2 Oct 2022| ONPASSIVE
Types Of Primary And Secondary Data Sources
Aside from its use in predictive analytics, data has become an essential input driving growth, allowing businesses to extract valuable insights and improve decision-making. One cannot think about Artificial Intelligence without data, as data is a crucial component of AI. Large amounts of data must be fed into an AI algorithm to make predictions.
As a general concept, data refers to the fact that some existing knowledge of the information is represented or coded in some form suitable for practical usage or processing. This article will discuss the various types of data and data sources that businesses can use to implement Artificial Intelligence and improve decision-making.
There must be a process of gathering and sorting data to analyze, present, and interpret information from the data. Various data collection methods fall into two categories: primary and secondary data source.
The term primary data refers to data created by a researcher. In contrast, secondary data is data that agencies and organizations have already collected to conduct an analysis. Surveys, observations, questionnaires, experiments, personal interviews, and other primary and secondary data source are possible. Data from ERP (Enterprise Resource Planning) and CRM (Customer Relationship Management) systems can also be used as a primary source of data.
On the other hand, secondary data sources can include government publications, staging websites, publications from independent research labs, journal articles, and so on. In the process of data wrangling, the transformed “raw” data set into another format can also be viewed as a secondary data source. Secondary data can be helpful in data enrichment when the primary source data lacks information. It can improve the precision of the analysis by including more attributes and variables in the sampling.
A set of variables, either qualitative or quantitative, can define data.
Qualitative data can provide insights and understanding about a specific problem.
Quantitative data, as the name implies, deals with quantity or numbers. Categories or so-called classes can be used to categorize this numerical data.
Although both types of data can be considered separate entities that provide different outcomes and information about a sample, it is essential to understand that both types are frequently required to perform quality analysis. We may attempt to solve the wrong problem or the right problem incorrectly if we do not know why we see a particular pattern in behavioural data.
A real-world example would be gathering qualitative data about customer preferences and quantitative data about the number and age of customers to analyze customer satisfaction and discover a pattern or correlation of changing preferences with different customer age groups.
Data can be captured in various shapes, some of which may be easier to extract than others. Data in multiple forms necessitate different storage solutions and, as a result, should be approached in various ways. We distinguish three types of data at Kantify: structured, unstructured, and semi-structured data.
Structured Query Language frequently manages structured data, or SQL – a programming language designed for managing and querying data in relational database management systems. Structured data is tabular data that contains well-defined columns and rows. The main advantage of this type of data is that it is simple to store, enter, query, modify, and analyze.
Unstructured data is the most basic form of data. It can take the form of any type or file, including pictures and graphic images, webpages, PDF files, videos, emails, word processing documents, and so on. This information is frequently stored in file repositories. It can be challenging to extract useful information from this type of data. A text, for example, can be analyzed by extracting the topics it discusses and whether the text is positive or negative about them.
As the name implies, semi-structured data is a hybrid of structured and unstructured data. A semi-structured data set may have a consistent, defined format, but the structure may be loose. The system does not have to be tabular, and parts of the data may be missing or of different types. Photos of other graphics, for example, can be tagged with keywords, making it simple to organize and locate drawings.
Data From Historical And Real-Time
Historical datasets can help answer the kinds of questions decision-makers want to compare to real-time data. Historical data sources may be best suited for developing or refining predictive or prescriptive models and providing insights that can improve long-term and strategic decision-making. The basic definition of real-time data is data transmitted to the end-user as soon as it is collected. Real-time data can be instrumental in traffic GPS systems, benchmarking various analytics projects, and keeping people informed via instant data delivery.
Both types of data sources should be considered equally in predictive analytics because they can help predict and identify future trends.
Internal data is information gathered within an organization and can include personnel, operations, finance, maintenance, procurement, and many other areas. Internal data can provide information on employee turnover, sales success, profit margins, an organization’s structure and dynamics, and so on.
You may be wondering if internal data is the same as primary data and external data is the same as secondary data. External data is information gathered from outside sources, such as customers, staging websites, agencies, etc. External data collected from social media, for example, can provide insights into customer behaviour, preferences, and motivations. This is similar but not identical.
Internal and external data sources are classified primarily based on where the data comes from – whether it was collected within your organization or from a source outside of your organization. The term “primary and secondary data source” refers to the purpose and time frame for which the data was collected whether by the researcher for a specific project or from another source, even within the same organization.
We use Artificial Intelligence and Machine Learning to assist businesses in making smarter, data-driven decisions. These various types of data sets can be found within an organization. Still, they can also be found in external data sources such as the internet. If you’d like to learn more about how your company can use data to boost growth, please contact the ONPASSIVE team for more info.
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