Amal leans back and looks at her notebook. Three pages filled. Plus four whiteboards covered in post-its from the requirements workshop. Somewhere in all of that are the business objects she needs for the new data model. "Diego," she asks, "how long did it used to take you to figure out what actually needed to be modeled after a workshop like this?" Diego smiles. "Back in the day? Sometimes a week."
AI systems require perfectly structured data but cannot create the necessary data models themselves. Why does even the most powerful AI fail to understand what a "customer" or "product" means in a specific company? And why is precisely this definition work the key to success for every AI implementation?
Artificial intelligence is currently revolutionizing virtually every business area. Yet amid all the enthusiasm for these technologies, a fundamental paradox is often overlooked: AI requires high-quality, structured data to function at all. At the same time, AI itself is unable to create the data structures it needs to work.
How can I use natural language in my modeling process to achieve high-quality information models?
Resilient and temporally correct dimension Id’s for fact as well as dimension tables
In a dimensional data model based on a Data Vault data model, are integer values still necessary as dimension Id’s? And if so, how can the dimension Id’s be provided correctly?
An EXASOL Webinar serie
We are back again after a long time, with a new webinar. The last one, we (Mathias and I) did together is almost four years ago. Time flies by! What's up this time?
The fictitious company FastChangeCoTM has developed a possibility not only to manufacture Smart Devices, but also to extend the Smart Devices as wearables in the form of bio-sensors to clothing and living beings. With each of these devices, a large amount of (sensitive) data is generated, or more precisely: by recording, processing and evaluating personal and environmental data.
In recent weeks I have read so many pessimistic and negative articles and comments in the social media about the state of data modeling in companies in Germany, but also worldwide.
Why? I don't know. I can't understand it.
I know many companies that invest a lot of time in data modeling because they have understood the added value. I know many companies that initially rejected data modeling as a whole, but understood its benefits through convincing and training.
Isn't it the case that we (consultants, managers, project managers, subject-matter experts, etc.) should have a positive influence on data modeling? To support our partners in projects in such a way that data modeling becomes a success? If we ourselves do not believe that data modeling is a success, then who does?
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