Data collection is the systematic approach of gathering and measuring information from various sources to get a complete and accurate picture of an area of interest. Collecting data allows a person or organization to answer relevant questions, evaluate outcomes, and make predictions about future probabilities and trends.
Collecting accurate data is critical to maintaining research integrity, making informed business decisions, and ensuring quality assurance. For example, in retail, data can be collected from mobile apps, website visits, loyalty programs, and online surveys to learn more about customers. In a server consolidation project, data collection would include not only a physical inventory of all servers, but also an exact description of what is installed on each server: the operating system, middleware, and application or the database supported by the server.
Data collection methods
Surveys, interviews and focus groups are the main instruments for collecting information. Today, with the help of web and analytics tools, organizations are also able to collect data from mobile devices, website traffic, server activity and other relevant sources, depending on the project.
Big data and data collection
Big data describes large amounts of structured, semi-structured and unstructured data collected by organizations. But since it takes a lot of time and money to load big data into a traditional relational database for analysis, new approaches to data collection and analysis have emerged. To collect and then extract big data in search of insights, raw data with extended metadata is aggregated into a data lake. From there, machine learning and artificial intelligence programs use complex algorithms to find repeatable patterns.
Generally, there are two types of data: quantitative data and qualitative data. Quantitative data is any data that is in numerical form – for example, statistics and percentages. Qualitative data is descriptive data — for example, color, smell, appearance, and quality.
In addition to quantitative and qualitative data, some organizations may also use secondary data to help make business decisions. Secondary data is usually quantitative in nature and has already been collected by another party for a different purpose. For example, a business may use US Census data to make decisions about marketing campaigns. In media, a press team can use government health statistics or health studies to drive content strategy.
As technology evolves, so does data collection. Recent advances in mobile technology and the Internet of Things are forcing organizations to think about how to collect, analyze and monetize new data. At the same time, privacy and security issues related to data collection are intensifying.