Data Strategy
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
Larry Burns • The Currency of Information: Managing Data as an Asset (Part Three)
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)
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
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
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
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
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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
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.