Developing a good data model requires an understanding of the organization’s processes, customers, and services/products, as well as the ability to organize those processes, etc., in a visual format. With the creation and collection of data continuously growing, extracting insights from the overabundance of data available needs well-designed data models. Their schemas are flying around, each of them having its own lifecycle.” It seems that companies are having hundreds, sometimes thousands, if not tens of thousands, of APIs and microservices, and these are handled using dozens of different technologies. “The ability to handle complexity and scale is another challenge. According to Pascal Desmarets, Founder and CEO of Hackolade: As a result, not all the links in this chain are altered with a single command, and not at the same time.
The different departments of an organization may be visualized as the links of a chain, with some links (departments) using different technologies. The more technologies being used, the more complex its data model. The goal of a data model is to show what kind of data is being used and stored, the relationships between these entities (objects that exist), and ways the data can be organized. It is used to display and communicate connections between objects/concepts and shows their relationships. Data Modeling describes the creation of a visual representation (a chart or diagram) of a data system, or parts of that system. A data model should show the relationships that exist between various customers, concepts, products, among many.
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