Structured data includes data stored in tables and databases and is defined as data that has been organized in a format that computers and machines can understand. It's the type of data most commonly used by artificial intelligence (AI) applications. The term primary data refers to data originated by the researcher himself, while secondary data are existing data collected by agencies and organizations for the purpose of carrying out an analysis. Primary data sources can include surveys, observations, questionnaires, experiments, personal interviews, and more.
Data from ERP (enterprise resource planning) and CRM (customer relationship management) systems can also be used as the main source of data. In contrast, secondary data sources can be government publications, storage websites, publications from independent research laboratories, journal articles, etc. The raw data set transformed into another format, in the process of data processing, can also be considered a secondary data source. Secondary data can be a key concept in terms of data enrichment when the data from the primary source is not robust enough with the information, and can improve the accuracy of the analysis by adding more attributes and variables to the sampling.
Figure 1 shows a sample of tabular data with characteristics of different types of data. The characteristics and data types of each of them are as follows. You can't think about artificial intelligence without thinking about data, because data is an essential part of AI. The ability of AI to work expertly with data analysis is the main reason why artificial intelligence and big data now seem inseparable.
In predictive analysis, equal attention must be paid to both types of data sources, as both can help predict and identify future trends. It is also necessary to convert the data types of some variables in order to choose the appropriate visual encodings in data visualization and storytelling. Having data in different forms requires different storage solutions and must therefore be approached in different ways. We help companies make smarter, more data-driven decisions with the help of artificial intelligence and machine learning.
Real-time data can be very valuable in aspects such as GPS traffic systems, for comparing different types of analysis projects and for keeping people informed through instant data delivery. Demand for data professionals and people with a master's degree in business analytics or data analysis is expected to increase as companies expand their big data and artificial intelligence capabilities in the coming years. We explain the different types of data and data sources that companies can leverage to implement artificial intelligence and improve the decision-making process. Basically, scientists machine thousands of laboratory tests or images of diseases, and artificial intelligence medical devices analyze, compare tests with each other and capture trends that humans could never notice because they can't contemplate so much data simultaneously.
