Data Strategy
Data is always an abstraction of reality based on underlying assumptions as to how to categorize the unstructured phenomena of the real world.
Clayton M. Christensen • Competing Against Luck: The Story of Innovation and Customer Choice
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
Meta-data management. Meta-data, which is commonly defined as “data about the data” is used to ensure traceability throughout the environment and ensure there is a common understanding of the meaning of the information contained in the BI environment.
Steve Williams • The Profit Impact of Business Intelligence
The cornerstone of a successful RAG implementation is the quality of your data
DataStax • Retrieval Augmented Generation (RAG) Explained: Understanding Key Concepts
Yet many of us who have tried to use data to inform decisions in organizations have experienced a different reality. One where we are constantly confused by how metrics are defined, bicker over how to interpret various analyses, and struggle to apply the insights into action.
Julie Zhuo • The Data-Informed Manifesto
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
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
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
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.