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What is data collection?

Data collection is understood as the process of collecting, measuring and analysing information deriving from countless different sources. The data collected are used to develop solutions for artificial intelligence (AI) and machine learning. They must be gathered and saved in a way that makes sense for the business issue that we want to address. An effective data collection supplies the information needed to answer questions, to analyse business performance or other results, and to forecast future trends, actions and scenarios.

 

What methods of data collection are there?

The methods employed to gather data vary depending on the type of application involved. Some entail the use of technology while others are manual procedures. The following are some common methods of data collection:

  • automatic operations of data collection that are integrated with business applications, websites and mobile apps;
  • sensors that gather operational data from industrial equipment, vehicles and other machinery;
  • data collection from information service providers and other external sources of data;
  • monitoring of social media, discussion forums, review sites, blogs and other online channels:
  • surveys, questionnaires and forms completed online, in person or by phone, email or regular post;
  • focus groups and one-on-one interviews;
  • direct observation of participants in a research study.

 

The data are key

Machine learning is based on the use of algorithms. These algorithms imitate the way in which human beings learn, gradually improving their accuracy.

Similar to the human brain, an algorithm needs pieces of information to gain knowledge and understanding.

These pieces of information for the algorithm really are the data.  Machine learning (ML) is based on input data to comprehend entities, domains and the connections between them for the purpose of making predictions or decisions without being explicitly programmed to do so.

It turns out that the most critical factor in ML is not the learning process of the machine but the preparation of the data necessary to train the machine. The factor that determines the successful outcome of a machine learning project is just that: the quality of the data collected. Data collection requires a great deal of time and resources, but it’s fundamental.

 

Why is the quality of the data so important?

The main purpose of the data collection is to gather information in a measured and systematic way to ensure accuracy and facilitate data analysis. Since all collected data are intended to provide content for analysis of the data, the information gathered must be of the highest quality to have any value.

Regardless of the way data are collected, it’s essential to maintain the neutrality, credibility, quality and authenticity of the data.  If these requirements are not guaranteed, then we can run into a series of problems and negative results, including the following:

  • Data cannot be validated.
  • Decisions based on the data can be compromised.
  • Further research can be distorted.
  • Goals are not reached.
  • Questions do not receive an appropriate response.
  • Precious resources are wasted.

 

Collaborating with a data provider enables you to collect a large amount of training data that is varied and of high quality. The data provider promises to collect the right data to meet business or research needs so that you can get your desired results, thereby saving time and resources.