Data modeling is analyzing and defining all the distinct data your business collects and produces and the relationships between those bits of data. Using text, symbols, and diagrams, data modeling concepts create visual representations of data as it is captured, stored, and used in your business. Because your business determines when and how your data is used, the data modeling process is an exercise in understanding and clarifying your data needs.
The Benefits Of Data Modeling
By modeling your data, you can document your data types, how you use it, and data management requirements around its use, protection, and governance. The benefits of data modeling include:
- Creating a collaboration structure between your IT team and your sales teams.
- Expose opportunities to improve business processes by defining data needs and uses.
- Save time and money on IT and process investments through proper planning.
- Reducing errors (and error-prone redundant data entry) while improving data integrity.
- Increase the speed and performance of data retrieval and analysis by planning for capacity and growth.
- Definition and monitoring of key performance indicators adapted to your business objectives.
The process of obtaining the results of data modeling is just as important as the actual outcomes.
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Conceptual Data Modeling
A conceptual data model defines the overall structure of your business and your data. Your conceptual data model, used to organize business concepts, is defined by your stakeholders, data engineers, or data architects. For example, you might have data about customers, employees, and products, and each of these data buckets, called entities, has relationships with other entities. Entities and entity relationships are defined in your conceptual data model.
How does this data modeling tool perform?
Another essential attribute to consider is performance: speed and efficiency, which translates to the ability to keep the business running smoothly while users run analytics. The best-planned data model isn’t planned if it can’t work in natural, concrete conditions, which hopefully involve business growth and increased volumes of data, retrieval and analysis.
Does this data modeling tool require maintenance?
If every change to the business model requires tedious changes to your data model, your business will not get the most out of the model or associated analytics. Look for a data modeling tool that makes maintenance and updates manageable so your business can scale as needed while still having access to the most recent data.
Will this data modeling tool protect your data?
Government regulations require you to protect your customer data, but the viability of your business requires protecting all of your data as a valuable asset. Please ensure the data modeling tools you choose have robust security measures built in, including controls around giving access to those who need it and blocking those who don’t.
The most important consideration about data modeling is that it aims to create the foundation of a database that can quickly load, retrieve, and analyze large volumes of data. A practical data modeling concept requires mapping business data, relationships between data, and a way to use the data.
How often should the training of a data model be re-run?
How often training this data model needs to be done again varies depending on the model and the problem it solves. This may imply that its learning is carried out again every day, every week or more periodically, for example, every month or every year, depending on the training of the datasets, both in terms of a decrease in model performance and other data science considerations.
Which three ideas are the foundation of data modeling?
There are three concepts in database modeling: conceptual data modeling, logistics data modeling, and physical data modeling. From abstract to discrete, data modeling concepts create a blueprint of how data is organized and managed in an organization.
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