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When Your Dashboard Goes Blank – Who do you call?

Paul Blogs on BI

The first message was from someone wanting to book a service for their Alfa Romeo and the second message was from a another person that was panicked by the fact that the dashboard on their Alfa Romeo Stelvio had just gone blank and they did not know what to do! So next time your dashboard goes blank, there is a service that covers that.

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The Five Use Cases in Data Observability: Ensuring Data Quality in New Data Source

DataKitchen

Detecting Anomalies: Identifying irregularities such as embedded delimited data, leading/trailing spaces, inconsistent patterns, and non-standard blank values. Are There Non-Standard Blank Values? Schema Validation: Ensuring the data adheres to the defined schema. Key Data Evaluation Questions : Do I Understand This New Data?

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War Rooms Suck

DataKitchen

It was reminiscent of another time when I heard about a three-day-long marathon session or even that peculiar incident where a staggering 26 people were engrossed for over 8 hours only to find the root cause of a blank report. Such episodes epitomize inefficiency and disarray; I’ve grown to detest them. War rooms steal productivity.

Testing 120
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The Syntax, Semantics, and Pragmatics Gap in Data Quality Validation Testing 

DataKitchen

They must thus rely on data stewards or business customers to ‘fill in the blank’ on various data testing rules. These tests allow users to customize testing protocols to fit specific business requirements with a “fill in the blank” model, offering a perfect blend of speed and robustness in data testing.

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SHACL-ing the Data Quality Dragon II: Application, Application, Application!

Ontotext

It points to the violated SHACL shape, which is typically identified with a blank node identifier. However, blank nodes are temporary identifiers that can change, or the result may be physically separate from the shapes. The sh:sourceShape property is a bit more complex.

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What is a Data Mesh?

DataKitchen

Third-generation – more or less like the previous generation but with streaming data, cloud, machine learning and other (fill-in-the-blank) fancy tools. We’ll cover some of the potential challenges facing data mesh enterprise architectures in our next blog. And you guessed it, managed by a specialized team drowning in technical debt.

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Even more feathers and rumours! Here are the PASS Expense Submissions I made while on the PASS Board.

Jen Stirrup

I have blogged previously about PASS Finances and I also wrote a number of blogs while I was on the Board, which were not well read at the time by the community. The demands for transparency were not matched by readership of the blogs posts themselves, which was always strange to me. So here you go. Fill your boots.