Data collection is essential for businesses, organizations, and even personal use. In the digital age data is one of the most valuable resources at your disposal.
The right data, used properly, can propel your brand forward by helping you make the right decisions in areas such as choosing a market segment, finding the ideal marketing mix, financial decisions, and more. When used incorrectly, it can seem like the choices being made by you or your team are always falling short.
How can you make sure you have the right information to make important decisions? By adopting sound data collection methods and analysis. In this guide, you’ll learn:
- The types of data you can collect
- 7 data collection methods
- Steps to collect useful data using the methods you learn
What is data collection
Data collection is the process of gathering and categorizing relevant information that can then be used to make decisions about specific situations. As you can tell from the definition, it’s not a process that’s only for business.
In every aspect of our lives, we go through the process of data collection. For example, if you want to move to a new city, you collect as much data as you can. When assessing a new job offer, you collect data about the company’s growth, salary scale, etc.
Moreover, data collection should imply a kind of summarising information with insights you’ve collected. It helps to understand all the main points about the subject in question. To add some cool design elements to this process, you can use one-pager templates.
By analyzing the data you’ve gathered, you can uncover patterns, trends, and correlations that provide valuable insights. These insights guide informed decision-making and enable you to make well-informed choices based on evidence.
Amidst the digital age, data collection has taken on new dimensions. In essence, data collection has transcended its conventional boundaries, becoming a driving force behind technological innovation and societal progress.
In a business setting, the data collection process and methods are more formal and tend to yield better outcomes as a result. That’s in part due to a clear delineation between the types of data that can be collected.
Within this stage, data processing assumes a pivotal role, orchestrating the orchestration, refinement, and translation of amassed data into a format that’s both pragmatic and navigable.
The modern landscape sees businesses and institutions capitalizing on data’s potential, propelling data processing techniques to unprecedented sophistication.
This evolution, influenced by skills cultivated in a data science bootcamp, strategically empowers the extraction of concealed patterns and invaluable insights. It fundamentally reshapes the landscape of decision-making and issue resolution across diverse industries.
The tools and ways we handle data keep getting better and better, helping people and groups manage lots of data well. Doing this not only speeds up decision-making, but also makes the conclusions we reach more accurate and meaningful.
Also, as data becomes more and more important for how things work, its role in guessing what might happen and predicting future trends is getting even more important.
This evolution empowers the extraction of concealed patterns and invaluable insights, fundamentally reshaping the landscape of decision-making and issue resolution across diverse industries.
Types of data you can collect
The type of data you collect determines how much you can trust it and the versatility. There are two major types of data that can be further broken down into subcategories.
Primary data collection
Primary data, also known as raw data, is the data you collect yourself and are the first person to interpret. It’s data that’s gotten directly from the source. That could be in-person interviews, surveys sent out to your audience, or even courses. Put another way, you’re the first person or group to interact with and draw conclusions from the data.
Primary data is usually collected with a specific goal in mind but can be more challenging for the researcher to interpret. That’s because the data is unstructured and needs to be arranged in a way that allows you to make meaningful decisions.
Secondary data collection
Secondary data refers to information you use which has been collected, analyzed, and structured by another person or group. Things like research papers, books, other websites, etc. can be considered primary data that, when used by you, are secondary data.
This type of data is much easier to collect and use but it may not be as applicable to your situation. For example, HubSpot does a survey of marketers every year and publishes its findings in a report called The State of Inbound. The data is high quality but may not be as useful to your specific situation even if you serve marketers.
Both primary and secondary data can be broken down into subcategories referred to as qualitative and quantitative data.
Qualitative data collection
Qualitative data is information that’s descriptive in nature. It’s used to understand and characterize a problem, sentiment, or an individual/group. It can be recorded and measured but cannot be quantified using numbers.
For example, you can record that someone is unhappy and measure the level of unhappiness using descriptive words but it can’t be quantified. This kind of primary data is gathered using interviews, open-ended survey questions, etc. and can be used to answer the question “why?” Secondary data can be gathered from firsthand accounts such as a journal.
Quantitative data collection
Quantitative data is information gathered in numerical form and, as a result, can be easily ordered and ranked. This data is necessary for calculations and further statistical analysis. Just like with qualitative data, the information derived here can be used to make decisions in a personal or business setting.
Quantitative data is easier to handle and measure because it’s not open to different interpretations. For example, if you ask someone how many times they’ve gone to the gym this week, there’s a simple numerical answer. If you asked someone why they went to the gym, their answer can be interpreted in different ways depending on who’s analyzing it.
