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Episode 2: Defining and Solving Business Risks
Join Us for Unlocking the Vault with Dan Linstedt Audit compliance, global security, distributed privacy policy, and insuring raw data is available for data scientists are just a few of the business risks we face…but are all tackled with the DV2.0 methodology. In this episode, Dan outlines some of the most vexing problems encountered by business…
Poison Data and Espionage, AI, ML, Deep Learning
In Episode 2 of Unlocking the Data Vault, I mentioned the term: Poison Data. I must admit, it’s a cool term, and I also admit the term is not mine. It stems from espionage in an attempt to poison data that feeds AI / ML and deep learning algorithms. It distorts documents, imagery, video, and…
Why Modern Analytics Fails to Scale Sustainably
Modern analytics initiatives often fail to scale due to a systemic fracture between decision rights and accountability. This misalignment leads to fragmented ownership, repeated rework, and deferred decisions that undermine sustainable growth. The core issue lies in governance and operating model design rather than technology alone. Understanding this fracture clarifies why technology upgrades alone cannot resolve scaling challenges and highlights the political trade-offs involved in realigning authority and incentives.
Positive and Negative Impacts of AI Machine Learning
While the craze of AI has been dominating several minds these days, there are two sides to reality that the majority of folks aren’t prepared for or don’t know the answer to. The first of course is that it’s not there yet. And, the second is that it’s getting there at an unprecedented pace. Nevertheless,…
FAQ About Data Vault 2.0
This post is a short Q&A of frequently asked questions that I get all the time. Whether you’re just getting started with Data Vault, or are fully certified, there is something here for everyone. If you find that you have additional questions for me, feel free to use the contact-us form on our site to…
Medallion Labels and Their Historical Roots in Data Readiness Classification
Medallion labels classify data readiness stages but do not constitute an architecture or governance framework. These labels have historical precedents that reflect recurring enterprise needs to communicate data condition amid scaling pressures. Misinterpreting them as control mechanisms creates accountability gaps and semantic drift. Recognizing their lineage clarifies what they communicate and what responsibilities remain separate. This understanding reduces risks tied to oversimplified data state classifications.
