questions, evaluate your hypothesis, and even successfully forecast trends.

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