Data Collection: Definition, Steps, Methods, & Examples

Explore robust data collection methods, tackle challenges, and unlock data-driven strategies with our comprehensive guide. Elevate your data quality with Polymer today!

February 14, 2024
Written By
Rand Owens
Founding team member at Motive (Formerly KeepTruckin) and passionate about all things Marketing, RevOps, and Go-To-Market. VP of Marketing @ Polymer Search.

Data Collection: Definition, Steps, Methods, & Examples

Data collection is one of the earlier phases of data analysis.

Its importance cannot be understated.

After all, an unreliable data collection process yields low-quality (or inaccurate) data, which jeopardizes the entire data analysis process.

While data collection seems straightforward on paper, it requires a strategic approach to be successful. Apart from gathering data, you must also contend with challenges like ensuring data quality, integrating multiple data sources, and data governance. 

Here's everything you need to know to conduct an effective and efficient data collection process. 

Table of Contents

  • What is data collection?
  • Purpose of data collection
  • Sources and challenges of data collection
  • The data collection process
  • How to create a data collection plan
  • Data collection FAQs
  • Improve your data collection quality with Polymer

What is data collection?

You need a firm grasp of the data collection process to master the fundamentals of data analysis.

From a 30,000 ft. view, data collection is about gathering raw data to conduct further analysis.

Data collection methods have two categories: quantitative and qualitative.

Quantitative data collection gathers and measures numerical units. Statistical analysis is then used to convert data into actionable insights. 

Some examples of quantitative data are: 

  • Website monthly traffic volume
  • Total ad spend
  • Gross monthly revenue 

Qualitative data, however, organizes and analyzes non-numerical, descriptive information. Conclusions are drawn by consolidating related qualitative data into meaningful groups, which can be analyzed, scored, and visualized. 

Some examples of qualitative data are:

  • User sentiment
  • Case studies
  • Survey results

Purpose of data collection

The purpose of data collection boils down to the following: 

1. Optimize strategies

Businesses collect and use past data to make informed decisions for new or existing strategies. 

For example, website traffic data is often used to pinpoint your top-performing content. This will help fine-tune your content strategy to align with your target audience's needs and preferences. 

In case first-party historical data is unavailable for new projects, businesses rely on other data collection methods. Your options include running polls, sourcing data from industry reports, leveraging AI tools, and so on. 

2. Answer research questions 

Data collection allows businesses to uncover facts, test hypotheses, and answer research questions to enhance their decision-making process. Businesses may also publish research results to establish brand authority, raise awareness, earn the audience's trust, or accomplish any marketing objective.  

Industry-leading brands, for example, compile and publish industry reports as "lead magnets." These are offered in exchange for information, such as the user's name and email address—converting them into leads in the process. 

3. Understand customers better

Acquiring customer data helps businesses improve several areas of their marketing, product development, customer service, and more. 

A typical approach is to host surveys to understand customer satisfaction and unveil pain points. This allows businesses to develop new product features or updates that target their customer's needs.

Customer data come in qualitative and quantitative forms. Average time on site, for instance, is quantitative, whereas customer testimonials are qualitative. 

4. Manage risks

Data can be used to predict possible outcomes, gauge their impact, and identify necessary adjustments—making data collection valuable to risk management. To create a reliable risk management process, it's crucial to have equally reliable data collection methods. 

Bear in mind that risk data collection can be simple or complex, depending on the nature of your business. 

Internal operations like manufacturing and packaging, for example, require a multi-step approach. This includes identifying hazards, validating risk management strategies, creating documentation, and more. 

Risk analytics may utilize AI and machine learning to refine big data into readable insights. 

Sources and challenges of data collection

The data collection sources you need will vary depending on your project, goals, and assets. These can be classified as either primary or secondary sources. 

A primary data source generates raw, untouched data that you collected directly. Primary data always pertains to newly acquired information captured for an intended purpose. 

On the flip side, secondary data sources produce data obtained by another party. This includes internal data collected by third-party software, such as analytics tools and social media websites. 

To help you understand primary and secondary data sources, below are common data collection sources used by businesses:

Primary data sources

  • Internal company records
  • Survey results (conducted by your business)
  • Focus groups
  • In-store tests
  • Consumer feedback
  • Mail-in questionnaires
  • Direct personal investigations

Secondary data sources

  • Third-party industry reports
  • Third-party case studies
  • Split testing tools
  • Statistics websites
  • Analytics tools
  • Ad network reports
  • Customer Intelligence (CI) services

What are the challenges in data collection?

