- \n
- Data collection is the process of gathering and analyzing information<\/strong> for business, operations, or research.<\/li>\n\n\n\n
- Data can be divided into two main types: qualitative and quantitative<\/strong>. The latter is raw, context-free information, while the qualitative data is contextual, explaining the “why” behind the numbers.<\/li>\n\n\n\n
- Data can also be classified as primary (collected first-hand) or secondary (collected by others earlier).<\/li>\n\n\n\n
- The five key data collection methods<\/strong> include document review, questionnaires\/surveys, interviews, focus groups, and observations.<\/li>\n\n\n\n
- Effective data collection ensures that businesses gather accurate, relevant data<\/strong> for analytics and decision-making. Using proper data collection tools and methods can minimize errors and improve research outcomes.<\/li>\n\n\n\n
- Common mistakes <\/strong>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.<\/li>\n\n\n\n
- Laws like HIPAA <\/strong>(for healthcare in the US) and GDPR <\/strong>(for personal data in the EU) govern how data is collected and stored.<\/li>\n<\/ul>\n\n\n\n
What Is Data Collection? Definition<\/h2>\n\n\n\n
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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
On the other hand, you could be collecting customer data in order to learn more about your clients\u2019 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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
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\u2019t harm the people whose information is shared online.<\/p>\n\n\n\n
Key Steps in the Data Collection Process<\/h2>\n\n\n\n
![\"Data](\"https:\/\/www.brosix.com\/wp-content\/uploads\/Data-collection-process.png\")
<\/figure>\n\n\n\nRegardless of what data you want to collect and what data collection procedures you use, the process itself is always the same:<\/p>\n\n\n\n
- \n
- Setting a goal for data collection (example: find out how many gen x and baby boomers use your banking app)<\/li>\n\n\n\n
- Setting a schedule for data collection<\/li>\n\n\n\n
- Settling on data collection methods (example: will you only conduct a survey or will you also do in-person interviews)<\/li>\n\n\n\n
- Collecting and analyzing the data<\/li>\n<\/ol>\n\n\n\n
Main Types of Data<\/h2>\n\n\n\n
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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
Let us tell you more.<\/p>\n\n\n\n
Quantitative data<\/h3>\n\n\n\n
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?).<\/p>\n\n\n\n
Quantitative data is context-free and raw. You\u2019ll 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 \u201ccorrelation doesn\u2019t imply causation\u201d argument that depicts how a lack of data might lead to false conclusions).<\/p>\n\n\n\n
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\u2014mission qualitative data collection.<\/p>\n\n\n\n
Qualitative data<\/h3>\n\n\n\n
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.<\/p>\n\n\n\n
This conclusion might\u2019ve begun as an assumption that you decided to test out. To test your theory, you\u2019ve devised an interview that clearly addresses the possibility of your clients\u2019 distrust of online transactions.<\/p>\n\n\n\n
Both quantitative and qualitative data can be collected either by you during your research or by another organization at an earlier time.<\/p>\n\n\n\n
Primary data<\/h3>\n\n\n\n
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\u2019s also a higher data accuracy and relevance to your research goals.<\/p>\n\n\n\n
Secondary data<\/h3>\n\n\n\n
Secondary data is data collected by someone else earlier. It is actually very important for budgeting your research. That\u2019s 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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
Now that you and data are on a first-name basis, let\u2019s learn the ways you can obtain it.<\/p>\n\n\n\n
Primary Data Collection Tools and Methods<\/h2>\n\n\n\n
![\"Data](\"https:\/\/www.brosix.com\/wp-content\/uploads\/Data-collection-method-roundup.png\")
<\/figure>\n\n\n\nThe five primary data collection methods are:<\/p>\n\n\n\n
- \n
- Document review<\/li>\n\n\n\n
- Questionnaires and surveys<\/li>\n\n\n\n
- Interviews<\/li>\n\n\n\n
- Focus groups<\/li>\n\n\n\n
- Observations<\/li>\n<\/ol>\n\n\n\n
We can group them according to the type of data we need:<\/p>\n\n\n\n
- \n
- Quantitative data (document review, questionnaires, and surveys)<\/li>\n\n\n\n
- Qualitative data (interviews, focus groups, and observations)<\/li>\n<\/ul>\n\n\n\n
