One of the most commonly used phrases in the business world is "data-driven" — and for a good reason. Data is essential to any business and can help you make better decisions in product development, customer service, and marketing strategy.
Everyone knows that mastering the art of collecting, analyzing, and using data is a key success differentiator. But there’s more to it than beating your competitors in collecting huge amounts of data—to reap its benefits, you must know which data to collect and how to look at it.
With our step-by-step data collection guide, you’ll have the tools to find the right data for your needs (and avoid the common pitfalls).
What Is Data Collection?
Data is information that can be collected and stored. Every day we come across data without even realizing it—a restaurant menu, the weather forecast, stock market values, and even the information in this article…
Data collection is the process of gathering data from various sources and organizing it in a systematic manner. A telephone survey, observation, social media monitoring, a sign-in form, or an in-depth interview are all examples of data-gathering methods.
Data is not collected in a vacuum. Accurate data guides us in business decision-making, predicting trends, evaluating outcomes, and understanding the current state of the business.
Without accurate current data, we’re just making blind decisions. And missing opportunities.
What Kind of Data? Understanding the 4 Main Data Categories
As your company grows, your data collection needs will increase, and so will the volume of data you need to manage. But not all data is created equal.
We can classify data in several ways, the most common being by source and value. Whether data is primary or secondary depends on its source, while the qualitative and quantitative nature relies on the value type and data collection process.
Quantitative Data vs Qualitative Data
The first distinction to be made is between qualitative and quantitative data. Quantitative data are, simply put, numbers. Businesses collect quantitative data through surveys, polls, or third-party database acquisition for statistical purposes and to better predict customer behavior.
Qualitative data, on the contrary, is non-numerical in nature and helps us understand customer behavior, motivation, and value perception. It is generally made up of text, then synthesized into categories. The most common methods of qualitative data collection are focus groups, customer interviews, or product reviews.
Another way to think about qualitative vs quantitative data collection is by asking yourself if your goal is to generalize your findings or to understand behavior in detail. If your goal is to generalize (i.e., estimate the willingness to pay for graphic design software of freelance designers with at least five years of experience), then it’s best to collect quantitative data. If it's important that you understand each individual's experience in detail (i.e., how freelance designers feel about a specific feature), then it's better to collect qualitative data through user research methods.
Both approaches are critical for market research. Quantitative research is often used in market research because it allows marketers to explore consumers' purchasing habits, buying behavior patterns, and other behaviors related to product usage. Qualitative data may inform the brand voice, and user segmentation, or measure the user satisfaction with a specific feature or the product overall.
Primary vs. Secondary Data
Primary data consists of first-hand original information that has not been collected by anyone else. For example, if you wanted to know how many people eat at your restaurant on a Friday night, you would need to observe the customers directly and collect the numbers. Primary data collection is highly accurate (as long as the methods are rigorous), but can be time-consuming and expensive.
Secondary data is second-hand information (usually quantitative) that has already been collected by someone else. This type of data is often used by marketers because it saves them time and effort while still giving them valuable information. On the downside, it raises concerns over accuracy. A good example of secondary data would be census reports or government statistics that have already been compiled by experts in the field.
Data Is Power: Why Accurate Data is the Lifeblood of Any Business
Data collection isn't just about being able to look back and see how your business has performed in the past. It's also about looking forward and understanding what's coming next. If you’re making your business decisions based solely on intuition or external advice (or on a 280-character tweet), just know that your competition is probably doing thorough research every step of the way.
By being proactive in collecting your own data, you will be able to respond more quickly to changes in market conditions and customer needs. This also gives you greater control over your business, allowing you to adapt in real-time rather than reacting after the fact.
The more accurate your data, the more you can rely on it to help you make decisions that will help you reach your goals and objectives. This will also help advisors better support your growth and reassure investors that your growth forecast has a sound basis.
Data can also be used to identify shortcomings in your business. If you're trying to increase sales and find that they're not increasing as much as expected, you might look at the data you have collected over time and see if there are any patterns or trends that can help explain why this is happening. What are the friction points highlighted during customer interviews? Are there any patterns that can help explain revenue dips? If so, these trends can help guide your next steps.
By collecting data about your customers' preferences and behaviors, you'll be better equipped to predict their future behavior as well as respond with agility when changes occur in the market around them — whether those changes are due to new competitors entering the space or new technology becoming available that allows people to do things differently than before.
What Are the 5 Steps to Successful Data Collection?
Inaccurate, outdated, or inconsistent data can be as harmful as no data at all. Data quality should be one of your top priorities to avoid making decisions based on false assumptions or skewed information. That being said, the data collection process can be expensive, time-consuming, and error-prone if not planned carefully.
But don’t fret! Follow the following steps for an end-to-end data collection process you can trust to give you the insights you need.
Step 1: Identify the Question(s) You Want to Answer
The first step in any data collection project is to identify the questions that you want the new data to answer. This may seem obvious, but it’s easy to jump head-first into data collection without thinking about what you hope to achieve with it. It’s then no surprise to end up with thousands of unorganized data. And more unanswered questions.
