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Detailed Information About Data Analytics and Data Collection

Updated: May 6


Data collection is the process of gathering information from a specific source to obtain an answer to a specific question. Data collection is the first and most important step in statistical research.


Statistical study here means a study conducted by an institution in which the researcher collects data on figures. In simple terms, statistical research is statistical collection, combination, analysis, interpretation, etc. It is the investigation of facts using methods. Therefore, the main purpose of data collection is to gather evidence to arrive at a better and more reasonable answer to the problem. To opt data analytics course you can read this article for detailed information.


What is Data Analysis?


Analytics discovers patterns and patterns extracted from your data. Information is absolutely useless without analysis. Analytics is how you understand your data and identify actionable trends. A lot of value is hidden in this big data, but applications and companies cannot unlock this value without analysis.


In the mobile app, your data may tell you that you sent 14,000 push messages last month. This data doesn't mean much on its own, but an analytics tool might dig deeper and reveal that your app is sending 3.7 messages per user with a 20% open rate. This transforms your data and gives you an advantage in mobile marketing.


Data Analytics and Data Collection
Data Analytics and Data Collection


Important Terms related to Data Collection:


1. Researcher: A researcher is a person who conducts statistical research.

2. Enumerators: To collect data for statistical research, the researcher needs the help of some people. These people are called census takers.


Data is the need of the hour, and their collection and analysis is the basis of successful business and research today. Napoleon Bonaparte talked about the importance of information 200 years ago with the words '90% of war is information' .Therefore, collecting and analyzing data will be the key to success in many fields.


What are the different data collection methods?


Information required to provide the information sought may be gathered from one or more sources. For example, to analyze products and marketing campaigns, the marketer may collect customer data about business activities, website visits, mobile applications, loyalty programs and online surveys. 


Methods used to collect data vary depending on the type of application. Some involve the use of technology, while others are manual processes. Below are some common types of data collection: data collection processes are built into business processes, websites, and mobile applications. The  sensor collects performance data from industrial equipment, vehicles and other machinery.


Data information from information services and other external sources.

to monitor social media, forums, review sites, blogs and other online networks.

surveys, polls and surveys, online, in person or by phone, email or regular mail.

focus group and one-on-one interviews and direct observation of research participants.


What are the most common challenges in data collection?


Some of the most common challenges encountered during data collection include:


  • Data quality issues. Bad news often includes errors, inconsistencies, and other problems. Ideally, data collection strategies are designed to prevent or reduce such problems. But in most cases, this isn't that unreasonable. As a result, collected data often needs to be processed through statistical data analysis to identify problems and improve the data to solve them.


  • Search for relevant information. Due to the large scale of the management system, collecting data for analysis can be a difficult task for data scientists and other users in the organization. The use of information processing technology makes it easier to find and access information. This may include, for example, creating a catalog of information and searchable information.


  • Selection of information to collect. This is a fundamental problem in both front-end data collection and data collection for program analysis. Collecting unnecessary data adds time, cost and complexity to the process. However, omitting useful information can limit the information generated in the business and affect analysis.



  • Working with big data. A large environment often contains a combination of large volumes of structured, unstructured, and semi-structured data. This makes initial data collection and classification difficult. Data scientists also need to filter the underlying data stored in the data lake to use in specific analyses.


  • Short answer and other research questions. The lack of answers or willing participants in research studies raises questions about the accuracy of the data collected. Other research challenges include training people to collect data and establishing adequate quality assurance mechanisms to ensure data accuracy.


How is data collected?


A good data collection process includes the following steps:

Identify the business or research problem to be solved and set goals for the project.

Gather information necessary to respond to a business request or provide research information. Identify information sources that can provide the information you are looking for. Develop a data collection plan, including the collection methods to be used.

Collect available data and begin preparing the analysis.


Data Collection:

Data collection is the process of collecting and measuring data on target variables through a well-designed system to evaluate results by answering relevant questions.


Data Analysis:

Data analysis is a process that involves structured data that must be analyzed for interpretation to identify relevant information, draw conclusions, and assist in making research decisions.


Difference between data collection and data analysis:


Data collection is the collection of data from different sources, while data analysis is the process of obtaining useful information from these data.


The difference between them lies in their respective activities, apart from their basic functions. Specialized data analysis methods and tools are required to process data collected in different places and ways and extract insights.


Some of the different types of data collection:

• Collecting new data from the internet and other sources

• Using collected and stored data

• Using third party data

• Purchasing data


Data collection methods are based on:

• The research problem being investigated

• Research design

• Data collected regarding variables









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