You can also rely on the so-called mixed methods research, which combines elements from both qualitative and quantitative research methods.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
Quantitative data collection methods<\/h3>\n\n\n\n
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.<\/p>\n\n\n\n
Document review<\/h4>\n\n\n\n
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\u2019t want to waste resources doing work that someone else has already done.<\/p>\n\n\n\n
During the document review, you\u2019ll 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).<\/p>\n\n\n\n
Questionnaires and surveys<\/h4>\n\n\n\n
Think of questionnaires and surveys as Lego blocks and Lego figures.<\/p>\n\n\n\n
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\u2019re left with a lot of Lego blocks that you might not know what to do with.<\/p>\n\n\n\n
Surveys are Lego figures; they give the \u201cblocks\u201d of questionnaires meaning, and depending on the data quality, your survey \u201cLego figure\u201d could look amazing. A survey is the entire process of handling raw data gathered from conducting questionnaires.<\/p>\n\n\n\n
Finally, questionnaires and surveys might produce qualitative data as well. All it takes is asking open-ended questions.<\/p>\n\n\n\n
Qualitative data collection methods<\/h3>\n\n\n\n
Collecting this data gives more insight into the quantitative data as well as opens opportunities for further research. Here\u2019s what to do:<\/p>\n\n\n\n
Conduct interviews<\/h4>\n\n\n\n
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\u2019re trying to solve or a goal you\u2019re trying to achieve.<\/p>\n\n\n\n
There are two types of interviews or two stages if you\u2019d like\u2014structured and unstructured.<\/p>\n\n\n\n
You would conduct structured interviews in the initial stages of interviewing job candidates and unstructured interviews in the later stages. Let\u2019s see why.<\/p>\n\n\n\n
Both can be carried out as in-person interviews or through various methods (phone, IM chat, video meetings).<\/p>\n\n\n
[highlight_block title=”Learn how video meetings work.” link1=”https:\/\/www.brosix.com\/blog\/virtual-meetings\/” link2=”” link3=”” ]<\/p>\n\n\n\n
Structured interviews<\/h5>\n\n\n\n
These interviews are no-nonsense; here, you ask interviewees the same set of closed-ended questions in the same order with no additional ones.<\/p>\n\n\n\n
They\u2019re great because they\u2019re easily quantifiable, they\u2019re 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.<\/p>\n\n\n\n
Unstructured interviews<\/h5>\n\n\n\n
These interviews consist of open-ended questions you would ask a candidate you\u2019re seriously considering hiring for a job. Questions like \u201cWhat would you change in our current marketing campaign?\u201d or \u201cWhat sustainability policies do you believe our company could improve on?\u201d. In addition to asking open-ended questions, during an unstructured interview, you can steer the conversation wherever you think you\u2019d gain more context and learn more about the interviewee.<\/p>\n\n\n\n
The problem with unstructured interviews is that only trained interviewers can conduct them successfully, so you\u2019ll need an interviewing expert on your team if you\u2019re not already one.<\/p>\n\n\n\n
Devise focus group interviews<\/h4>\n\n\n\n
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.<\/p>\n\n\n\n
You can think of focus groups as discussion panels. Everybody, including the moderator, has knowledge on the selected topic. The moderator\u2019s 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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
Perform observation<\/h4>\n\n\n\n
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\u2019s ability to interpret the collected data, so it\u2019s susceptible to bias. However, when conducted correctly, it\u2019s a great way to determine pain points within your target group.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
Being aware of potential pitfalls can help steer you toward success. Here are some common data collection problems you might face during your research.<\/p>\n\n\n\n
Faulty Data Collection Practices: Five Common Mistakes<\/h2>\n\n\n\n
![\"Faulty](\"https:\/\/www.brosix.com\/wp-content\/uploads\/Faulty-data-collection-practices.png\")
<\/figure>\n\n\n\nThe 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:<\/p>\n\n\n\n- \n
- Population specification error<\/li>\n\n\n\n
- Sample frame error<\/li>\n\n\n\n
- Selection error<\/li>\n\n\n\n
- Non-responsive error<\/li>\n\n\n\n
- Measurement (observational) error<\/li>\n<\/ol>\n\n\n\n
