Methods, Challenges and Milestones

What is Data Collection?

Data collection is the process of collecting data for use in business decision making, strategic planning, research, and other purposes. It is a crucial part of data analysis applications and research projects: effective data collection provides the information needed to answer questions, analyze business performance or other results, and predict trends. , actions and future scenarios.

In companies, data collection is done at several levels. Computer systems routinely collect data about customers, employees, sales, and other aspects of business operations as transactions are processed and data is entered. Companies also conduct surveys and track social media for customer feedback. Data scientists, other analysts, and business users then collect relevant data for analysis from internal systems, as well as external data sources if necessary. This last task is the first step in data preparation, which involves collecting data and preparing it for use in business intelligence (BI) and analytics applications.

For research in science, medicine, higher education, and other fields, data collection is often a more specialized process, in which researchers create and implement measures to collect specific sets of data. . In commercial and research contexts, however, the data collected must be accurate to ensure the validity of analysis results and research findings.

What are the different data collection methods?

Data can be collected from one or more sources as needed to provide the information sought. For example, to analyze sales and the effectiveness of its marketing campaigns, a retailer may collect customer data from transaction records, website visits, mobile applications, its loyalty program and an online survey.

The methods used to collect the data vary depending on the type of application. Some involve the use of technology, while others are manual procedures. Here are some common methods of data collection:

• automated data collection features integrated into business applications, websites and mobile applications;
• sensors that collect operational data on industrial equipment, vehicles and other machinery;
• collection of data from information service providers and other external data sources;
• follow social media, discussion forums, review sites, blogs and other online channels;
• surveys, questionnaires and forms, conducted online, in person or by telephone, email or regular mail;
• focus groups and individual interviews; and
• direct observation of participants in a research study.

What are common challenges in data collection?

Some of the challenges often encountered when collecting data include the following:

• Data quality issues. Raw data typically includes errors, inconsistencies, and other issues. Ideally, data collection measures are designed to avoid or minimize these issues. It’s not foolproof in most cases, though. Therefore, the collected data usually needs to be subjected to data profiling to identify issues and data cleansing to fix them.
• Find relevant data. With a wide range of systems to navigate, collecting data for analysis can be a daunting task for data scientists and other users in an organization. Using data curation techniques makes it easier to find and access data. For example, this may include creating a data catalog and searchable indexes.
• Decide what data to collect. This is a fundamental problem both for the initial collection of raw data and when users collect data for analytics applications. Collecting unnecessary data adds time, cost and complexity to the process. But omitting useful data can limit the commercial value of a data set and affect analysis results.
• Treat with bigdata. Big Data environments typically include a combination of structured, unstructured, and semi-structured data in large volumes. This makes the initial stages of data collection and processing more complex. Additionally, data scientists often need to filter raw data sets stored in a data lake for specific analytical applications.
• Low response rate and other research issues. In research studies, a lack of responses or willing participants raises questions about the validity of the data collected. Other research challenges include training people to collect data and creating sufficient quality assurance procedures to ensure data accuracy.

What are the key steps in the data collection process?

Well-designed data collection processes include the following steps:

1. Identify a business or research problem that needs to be solved and set goals for the project.
2. Gather the data required to answer the business question or provide the research information.
3. Identify datasets that can provide the desired information.
4. Establish a data collection plan, including the collection methods that will be used.
5. Collect available data and start working to prepare it for analysis.

Data Collection Considerations and Best Practices

Two main types of data can be collected: quantitative data and qualitative data. The first is numerical – for example, prices, amounts, statistics and percentages. Qualitative data is descriptive in nature — for example, color, smell, appearance, and opinion.

Organizations also use secondary data from external sources to help make business decisions. For example, manufacturers and retailers can use US Census data to plan their marketing strategies and campaigns. Companies can also use government health statistics and external health care studies to analyze and optimize their medical insurance plans.

The European Union’s General Data Protection Regulation (GDPR) and other privacy laws enacted in recent years enhance privacy and data security when collecting data, especially if it contains information personal about customers. An organization’s data governance program should include policies to ensure data collection practices comply with laws such as GDPR.

Other data collection best practices include the following:

• Make sure you collect the right data to meet business or research needs.
• Make sure the data is accurate, either when it is collected or as part of the data preparation process.
• Don’t waste time and resources collecting irrelevant data.