Primary quantitative data is gathered using close ended survey questions and rigid one-on-one interviews. Secondary data can be gathered through published research and official statistics. Quantitative data answers the questions “how much” “how often” and “how many.”
During data room due diligence, the focus is on a comprehensive review of confidential documents and information. The goal is to scrutinize and verify critical records. This most often involves details about money stuff, legalities, and how the business tick. This is important when companies are talking about teaming up or one’s taking over the other.
For instance, imagine a company wanting to buy another – they’d be snooping around financial papers, legal contracts, and how things run day-to-day to decide if it’s a good move.
7 Data collection methods
There are multiple data collection methods and the one you’ll use will depend on the goals of your research and the tools available for analysis. Let’s look at each one in turn.
1. Close ended question surveys
Close ended survey questions fall under quantitative primary data collection. It’s the process of using structured questions with a predefined series of answers to choose from. Keep in mind that close ended questions can be combined with open-ended questions within the same survey.
That means you’re able to collect quantitative and qualitative data from the same respondent. A good example of this would be an NPS survey. The first question includes a rating scale while the second question is an open-ended question and seeks to understand the reason behind the answer.
Likert scale questions (which is an interval scale) also fall under this category. They’re ideal for measuring the degree of something like frequency or feeling.
- They’re inexpensive and can be sent out to many people
- People are able to answer anonymously
- It’s easy to analyze the data received because the survey software will do a lot of the work
- The response rate is lower
- You’re unable to ask clarifying questions in most cases
- Many respondents won’t complete the entire survey
2. Open-ended surveys
Open-ended survey questions are ideal when you’re trying to understand the motivations, characteristics, or sentiment behind a stance. You’re able to capture data that close ended questions simply can’t give you.
While open-ended survey questions can yield a wealth of insights, it’s important not to overdo it. When you have too many open-ended questions or they’re too complex, fatigue sets in. This increases the likelihood that your respondents will abandon the survey altogether, leaving you with incomplete data.
- They yield more insights
- You can get voice of customer data to use in marketing campaigns like social media, email marketing, and SEO campaigns.
- Can be used to probe different angles of a problem even if you don’t have prior experience
- Much more difficult to analyze
- Still can’t ask clarifying questions
- Answers may be all over the place and hard to group
Interviews are a tried and tested way to collect qualitative data and have many advantages over other types of data collection. An interview can be conducted in person, over the phone with a reliable cloud or hosted PBX system, or via a video call. The in-person method is ideal because you’re able to read body language and facial expressions and pair it with the responses being given.
There are three main types of interviews. A structured interview which can be considered a questionnaire that’s given verbally. There’s little to no deviation from the questions that were set in the beginning. A semi-structured interview has a general guideline but gives the interviewer the leeway to explore different areas based on the responses received. An unstructured interview has a clear purpose but the interviewer is able to use their discretion about the type of questions to ask, what to explore, and what to ignore. This gives the most flexibility.
- Gather deep insights from people interviewed
- Ability to explore interesting topics on the fly
- Develop a more nuanced understanding of the problem or situation at hand
- The data tends to be more accurate because of the clarifying questions that can be posed
- Expensive to do them at scale
- May be difficult to coordinate schedules with the person being interviewed
- Much more time consuming than other methods
4. Online analytics tools
In the digital age, there are countless analytics tools you can use to track and understand user behavior. If you have a website or app, you’ll be able to gather a wealth of data. For example, using Google Analytics, you can see the most popular pages, how many people are visiting them, the path they take before converting, and so much more.
With those insights, you can optimize different aspects of the sales funnel and improve your results over time.
- Understand how people are interacting with your web properties
- Create tests and hypothesis to improve your results
- Unable to interact with visitors in a meaningful way
- The data is limited and doesn’t tell you why certain things happen
5. Observational data collection
This is one of the most passive data collection methods and may not be the best first choice. The researcher can observe as a neutral third party or as a participant in the activities going on.
Because of this, it’s possible to introduce biases into the research which will affect the quality of the data. As a participant, their attitudes or perception of what’s being observed may be skewed in one direction or another and make it hard to remain objective.
Think of Google Sheets alternatives as your helpful companions in research. They offer an array of user-friendly tools, perfect for crafting surveys and diving into data analysis.
Need to stay objective? Doing scientific research or market analysis? They are super handy and make data collection a breeze.
So, on your next research journey, consider these alternatives your trusty sidekicks, making your work smoother.