Population specification error<\/h3>\n\n\n\n
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 \u201clineup\u201d for the following summer with their feedback in mind could drag your company into bankruptcy. Why? Because it\u2019s not the moms who eat the most ice cream; it\u2019s the kids who do.<\/p>\n\n\n\n
Sample frame error<\/h3>\n\n\n\n
This is an error of covering a smaller sample of the target demographics than required for the research to be accurate.<\/p>\n\n\n\n
A good example of this is a national survey conducted only via landlines. You\u2019d be missing out on all the mobile users, leading to very skewed results.<\/p>\n\n\n\n
Selection error<\/h3>\n\n\n\n
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\u2019re comfortable communicating with rather than approaching a wide range of passersby randomly.<\/p>\n\n\n\n
Nonresponse error<\/h3>\n\n\n\n
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\u2019t sent the questionnaires to 100% of your sample group.<\/p>\n\n\n\n
Measurement error<\/h3>\n\n\n\n
These can be systematic and random errors. There are four systematic errors: instrumental, environmental, observational, and theoretical. Let\u2019s take the aforementioned example of measuring noise levels at the airport to explain them.<\/p>\n\n\n\n
- \n
- An instrumental error would mean the airport was using faulty sensors to measure the noise level<\/li>\n\n\n\n
- An environmental error would be that noise levels were measured both during airport downtime and at active time<\/li>\n\n\n\n
- An observational error would be that airport staff had drawn wrong conclusions from the sensors\u2019 data<\/li>\n\n\n\n
- 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)<\/li>\n\n\n\n
- A random error example would be airport staff experiencing burnout due to being understaffed, thus increasing human error<\/li>\n<\/ul>\n\n\n\n
Data Collection Regulations<\/h2>\n\n\n\n
![\"Data](\"https:\/\/www.brosix.com\/wp-content\/uploads\/Data-collection-regulations.png\")
<\/figure>\n\n\n\nMore 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\u2019 personal data have to adhere to the GDPR (General Data Protection Regulation) law when they obtain data.<\/p>\n\n\n\n
HIPAA is, as stated by the Centers for Disease Control and Prevention<\/a>, \u201da federal law that required the creation of national standards to protect sensitive patient health information from being disclosed without the patient\u2019s consent or knowledge.\u201d<\/p>\n\n\n\n
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\u2019s data protection authorities can fine them up to almost $21 million.<\/p>\n\n\n\n
When using Brosix to share client information internally, you are never in breach of HIPAA<\/a> or GDPR<\/a> regulations. If you are in the patient care industry, Brosix is your go-to healthcare communication app<\/a> since it’s HIPAA-compliant.<\/p>\n\n\n\n
Now that you know a thing or two about how data collection is regulated, let\u2019s learn how to conduct it.<\/p>\n\n\n
[highlight_block title=”If you\u2019d 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\/” ]<\/p>\n\n\n\n
Data Collection and Data Security With Brosix<\/h2>\n\n\n\n
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\u2019s why we don\u2019t purge unused accounts.<\/p>\n\n\n\n
One of our data security features is our Web Control Panel<\/a>, which allows you to restrict access to specific users\u2019 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.<\/p>\n\n\n\n
Thanks to Brosix<\/a>\u2019s safety features like peer-to-peer file transfer<\/a> and heavy-duty data encryption, companies can share client information internally without fear of a third-party data breach. Even Brosix can\u2019t access the data, as it\u2019s encrypted on the sender\u2019s end and decrypted on the receiver\u2019s end.<\/p>\n\n\n\n
Conclusion<\/h2>\n\n\n\n
![\"Conclusion\"](\"https:\/\/www.brosix.com\/wp-content\/uploads\/Data-collection-Conclusion.png\")
<\/figure>\n\n\n\nData 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.<\/p>\n\n\n\n
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.<\/p>\n\n\n
[highlight_block title=”Try Brosix for free and see what features you can add to your arsenal!” link1=”https:\/\/www.brosix.com\/request-demo\/” link2=”” link3=”” ]<\/p>\n\n\n\n
FAQ<\/h2>\n\n\n
\n\n\nWhat are some real-life examples of data?<\/h3>\n
\n\nData collected by healthcare practitioners on a daily basis are medications and prescriptions administered to patients, operations data, and encounter and discharge forms. Financial institutions typically collect assets, liabilities, equity, cash flow, income, and expenses.
