Data Strategy
The starting point of any RAG system is its source data, often consisting of a vast corpus of text documents, websites, or databases
DataStax • Retrieval Augmented Generation (RAG) Explained: Understanding Key Concepts
The cornerstone of a successful RAG implementation is the quality of your data
DataStax • Retrieval Augmented Generation (RAG) Explained: Understanding Key Concepts
Safeguarding sensitive information with Oracle's AI platform PII ...
blogs.oracle.com
Meta-data management efforts provide for the management and traceability of all components of the BI environment. Meta-data needs range from ensuring that definitions and business rules associated with BI applications are made available to business users to providing source data mapping information to manage the environment or answer questions
... See moreSteve Williams • The Profit Impact of Business Intelligence
Also, eliminating irrelevant or sensitive information such as personally identifiable information (PII) is crucial to align with privacy standards
DataStax • Retrieval Augmented Generation (RAG) Explained: Understanding Key Concepts
In today’s data world, there are so many options for an EL tool to avoid you developing your own extracting script and to help you gain a LOT of time.
Fivetran, Mage, and Airbyte to mention a few.
You don’t have to maintain custom scripts, these tools come with +300 connectors, basic scheduling, and error handling.
Jeremy • What I Learned After One Year of Building a Data Platform From Scratch
This brings us to our third point: Because data only has meaning within a particular context, consumers of data and information need metadata to help them decide how to use it. This is an important difference between data and other assets. You don’t need much metadata when deciding whether to spend a $20 bill, but you do need metadata when trying... See more
Larry Burns • The Currency of Information: What Kind of Asset Is Data? (Part Two)
One of the problems with owning your own data is that it ignores the question of what format that data is in. Data formats are sometimes considered carefully, but they can also be hammered out fast to get a minimum viable product out the door.
Byrne Hobart • The Promise and Paradox of Decentralization
Quality data is crucial for automation, pattern recognition, and training AI for advanced capabilities. A data-first mindset and a data-focused platform are essential for long-term success