- It’s widely accepted
- Can be applied in many of situations
- Relatively easy to set up and execute
- More difficult to remain objective
- Some things cannot be observed by a researcher
6. Focus groups
Focus groups are similar to interviews but take advantage of a group. A focus group comprises of 3 – 10 people and an observer/moderator. Fewer than that and you’re better off doing interviews and any more than that may be unmanageable.
It’s ideal when you’re trying to recreate a specific situation or want to test different scenarios and see how people will react. The best results come when the participants fit a specific demographic or psychographic profile.
- The information is insightful and reliable
- It’s more economical than hosting individual interviews
- You can also collect quantitative data by administering surveys at the beginning of the session
- More expensive than other methods
- Participants can become the victims of groupthink
- Difficult to coordinate the schedule of multiple participants
- Need specialized researchers to moderate the group
7. Research or reported data collection
This data collection method is used when you can’t take advantage of primary data. Instead, you’re able to use information that has already been gathered from primary sources and made available to the public. In some cases, the information is free to use and in other cases, you may have to pay to gain access. For example, some research papers require payment.
- Faster than in-person interviews
- You can use multiple data sources together to get a more holistic picture
- Reliant on the quality of the third party for your data
- It may be difficult to find data that’s directly related to the problem you want to solve
Important steps to collect useful data
At this point, you know the data collection methods available, their pros, and their cons. Now, let’s look at the steps required to collect meaningful data.
Determine the goal for the data collection
You can collect data and store it until it becomes useful one day. This doesn’t help you or prove the case for the resources you expend to get the data in the first place. Before you implement any data collection strategy, take a moment to understand where it’ll be applicable.
Who will you collect the data from, where and how will you use it? Will you exclude certain audiences completely?
For example, if you’re sending out a survey, what are you trying to measure and improve? Is a customer satisfaction survey, price sensitivity survey, or countless other types of surveys best? Each one has its own nuances, pros, and cons.
How long you’ll collect the data
In a few cases, you can collect data indefinitely and continually update your assessments. For example, you should be collecting analytics data from your website at all times. In most cases, there should be a hard stop date for your data collection. After that, you can start to analyze it, draw conclusions, and implement changes.
For example, you may want to record analytics data about an A/B test you’re running over the course of a month. It has a definite end date because you can’t analyze the data until the experiment is over.
Choose a data collection method
Set aside time to consider different data collection methods. You should pick a primary channel, and think about secondary options. For example, you might decide to collect data by asking people on your email list to fill in an online survey. A secondary method might be advertising the survey offline in your store. You could use a dynamic QR code generator to make it easy to access the survey.
If you use the wrong data collection method then it can severely impact the quality and usefulness of your data. For example, if you’re exploring a new product category and don’t have deep knowledge about the customers and competitors, a close ended survey will strengthen assumptions that may or may not be correct.
In this case, an open-ended survey where people can give more details would be better. Once you’ve finished that initial data collection exercise, you can confirm or invalidate many assumptions and then send out a close ended survey with more confidence.
Implement your data collection strategy
After you’ve done the initial planning and research, it’s time to implement it. Be flexible here because you may realize that the data collection method you chose isn’t ideal or the timeframe isn’t long enough to give you meaningful data. In those situations, you may want to change course or scrap the exercise and start over.
Analyze and draw conclusions from the data
The last step is the most important. At this point, your raw data isn’t too useful but when you categorize and quantify it, you can tease out insights that can be used in multiple areas.
Even after the initial analysis, it’s a good idea to get a third party to take a look or someone else in your organization. They may draw different conclusions than you which can open the doors for better results in marketing or operations.
In the domain of decision-making driven by data, possessing a data integration tool of utmost reliability is crucial. The ultimate step holds the highest significance since it empowers you to convert raw data into insights of great value.
By utilizing a data integration tool to categorize and quantify the information, you unlock its genuine potential, extracting valuable nuggets that can be applied in various domains.
In the realm of data-influenced decision-making, the significance of data integration emerges as a linchpin. It serves as the connective tissue between disparate data fragments, orchestrating a symphony of information that underpins informed choices.
This harmonious integration doesn’t just streamline operations, but infuses a deeper understanding, transforming raw data into a tapestry of insights.
By embracing robust data integration practices, organizations can seamlessly traverse the data landscape, unveiling nuanced perspectives that propel progress and innovation forward.
Nonetheless, even following the initial analysis, it is wise to solicit input from a third party or a colleague within your organization.