Data that is collected in real estate is the purpose, value, and ownership of a property, and municipal changes in the area of the property.<\/p>\n\n<\/div>\n<\/div>\n\nWhat are some examples of data collection?<\/strong><\/h3>\n
\n\nAn example of data collection is a supermarket observing the walking patterns of customers to determine where to build a new pathway. Another example is when evaluating to what degree a new ointment reduces muscle fatigue in sports professionals or using an F1 simulator used by Formula One pilots to prepare for races.<\/p>\n\n<\/div>\n<\/div>\n
\nWhat are the four data collection types?<\/h3>\n
\n\nThe four different types of data collection are observational, experimental, simulation, and derived.
Observational type is conducted through human observation, open-eyed surveys, and the use of data collection instruments.
Experimental data collection is conducted in order to determine the relationship between two variables.
Simulation data collection is a research technique in which you reproduce actual events under test conditions.
Derived data collection is the process of pulling existing data from various sources. Researchers do this as part of their secondary data collection methods.<\/p>\n\n<\/div>\n<\/div>\n\nWhat is the difference between big data and data science?<\/h3>\n
\n\nBig data refers to large volumes of complex data that are difficult to process using traditional methods, while data science involves analyzing and interpreting that data to extract meaningful insights. Essentially, big data is the “raw material,” and data science is the process of making sense of it.<\/p>\n\n<\/div>\n<\/div>\n
\nHow does data visualization aid in data collection?<\/strong><\/h3>\n
\n\nData visualization aids in data collection by providing a graphical representation of data, making it easier to understand trends and patterns. This can enhance the interpretation of qualitative and quantitative data, leading to better insights and decision-making.<\/p>\n\n<\/div>\n<\/div>\n
\nHow can I ensure the quality of the data I collect?<\/h3>\n
\n\nWhen you collect data, it is essential to employ effective data collection tools, utilize proper sampling methods, and establish clear data collection procedures. This helps minimize data quality issues such as inconsistent data, ensuring that data is reliable for analysis.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"
By the time you\u2019ve finished reading this blog post, you\u2019ll have learned more about what data collecting is, data types, common data collection methods, and more. You\u2019ll also have learned about potential pitfalls along the way, as well as collecting, storing, and removing personal data with Brosix. Let\u2019s dig in. Collecting Data (Key Takeaways) What […]<\/p>\n","protected":false},"author":2,"featured_media":20896,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-20888","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-remote-work"],"_links":{"self":[{"href":"https:\/\/www.brosix.com\/blog\/wp-json\/wp\/v2\/posts\/20888","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.brosix.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.brosix.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.brosix.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.brosix.com\/blog\/wp-json\/wp\/v2\/comments?post=20888"}],"version-history":[{"count":0,"href":"https:\/\/www.brosix.com\/blog\/wp-json\/wp\/v2\/posts\/20888\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.brosix.com\/blog\/wp-json\/wp\/v2\/media\/20896"}],"wp:attachment":[{"href":"https:\/\/www.brosix.com\/blog\/wp-json\/wp\/v2\/media?parent=20888"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.brosix.com\/blog\/wp-json\/wp\/v2\/categories?post=20888"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.brosix.com\/blog\/wp-json\/wp\/v2\/tags?post=20888"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- Data can be divided into two main types: qualitative and quantitative<\/strong>. The latter is raw, context-free information, while the qualitative data is contextual, explaining the “why” behind the numbers.<\/li>\n\n\n\n