Remove Data Collection Remove Experimentation Remove Information Remove Measurement
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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 362
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Health check on Tech: CK Birla Hospitals CIO Mitali Biswas on moving the needle towards innovation

CIO Business Intelligence

In this conversation with Foundry, Mitali discusses the accelerated importance of technology in healthcare, on enabling healthcare providers with data and why her team isn’t afraid of experimentation. The need is for a user-friendly system that captures all the data. Can you tell me about your career path so far?

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eCommerce Brands Use Data Analytics for Conversion Rate Optimization

Smart Data Collective

It is a crucial metric that provides priceless information about your website’s ability to transform visitors into paying customers. Collecting Relevant Data for Conversion Rate Optimization Here is some vital data that e-commerce businesses need to collect to improve their conversion rates.

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Digital listening reveals 3 leading innovation drivers

CIO Business Intelligence

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data.

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What you need to know about product management for AI

O'Reilly on Data

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Measurement, tracking, and logging is less of a priority in enterprise software.

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How Svevia connects roads, risk, and refuse through the cloud

CIO Business Intelligence

In order to do that, a digital transformation was required, and when it comes to information provision, there wasn’t much, so we put in place basic platforms to handle data, and developed a cloud architecture for infrastructure and applications.” It’s important to get access to data in order to digitize an entire company,” she says.

Risk 96
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Glossary of Digital Terminology for Career Relevance

Rocket-Powered Data Science

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: (1) Retail. (2) 2) Disaster Response. (3) 3) Machine maintenance. (4)