Processes & Methods Involved in Online Data Collection

Data Online Collection

What is Data Collection?

The process that you follow for gathering any piece of information for any purpose is called data collection. These purposes can be feasible decision-making, strategy-making or planning, research, etc.

Above all, the most crucial purpose is data analytics. The collected information allows you to get deep with it and draw some valuable breakthroughs. This is why data collection and analysis are closely related. Through analysis, you may discover valid reasons and answers to your queries. These queries can be about your business upsides, downsides, revenues, future trends, sales predictions, actions, or whatever your goal suggests. 

Processes Involved in Online Data Collection

As its definition defines, online data collection is all about drawing information for a purpose. This process involves multiple levels. These levels help in automation that makes it easy to process and analyse data.

Here are the key steps that complete this online data collection process:

1. Planning

This is a vital step because it tells where to start from, what resources you need, how to collect, costing analysis, and secure storage of that data. 

2. Data Identification

This stage defines how to identify the information you want to collect. Decide what information you require and the websites where it can be available. These can be, for example, customers’ intent, a CRM database, customers’ web journey, inquiries, etc.

Simply put, you have four types of data to identify. These are the following:

  • Nominal data can be a label of variables, but not in any order or quantity. For example, marital status, gender, nationality, any other physical attributes, etc.
  • Ordinal data represent a sequential set of information, which can be a mix of qualitative and quantitative datasets. For instance, customer feedback, education level, letters, grades, etc.
  • Discrete data refer to values that can have integers and whole numbers. For example, A2 (cell address), phone number, days in a week or months, etc.
  • Continuous data are fractional numbers, like a number representing the version of an android phone, Wi-Fi frequency, market share price, speed, etc

3. Determining Resources

Now that you have discovered your requirement, find out where you can access that target resources. Let’s say, you want to understand customer intent. The customers’ comments, carts, inquiries, and grudges can tell what they expect. To find an accurate resource, you should deeply consider and assess your goal or motive.

4. Methods Selection

Methods can make data collection easier. So, you should be considerate about the dos and don’ts, at first. You would have multiple methods to choose from, such as transactional data, website visits, mobile applications, contact details, online surveys, etc.

These all methods can never prove the best fit in all cases. Let’s say, you want to collect directors’ profiles on LinkedIn. It can be done manually and automatically. You have to decide which method is feasible and affordable.

Let’s get through some commonly used methods for online data collection.

  • Scripting by using Python or any programming language to draw bespoke data automatically using functions and validations for extracting data from business applications, websites, and mobile apps
  • Sensors data is collected from the Internet of Things sources like industrial equipment, vehicles, etc.
  • Third-party data vendors selling data online
  • Data scraping tools and applications that can extract desirable details from social media, discussion forums, review websites, blogs, and online resources
  • Online surveys, questionnaires, forms, emails, etc.

5. Storage Defining

For this purpose, you should have a server or cloud. The storage completely depends on the size and frequency of record collection. You may have Google drives to share small sets of information. For bulky data, the cloud and servers are the best. These platforms help in online analysis and accessing records remotely anytime.

Following this process is not like a walkover. It involves various potholes. These can be associated with quality, methods, applications, etc.

Challenges in Data Collection

Let’s find out the most common challenges.

  • Quality Issues

Extracting or collecting any piece of information typically includes a ton of errors, inconsistencies, and incomplete records. Ideally, this collection requires a design or draft to measure quality. It can help in minimizing challenges in the analysis. Data profiling can help in identifying the problem to fix through data cleansing methods.

  • Irrelevancy

It is not easy to deal with a range of applications, processes, and systems to navigate and collect information. Mostly, data scientists together with researchers can make it easier. The data curation technique really proves helpful in finding and accessing relevant records. It can track and search indexed data to extract in no time. 

  • Confusion over What to Collection

It’s the basic problem that data collectors face. The collection of redundant information burdens with extra time & money. Moreover, the process becomes complex. On the flip side, leaving relevant and useful information can be a big loss and adversely impact analytics results. The feasibility would not be there in decisions.

  • Struggling with Big Data

A big data environment is formed through a combination of structured, unstructured, and semi-structured data. They are available in massive volumes, which makes it hard to filter the useful data and smoothly go through their processing stages. Besides, filtering becomes a real struggle when data scientists have to often filter sets of raw data in various data lakes.

  • Low or Slow Responses and Research Problems

If you are likely to collect responses from the online surveys or polls, it may take hours and sometimes, this time does not end. This happens because most of the participants have a ton of questions about the validity and use of the data. Moreover, these extraction processes require proper training to collect and set up quality data.

Compliance is the Best Practice

The European Union’s General Data Protection Regulation (GDPR) and other privacy laws and regulations have been set up recently. These regulations control any misuse of data or negatively impact the privacy and security of data subjects. These are some big considerations that come along with gathering data, specifically the personally identifiable information or PII. To come across this problem, an organization should have a stringent data governance policy and program. Their privacy policies should ensure the best practices to use that fully comply with laws like HIPAA and GDPR.

Ensure that your collection has the right datasets, which are really helpful in meeting business or research purposes.  Also, take care of their accuracy or benchmark quality. You can easily define it at the time of collecting records or preparing for extraction.


Online data collection involves a range of processes and methods, which are helpful in accurately analyzing datasets. The process includes planning, data identification, determining resources, deciding methods, and storing securely. Basically, this process requires manual extraction, research, and automatic collection.

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