Data Strategy
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The research outlines best practices for enabling self-service analytics, emphasizing alignment with business goals, collaborative development between IT and business, and lightweight management for effective data and analytics success.
LinkHow to Enable Self-Service Analytics to Ensure D&A Success
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
Safeguarding sensitive information with Oracle's AI platform PII ...
blogs.oracle.com
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
The cornerstone of a successful RAG implementation is the quality of your data
DataStax • Retrieval Augmented Generation (RAG) Explained: Understanding Key Concepts
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 t... See more
Larry Burns • The Currency of Information: What Kind of Asset Is Data? (Part Two)
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
Data quality management ensures data is fit for consumption and meets the needs of data consumers. To be of high quality, data must be consistent and unambiguous. You can measure data quality through dimensions including accuracy, completeness, consistency, integrity, reasonability, timelines, uniqueness, validity, and accessibility.
Astasia Myers • 4 Data Trends to Watch in 2020
The purpose of metadata is not simply to describe data and information assets, but rather to proactively answer questions that consumers might have about them. Where did this data come from? How up to date is it? How trustworthy is it? What business process(es) created it? What business process(es) use it? What transformations or filtering have bee... See more