Their fresh viewpoint may result in different conclusions, introducing unpredictability into your process of exploring data, ultimately paving the way for improved outcomes in marketing and operations.
Their fresh viewpoint may result in different conclusions, introducing an element of unpredictability into your process of exploring data, ultimately paving the way for improved outcomes in marketing and operations, especially when supplemented with AI marketing analytics.
Furthermore, the wisdom of seeking input from impartial third parties or knowledgeable colleagues remains invaluable even post-initial analysis. Their distinct viewpoints may unveil unconventional conclusions, infusing an element of unpredictability into your data exploration process and potentially enhancing outcomes in marketing and operations.
The paramount importance of data security and protection cannot be overstated in the world of data collection. In an era marked by rapid technological evolution and the escalating worth of information, safeguarding data is imperative.
Collaborating with a renowned data science consulting company that specializes in securing valuable insights can offer invaluable guidance in mitigating the risks of data breaches. These firms are experts in upholding data confidentiality and protection, vigilantly guarding against potential threats and vulnerabilities while ensuring strict compliance with data privacy regulations.
Data security and protection
Collecting data is great, but what about safety and security? With the rapid advancement of technology and the value of information, having robust practices to stay safe from data breaches is a must. And the risk is not only about leaking valuable insights but also facing lawsuits from people whose details it is. Managing data is not something to take lightly.
In the realm of the digital world, ensuring the protection of data is just as important as its acquisition.
The integration of big data in banking has revolutionized the way financial institutions approach data collection and analysis. By harnessing vast amounts of data, banks can now gain deeper insights into customer behavior, enhance risk management, and drive innovation in product development.
In today’s digital age, both individuals and businesses are constantly seeking ways to build their credit in a secure manner. Chime’s Credit Builder addresses this need by offering a solution that is not just a secure credit card but also a tool aimed at enhancing financial health.
This service is meticulously designed to help users manage their finances more effectively while ensuring that their data remains protected. By leveraging Chime’s Credit Builder, users can enjoy the benefits of a financial product that supports their credit-building goals without the risk of compromising their personal or business data security.
This shift towards data-driven strategies in the banking sector underscores the importance of effective data collection methods. As banks continue to navigate the complexities of big data, the need for robust and versatile data collection techniques becomes increasingly vital.
This is precisely where our company excels, providing a NAKIVO solution for Microsoft 365 backup. By utilizing this powerful tool, you can guarantee the security of your emails, contacts, and calendar entries, effectively safeguarding your business against any potential losses.
First and foremost, make sure that your data collection methods don’t compromise your customers’ security online. Add to that, you can use a secure online data room to handle, process, and analyze confidential customer information. That way, you will be able to benefit from the insights you collect without risking any data leaks or security breaches.
Data is what makes the world go round and there are many data collection methods you can use to gain insights into your market. The one you choose will depend heavily on your goals, your customer base, and the resources available to your organization.
In a digital world where data is a treasure, ensuring its protection is akin to safeguarding a precious gem. Just as a well-secured vault protects its contents, implementing robust VPN encryption shields your valuable data from potential threats.
Lance Gross from VPNBrains emphasizes this, saying, “In today’s data-driven world, VPN encryption is not just a luxury but a necessity. It acts like an armored car for your data, transporting it securely across the Internet highway.
With the increasing risk of data breaches and cyberattacks, fortifying your data with VPN encryption becomes imperative. By deploying this powerful tool, you create a virtual fortress around your data, ensuring that it remains confidential from prying eyes.
In the intricate web of our digital era, data emerges as a highly coveted asset, demanding an enhanced level of protection equivalent to safeguarding precious gems within a fortified vault. Just as a secure vault diligently shields its contents, the utilization of potent VPN encryption stands as an unwavering guard that shields your invaluable data from looming threats.
The mounting risks associated with data breaches and cyber assaults underscore the absolute necessity of reinforcing your data with VPN encryption. This powerful tool assumes an imperative role, forming an intangible fortress that encapsulates your data, guaranteeing its confidentiality and resilience against the prying eyes of unauthorized access.
By instituting a VPN, your data is intricately routed through an encrypted conduit, robustly securing it against potential breaches and unwarranted intrusions, significantly enhancing the security and privacy of your digital transactions and communications.
Don’t look at any methods as being better than another. Rather, look at them as being appropriate for specific situations. Start your data collection journey by choosing the collection method that’s the easiest for you to implement right now and work your way up as you start to see results from it.