As we delve deeper into data collection processes, let's first look at the common challenges businesses face in data collection:

  • Setting project goals. Data is meaningless unless you know what it's for. As such, setting goals and deciding the data you need before rolling out data collection plans is important. 
  • Ensuring data quality. No matter how polished your data collection methods are, it's impossible to completely eliminate the chance of errors. While small inaccuracies in raw data are negligible, substantial errors warrant additional data profiling and cleansing are required.
  • Filtering the data. When dealing with large amounts of data, it can be difficult to sift out relevant, useful information, particularly with qualitative data sets. The good news is, modern Business Intelligence (BI) and analytics platforms have built-in filter and keyword search systems. 
  • Working around data privacy. Before gathering customer or traffic data, be sure to implement data privacy compliance measures, like auditing stored data and publishing clear privacy policies. Upgrading to compliant analytics software like Google Analytics 4 is also an important step moving forward. 
  • Integrating data from multiple sources. Building a unified data architecture streamlines your data management and decision-making processes, but it's not always easy to combine information from multiple data sources. Integrated dashboard reporting and BI tools can change this by providing your organization with a "single source of truth." 
  • Governing data. By collecting data, your organization is responsible for implementing a data governance framework. This is a collection of policies and procedures that ensure the quality, storage, security, and utilization of all data. 

The data collection process

The structure of a data collection process depends on a handful of factors—from the company's goals to the availability of data sources. 

While each data collection workflow may appear unique, there are five key steps that every business must take: 

1. Define your research goals

Establish well-defined goals for your data collection.  

Identifying your desired results is integral to the entire data collection process. It helps you select your data sources, specify Key Performance Indicators (KPIs) to track, set a target audience, build a data collection plan, and more. 

To set a research goal, elaborate the data analysis questions, hypotheses, or decisions you need to empower with data. You may also address recent problems or issues, which underline important metrics to measure and the ideal data sources.

Some examples are:

  • Question: "What percentage of users prefer the latest UI changes over previous versions?"
  • Hypothesis: "Posting Facebook content at 7 a.m. daily will result in a 10% increase in user engagement." 
  • Decisions: "Should we allow employees to work remotely?"
  • Problem: "What factors actually contributed to the poor sales performance last quarter?"

2. Specify your data sources

Identify the data sources that will generate the insights you need.  

Start by listing the metrics and KPIs that are vital to your research goals. Doing so helps pinpoint the exact tools you need to monitor, record, and analyze the data you need. 

Let's say your goal is to prove the effects of remote working on your sales team. Some of the key metrics you should focus on are: 

  • Win rate
  • Average lead response time
  • Percentage of time on selling activities
  • Sales per rep
  • Average conversion time

Naturally, these metrics are tracked via your sales management or CRM platform, making it one of your secondary data sources. Email service software, sales funnel tools, and time trackers can also be used to provide data your existing CRM solution doesn't measure. 

3. Build a data collection plan

With your research goals and data sources figured out, it's time to work on your data collection plan. 

First, build a list of data collection tools that track, compile, and report your data. 

Your data sources should lead directly to the data collection tools you need. 

For example, website optimization research requires traffic analytics, on-page SEO, and loading speed evaluation tools. Social media research goals, on the other hand, require in-app insights or reports from a social media management platform. 

It's also important to specify the scope of your data collection. This includes how long you'll collect data, the sample population, and other specific considerations that control or aid your data optimization and validation processes. 

4. Execute your data collection procedures

The next step is to get your data collection plan underway. 

Your data sources play a huge role during this phase.

For example, if your data source is a CRM platform, configure its built-in insights and reporting tools to produce the data you need. If you need to conduct surveys, consider investing in tools like SurveyMonkey, Google Forms, or Voicent.

5. Manage your data

Once your data collection strategy runs its course, your next priorities are data cleansing and presenting data insights.

Data cleansing is a delicate process that involves several steps to ensure quality and accuracy, such as:

  • Removing duplicate data
  • Detecting and correcting errors
  • Addressing incomplete data
  • Recognizing outliers
  • Standardizing data

Cleansed data must then be compiled, organized, and presented. This is a critical phase in a data collection plan where you reap the rewards in the form of actionable insights. 

How to create a data collection plan?

To create a data collection plan, produce a fleshed-out document with all the specific details.

Brainstorm your research goals, list down your data sources, and prepare the data collection tools. Presenting data insights is another key area you must elaborate on. 

