What are the best data modeling standards?

It is time to move away from these techniques and focus more on a LLM approach these days. More data is always better than less data that is structured and named or labeled correctly. There are GPUs and neural software platforms like tensor flow which are the way to go. ChatGPT is a good example here.

Adding details like data types, constraints, keys, and indexes ensures that the database will meet user needs, remain consistent, and operate with integrity. The standards you mentioned, the relational model and the dimensional model, have their unique strengths. While the relational model provides a simple, flexible structure suitable for a variety of data, the dimensional model shines in the context of business processes, making data easy to understand and analyze. One point I'd like to add is that choosing between these models, or indeed others, is contingent on the specific requirements of the business or process at hand. The best models align not only with the nature of the data but also with the goals and needs of the user.

One pertinent ethical issue that wasn't addressed in the article is data privacy and security. When designing and implementing physical data models, it's critical to consider privacy regulations and ethical standards, especially in our data-driven era with legislation like the GDPR and CCPA. Non-compliance could lead to legal repercussions and loss of trust from stakeholders.

In my prvious startup, we designed the whole tech architecture from scratch, including data modelling and schema design to make sure the system is robust and scalable. One of the main learnings from the experience when it comes to data modelling is this: - Data modelling is done based on how you want to use the data - Yes, there are many data modelling techniques out there. However, which technique you choose ultimately still boils down to how you want to use the data. - Once you have the clear direction, choosing a data modelling technique is pretty clear.

I really liked the book "Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World" by William Kent for making data modeling (dare I say it) fun and exciting. If you want to read a genuinely-timeless perspective on the philosophy and psychology of organizing structured information, give it a try.

Another most important piece in any data (engineering) pipeline apart from defining data relationships and storage is modelling the pipeline itself. A data pipeline consists of various horizontal stages like generation, transformation, ingestion, caching, curation, labelling, versioning and serving. While also having many vertical stages like security, adaptability, resource and compute constraints and much more. All of these stages need proper planning and abstraction with respect to the actual data dynamics. Although yes this may be revised over with actual technical details as well as it shapes up. Without proper data abstractions any ML project is bound to be bottlenecked or even worse facing unreliable results and even false metrics.

I believe the next steps or trends in data modelling is how one should model unstructured data. With the growing use of foundational models, it is more essential to understand how we manipulate unstructured forms of data such as images, audio, video, etc to fit our use cases. We require organisations to understand how they can scale their services as we grow more dependent on AI models in the future.