Before you start, make sure you know what decision you are trying to influence with your data and what information will help you make that decision.
In other words, define the question to be answered. If you’re not sure what it is, ask yourself:
- Are you hoping to learn more about your existing customers?
- Are you working on your target user segmentation?Who are your main competitors and what differentiates you from them?
- What are the acquisition channels you are focusing on and how are they performing?
- What is your conversion rate on your different entry points?
- Are you aware of customer satisfaction and friction points?
These general questions will help you define the scope of your data gathering and analysis project and guide you to the right data you’re hoping to uncover.
Step 2: Plan Your Data Collection
Once you have identified the questions, it’s time to think about how you will get that data. You’ll need to decide which data you want to collect, how much data you need, where you can get it from, and the desired timeline to do so. The most common methods include surveys, interviews, focus groups, feedback forms, and content analysis.
For example, you might want to assess customer satisfaction with a new feature. You’ll need to decide if you want to send a feedback form with a 5-point Likert scale or if you want to interview the top paying customers and collect their feedback directly. The first solution will gather more responses, but give you less rich data. The second will be more time-consuming, cover fewer customers, but give you deeper insights.
Before jumping in, take stock of the resources you have available and determine which sample size will bring confidence in the findings. Getting crushed under mountains of data is easy, so it’s important to plan ahead.
Step 3: Choose Your Data Collection Approach
It’s time to choose how you will obtain the data. You may decide to opt for a qualitative or quantitative approach or a mixed-method approach. You should also decide if you’re collecting the data directly from the source, or if you’re relying on third-party sources. Of course, you can mix and match methods as best fits your research question.
For example, if you want to understand how customers feel about your product, it might be easier to ask them directly than to dig through archives of old customer service calls (which might be outdated). On the other hand, if you want to know about industry trends or market conditions, secondary data could be the right choice.
Step 4: Gather the Data
Now that you have a clear idea of what information you need and how to find it, it's time to start collecting the data. Make sure you planned ahead which data points you need from each source and where best to store them.
This is the most time-consuming and error-prone step of the data collection process. Pareto’s extended team can tackle your data collection and enrichment with unmatched speed and accuracy so you can focus on making the best data-driven decisions.
During this process, beware of data protection laws compliance. Before you start collecting data, have a look at the General Data Protection Regulation (GDPR). The GDPR has implications for any business that collects personal information from European Union residents, regardless of where the company is based.
Step 5: Analyse your Data
Congrats, you’ve reached the final step! Now that you have collected the data, you’ll need to make sense of it.
It’s easy to bypass this final step. Storing data without analysis is not particularly useful. This final step is crucial to ensure that you are able to draw meaningful conclusions from your research. It helps you identify patterns in data that can be used for predictive analytics, which can help you make better decisions based on historical trends and up-to-date information.
To make sense of the raw data, you may transform it into charts, graphs, tables, or other visual representations. The important thing is that you can draw conclusions from it and establish the next steps. For every data collection project, make sure you’re compiling the most relevant insights in a report and defining action items.
What Are the Common Pitfalls to Avoid With Data Collection?
Data is the lifeblood of today’s business world. However, the process of collecting data can be challenging for businesses, especially those without the right tools or processes in place. Here are some common pitfalls that you should avoid when collecting data:
- Low-quality or inaccurate data. Raw data is very error-prone, especially when adding human error to the equation. More than that, information changes constantly and 3% of data becomes obsolete every month.
- Inconsistent data. When collecting data from multiple sources, information may be inconsistent, making it difficult to determine its validity and sometimes rendering it unreliable to use.
- Data downtime. The inability to use the data while it’s being collected, updated, and processed is unavoidable. However, good planning will avoid excessive downtime and prevent it from becoming obsolete during the collection process.
- Data duplicates. Differences in spelling or formatting can create duplicates and tamper with the analytics. To prevent this, make sure you’ve established a style and spelling guide before starting the data collection process.
- Excessive data. Having insufficient data is a problem, but getting lost in too much data is the other side of the coin. This can lead to analysis paralysis where it's difficult to understand what data you should be looking at or what questions to ask about the data.
- Lost data. Most organizations utilize only a portion of their data, leaving the rest hidden, unorganized, and generally not usable. This happens for many reasons including lack of access to certain datasets, lack of flexibility to access different types of data sources or lack of strategic planning before collecting data.
You can prevent or mitigate most of these challenges by following the step-by-step approach laid out in this article. Taking those extra steps before jumping into collecting data into a spreadsheet will go a long way in getting the right data to build and maintain your edge.
Get Accurate Data at Your Fingertips With Pareto
Collecting accurate data is a complex, error-prone, and time-consuming process. With Pareto backing you up, you can get the data you need to grow with unmatched speed and accuracy.
Get a headstart in making strategic breakthroughs with the right data at your fingertips.