Some of the mistakes encountered in data modelling are:

• Building massive data MODELS: As good practices, a data model is recommended to have <=200 tables. This is because large data models are more likely to have design faults.
• Lack of purpose: When the purpose of the business solution is not clear, then the data model generated would be incorrect as there is no means of validating the correctness of the model against the ORGANIZATION objectives.
• Unnecessary de-normalization: Denormalization should not be done unless we have a solid business reason as it contributes to data redundancy which might increase the cost of maintenance.
• Unnecessary surrogate KEYS: Surrogate keys are generated artificially for identifying the records. Too much use of these keys is not recommended when the natural keys can serve the purpose.

Data modelling is the process of developing a data model for storing in the database as per the business requirements. It ensures consistency and ensures NAMING conventions, semantics and security is followed along with ensuring the data quality. In the current era of technologies, data has become the new oil. It is very much important to ensure that the data is structured, developed, utilised and PRESENTED effectively for the growth of the organization. Due to this, data modelling has gained immense popularity and importance among database architects, software developers and business analysts.

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