Remove Analytics Remove Experimentation Remove Metadata
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

encouraging and rewarding) a culture of experimentation across the organization. Most of these rules focus on the data, since data is ultimately the fuel, the input, the objective evidence, and the source of informative signals that are fed into all data science, analytics, machine learning, and AI models. Test early and often.

Strategy 290
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How to build a safe path to AI in Healthcare

CIO Business Intelligence

This is evident in the rigorous training required for providers, the stringent safety protocols for life sciences professionals, and the stringent data and privacy requirements for healthcare analytics software. The stakes in healthcare are higher, as errors can have life-or-death consequences. To learn more, visit us here.

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AI in Analytics: The NLQ Use Case

Sisense

In my previous blog , I wrote about Natural Language Query (NLQ, or search analytics for some), as one of the major topics that we, the AI group in Sisense, are working on. NLQ is one of the oldest AI disciplines, but we’ve only recently started hearing about it in conjunction with BI and analytics.

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What is a data scientist? A key data analytics role and a lucrative career

CIO Business Intelligence

Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. It doesn’t conform to a data model but does have associated metadata that can be used to group it. What is a data scientist?

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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 363
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6 DataOps Best Practices to Increase Your Data Analytics Output AND Your Data Quality

Octopai

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. Did you just have a spectacular new idea for a data analytics product? The same goes for data analytics products.

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AI Governance: Break open the black box

IBM Big Data Hub

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation. This includes capturing of the metadata, tracking provenance and documenting the model lifecycle. The ability to track and share model facts and documentation across the organization provides backup for analytic decisions.