Explain Data Collection in Machine Learning?

Data collection is the process of gathering and measuring information from countless different sources. In order to use the data we collect to develop practical artificial intelligence (AI) and machine learning solutions, it must be collected and stored in a way that makes sense for the business problem at hand.

Why is Data Collection Important?

  • Collecting data allows you to capture a record of past events so that we can use data analysis to find recurring patterns. From those patterns, you build predictive models using machine learning algorithms that look for trends and predict future changes.
  • Predictive models are only as good as the data from which they are built, so good data collection practices are crucial to developing high-performing models.
  • The data need to be error-free (garbage in, garbage out) and contain relevant information for the task at hand. For example, a loan default model would not benefit from tiger population sizes but could benefit from gas prices over time.

What Are the Different Methods of Data Collection?

Data collection breaks down into two methods. As a side note, many terms, such as techniques, methods, and types, are interchangeable and depend on who uses them. One source may call data collection techniques “methods,” for instance. But whatever labels we use, the general concepts and breakdowns apply across the board whether we’re talking about marketing analysis or a scientific research project.

The two methods are:

  • Primary: As the name implies, this is original, first-hand data collected by the data researchers. This process is the initial information gathering step, performed before anyone carries out any further or related research. Primary data results are highly accurate provided the researcher collects the information. However, there’s a downside, as first-hand research is potentially time-consuming and expensive.

  • Secondary: Secondary data is second-hand data collected by other parties and already having undergone statistical analysis. This data is either information that the researcher has tasked other people to collect or information the researcher has looked up. Simply put, it’s second-hand information. Although it’s easier and cheaper to obtain than primary information, secondary information raises concerns regarding accuracy and authenticity. Quantitative data makes up a majority of secondary data.