If you intend to use secondary data sources, consider platforms with automation tools to save time and effort on repetitive tasks. This includes data entry, audience interactions, and visualizations.

BI tools like Polymer, for instance, connect with popular data sources and use automation to populate readable data dashboards. 

Polymer creates a single source of truth to help your business make data-driven decisions more efficiently. 

To get started, choose from one of the supported data sources, like Facebook Ads, Google Sheets, Jira, Zendesk, and Shopify. 

You can organize and filter your data under the "Data" tab on your data source page. Use the category tags on the left to quickly sieve out relevant data, or click 'Add Global Filters' to automatically highlight data under specific categories. 

To turn data into insights, switch to the 'Insights' tab to launch Polymer's block editor. 

This is a drag-and-drop tool that generates interactive charts and data elements, including: 

  • Pivot tables
  • Scatter plots
  • Line charts
  • Pie charts
  • Scorecards
  • Outlier maps
  • Dependency wheels

Simply click 'New Block' for the complete list of data visualizations you can create. 

Polymer also offers AI-powered recommendations to instantly refine your data into actionable information.

Look for the "Get to know your data" section for a list of research questions that your data set can answer.

Your choices depend on the information types and categories from your data source. For example, if you imported sales data from your eCommerce platform, some of the recommendations you'll find may include:

Click on any question to generate a preview of the data visualization. 

Polymer should show you exactly what the AI-driven data chart or table looks like. If you're happy with the results, click 'Add to Board' to save it in your dashboard. 

Use Polymer to easily combine multiple data visualizations into a single dashboard. For ideas, head to the 'Boards' tab to view AI-generated boards and kickstart your data management. 

Feel free to customize every section of the dashboard to suit your needs. 

Data collection tips for businesses

Use the checklist below to ensure an effective and reliable data collection process for your business:

1. Focus on KPIs

Rather than casting a wide net, build your data collection plan around specific KPIs that matter to your goals. Aiming to obtain as much data as possible without aligning them with relevant research questions is detrimental to data collection planning. 

2. Combine multiple data sources

Consider combining relevant data from multiple sources to see a more complete picture, identify correlations, and cross-reference facts. Remember to focus on KPIs and all the metrics necessary for calculating them. 

3. Test your data collection strategies

Before launching full-scope data collection, do a test run of your strategies to ensure they produce relevant insights of your desired quality. If the trial run fails, use data validation techniques and refer to data analysis examples to find potential improvements and adjustments.

4. Plan for long-term data storage

Big data requires massive amounts of storage and a detailed data management plan. Implement a data retention policy that outlines where data will be stored, how long they're kept, and what backups will be used.

5. Incorporate a review process

Don't stop assessing your data collection methods to identify inefficiencies. Look for potential improvements in your data sources, data gathering methods, data management tools, and other important areas to continuously refine data quality. 

6. Standardize your data validation

Use a consistent data validation process to avoid errors, incomplete data, and confusion among your data stakeholders. Create a standardized, documented process that includes details on your data validation rules, tools, and reviews. 

7. Build a single source of truth

Rather than analyzing and processing data from dozens of platforms, invest in data management solutions to create a centralized data hub. Polymer, for instance, pulls data from third-party integrations and lets you design comprehensive dashboards for data analysis.  

Data collection FAQs

What is the main purpose of data collection?

Businesses conduct data collection primarily to empower their decision-making with proven insights. This can target a variety of business processes, including marketing, customer service, product development, and budgeting. 

What is data collection method in research?

Data collection may focus on primary data sources like one-on-one interviews, surveys, and internal company records—or secondary sources like third-party case studies and analytics reports. Further analysis is done to refine data into meaningful insights, which answer a research question, optimize strategies, understand customers, or improve risk management. 

What are the factors to consider when collecting data?

Businesses need to consider several challenges when implementing data collection methods. This includes data privacy, integrations, security, quality, and governance.

What are 5 methods of data collection?

5 examples of data collection for businesses:

  • Surveys
  • Focus groups 
  • Analytics tools
  • Split testing
  • Direct observation

Improve your data collection quality with Polymer

When it comes to business decision-making, it pays to have a unified data repository. 

Polymer will provide you with a master hub for all your data management needs. It supports traditional data exports and integrations with popular services, like Google Sheets, HubSpot, and Salesforce. 

More importantly, Polymer is equipped with the best-in-class data visualization tools. Combine interactive elements, utilize AI-driven insights, and share your dashboard with colleagues—all in one place. 

Get started today by creating a free Poylmer account here

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