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Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis

DataKitchen

Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis Ah, the data quality crisis. It’s that moment when your carefully crafted data pipelines start spewing out numbers that make as much sense as a cat trying to bark. You’ve got yourself a recipe for data disaster.

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Bringing an AI Product to Market

O'Reilly on Data

In this article, we turn our attention to the process itself: how do you bring a product to market? The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. Agreeing on metrics. Identifying the problem.

Marketing 363
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Don’t Fear Artificial Intelligence; Embrace it Through Data Governance

CIO Business Intelligence

Preparing for an artificial intelligence (AI)-fueled future, one where we can enjoy the clear benefits the technology brings while also the mitigating risks, requires more than one article. This first article emphasizes data as the ‘foundation-stone’ of AI-based initiatives. Establishing a Data Foundation.

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Financial Dashboard: Definition, Examples, and How-tos

FineReport

In today’s dynamic business environment, gaining comprehensive visibility into financial data is crucial for making informed decisions. In this article, we will explore the concept of a financial dashboard, highlight its numerous benefits, and provide various kinds of financial dashboard examples for you to employ and explore.

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Product Management for AI

Domino Data Lab

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. It just looks like plausible English.

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Measuring Validity and Reliability of Human Ratings

The Unofficial Google Data Science Blog

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. Last, we’ll provide a case study of how xRR can be used to measure improvements in a data-labeling platform.

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Data Science, Past & Future

Domino Data Lab

Greg Linden ‘s article about splitting the website on Amazon. My colleague, Ben Lorica at O’Reilly, he and I did three large surveys about adoption for ABC, that’s AI, Big Data, and Cloud in enterprise. We have an article on this on Domino. One is data quality, cleaning up data, the lack of labelled data.