Data collection is the systematic approach to gathering and measuring information from a variety of sources to get a complete and accurate picture of an area of interest. Data collection enables a person or organization to answer relevant questions, evaluate outcomes and make predictions about future probabilities and trends.
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Accurate data collection is essential to maintaining the integrity of research, making informed business decisions and ensuring quality assurance. For example, in retail sales, data might be collected from mobile applications, website visits, loyalty programs and online surveys to learn more about customers. In a server consolidation project, data collection would include not just a physical inventory of all servers, but also an exact description of what is installed on each server -- the operating system, middleware and the application or database that the server supports.
Data collection methods
Surveys, interviews and focus groups are primary instruments for collecting information. Today, with help from 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 voluminous amounts of structured, semi-structured and unstructured data collected by organizations. But because it takes a lot of time and money to load big data into a traditional relational database for analysis, new approaches for collecting and analyzing data have emerged. To gather and then mine big data for information, raw data with extended metadata is aggregated in a data lake. From there, machine learning and artificial intelligence programs use complex algorithms to look for repeatable patterns.
Types of data
Generally, there are two types of data: quantitative data and qualitative data. Quantitative data is any data that is in numerical form -- e.g., statistics and percentages. Qualitative data is descriptive data -- e.g., color, smell, appearance and quality.
In addition to quantitative and qualitative data, some organizations might also make use of secondary data to help drive business decisions. Secondary data is typically quantitative in nature and has already been collected by another party for a different purpose. For example, a company might use U.S. Census data to make decisions about marketing campaigns. In media, a news team might use government health statistics or health studies to drive content strategy.
As technology evolves, so does data collection. Recent advancements 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 surrounding data collection heat up.
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How has your organization's data collection strategy evolved over the past 10 years?
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