Without data to inform critical decision-making processes, innovation, growth and progress are difficult to obtain. Developing an understanding of data collection is a bit like discovering a map that can lead you to hidden treasure.
So, let’s explore what data collection is, what it involves, and some of the challenges that may need to be overcome during the process.
What is data collection?
Data collection is the process of gathering data with a view to providing solutions, predicting patterns, and addressing queries. Data collection is fundamental to analysis, research and decision-making processes in a variety of fields including commerce, social sciences and healthcare.
What does data collection involve?
An experienced data collection company, such as shepper.com/, will begin the data collection process by seeking to understand the objectives that should be used to inform the direction of the project. Once the goals of the research project have been established, the right data collection methods and tools can be utilised to obtain high-quality data.
Data is then collected in a methodical way, using high ethical standards, before being validated for its authenticity and accuracy. This Harvard Business School article sets out the principles of data ethics in an easy to understand way, including transparency, privacy and intention.
Data can then be analysed to reveal information, correlations and trends, before it is interpreted and shared with stakeholders.
What are some common data collection challenges?
There are a number of common challenges that can be faced by data collection experts. It is particularly important to understand how to properly evaluate the quality of the data that has been collected, as raw data often presents discrepancies and errors.
Another challenge that can present itself is difficulty in locating data that is pertinent to the objectives driving the project, but experienced data scientists understand how to navigate different systems to collect the right data.
Understanding how to handle big data properly is another challenge and data scientists must often look through large amounts of raw data in order to locate the most relevant data for analysis.