Data Strategy
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
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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
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
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
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
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
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
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