Been a while since I have read the Teradata blog, some good thoughts. This is the challenge for many enterprises. And their operational data today is still relational.
Using AI on All Your Data: Building AI Models with Relational Data
By Ben MacKenzie in the Teradata Blog
As I have written elsewhere, the most striking advances in AI in the last few years have been in computer vision, natural language processing, and reinforcement learning: think of image classification, Google Translate, and AlphaGo. What has been overlooked is that innovations in machine and deep learning is also quietly revolutionizing the analysis of tabular, or relational, data. The less-hyped advances in the analysis of relational data will have far-reaching consequences for enterprises, since every enterprise has relational data. Powerful new techniques for relational data will enable enterprises with the right technology partners to make better decisions faster, with all of their data, all of the time. However, there are hurdles to achieving a future state of pervasive data intelligence across all enterprise data. In this blog, I will reflect on some of the many challenges particular to building models with relational data.
The most obvious difference is the data itself. When you are building a computer vision or natural language model, you start with a static set of images associated with categories, or a static corpus of text paired with corresponding translations. Acquiring these data sets, especially the labeled data that is essential for training the models, can be very difficult. However, the data acquisition challenges are fundamentally different from those involved in acquiring a relational data set. Relational data is the lifeblood of an organization. Like blood, it flows into and out of different parts of an organization, where it lives in diverse operational databases, typically using different data management schemes, and is subject to diverse security and privacy constraints. And yet, having a single, coherent, complete view of all the data across the organization is critical to the success of an AI effort. Achieving such a single perspective on all enterprise data is a job for an enterprise data warehouse, whether it’s a product like Teradata Vantage, a well-designed and implemented Data Lake, or a logical data warehouse combining data warehouse and Data Lake. A data warehouse is your unified ... '
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