Remove Testing Remove Uncertainty Remove Unstructured Data
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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Weve seen this across dozens of companies, and the teams that break out of this trap all adopt some version of Evaluation-Driven Development (EDD), where testing, monitoring, and evaluation drive every decision from the start. Two big things: They bring the messiness of the real world into your system through unstructured data.

Testing 174
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The genAI opportunity: From ‘data to insight’ to ‘context to action’

CIO Business Intelligence

There’s a constant risk of data science projects failing by (for example) arriving at an insight that managers already figured out by hook or by crook—or correctly finding an insight that isn’t a business priority. And some of the biggest challenges to making the most of it are well-suited to the skills and mindset of data scientists.

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American Honda IT to fuel innovation with generative AI

CIO Business Intelligence

The completion of such transformative EV and hydrogen fuel cell engineering — amid uncertainty about which technology will prevail as the industry standard — reflects the one constant American Honda’s VP of IT Bob Brizendine has confronted throughout his 36 years with the company: an ever-changing, winding road that never slows down. “We

IT 122
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Covid Data: An anomalous blip, or the new normal?

Cloudera

Insurance and finance are two industries that rely on measuring risk with historical data models. They have traditionally been slower-moving to adopt new structured and unstructured data inputs as regulatory considerations are always top of mind. This can be done at speed, and at scale.

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Belcorp reimagines R&D with AI

CIO Business Intelligence

These circumstances have induced uncertainty across our entire business value chain,” says Venkat Gopalan, chief digital, data and technology officer, Belcorp. “As The R&D laboratories produced large volumes of unstructured data, which were stored in various formats, making it difficult to access and trace.

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11 dark secrets of data management

CIO Business Intelligence

Some of the paradoxes relate to the practical challenges of gathering and organizing so much data. Others are philosophical, testing our ability to reason about abstract qualities. And then there is the rise of privacy concerns around so much data being collected in the first place. Unstructured data is difficult to analyze.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

Additionally, quantitative data forms the basis on which you can confidently infer, estimate, and project future performance, using techniques such as regression analysis, hypothesis testing, and Monte Carlo simulations. Making sense of and deriving patterns from it calls for newer, more advanced technology.