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Accomplish Agile Business Intelligence & Analytics For Your Business

datapine

No matter if you need to develop a comprehensive online data analysis process or reduce costs of operations, agile BI development will certainly be high on your list of options to get the most out of your projects. The term “agile” was originally conceived in 2011 as a software development methodology.

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Automating Model Risk Compliance: Model Development

DataRobot Blog

It has been over a decade since the Federal Reserve Board (FRB) and the Office of the Comptroller of the Currency (OCC) published its seminal guidance focused on Model Risk Management ( SR 11-7 & OCC Bulletin 2011-12 , respectively). To reference SR 11-7: .

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The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Ontotext

If you’ve used Google, you’ve used the cornucopia of Linked data across the Web, through Google’s Knowledge Graph (Google’s Knowledge Graph is reportedly supported by Freebase – the knowledge acquired by Google in 2010. )

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Fact-based Decision-making

Peter James Thomas

However, often the biggest stumbling block is a human one, getting people to buy in to the idea that the care and attention they pay to data capture will pay dividends later in the process. These and other areas are covered in greater detail in an older article, Using BI to drive improvements in data quality.

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Showpad accelerates data maturity to unlock innovation using Amazon QuickSight

AWS Big Data

Each of these tools were getting data from a different place, and that’s where it gets difficult,” says Jeroen Minnaert, head of data at Showpad. “If If each tool tells a different story because it has different data, we won’t have alignment within the business on what this data means.”

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Automating Model Risk Compliance: Model Validation

DataRobot Blog

When the FRB’s guidance was first introduced in 2011, modelers often employed traditional regression -based models for their business needs. While SR 11-7 is prescriptive in its guidance, one challenge that validators face today is adapting the guidelines to modern ML methods that have proliferated in the past few years.

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Themes and Conferences per Pacoid, Episode 7

Domino Data Lab

There are essentially four types encountered: image/video, audio, text, and structured data. If you’re currently wrangling with data quality issues, you might start looking ahead at how staffing or legal concerns will be among the next hurdles to confront. In any case, there’s a kind of survival analysis for AI adoption.