O'Reilly on Data
JULY 28, 2020
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.
Rocket-Powered Data Science
FEBRUARY 15, 2023
encouraging and rewarding) a culture of experimentation across the organization. Encourage and reward a Culture of Experimentation that learns from failure, “ Test, or get fired! This can be overcome with small victories (MVP minimum viable products, or MLP minimum lovable products) and with instilling (i.e., Test early and often.
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CIO Business Intelligence
NOVEMBER 13, 2023
Inadequate data management and governance Data is at the heart of digital transformation, and companies that don’t have adequate data management processes in place are likely to struggle. Ensuring data quality, privacy, and security is essential.
DataKitchen
APRIL 13, 2021
RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Data breaks.
CIO Business Intelligence
JUNE 14, 2023
Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications. The number of data analytics certs is expanding rapidly.
Ontotext
FEBRUARY 14, 2024
How do you measure its utility? If you ask it to generate a response, and maybe it hallucinates, you can then constrain the response it gives you, from the well-curated data in your graph. Data quality Knowledge graphs thrive on clean, well-structured data, and they rely on accurate relationships and meaningful connections.
O'Reilly on Data
MARCH 31, 2020
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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