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Build a strong data foundation for AI-driven business growth

CIO Business Intelligence

In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.

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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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What you need to know about product management for AI

O'Reilly on Data

AI products are automated systems that collect and learn from data to make user-facing decisions. All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. Machine learning adds uncertainty.

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The state of data quality in 2020

O'Reilly on Data

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

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Human-centered design and data-driven insights elevate precision in government IT modernization

IBM Big Data Hub

Government executives face several uncertainties as they embark on their journeys of modernization. What makes or breaks the success of a modernization is our willingness to develop a detailed, data-driven understanding of the unique needs of those that we aim to benefit.

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Enterprise Architecture: Secrets to Success

erwin

managing risk vs ROI and emerging countries)? Compliance and Legislation : How do we manage uncertainty around legislative change (e.g., data protection, personal and sensitive data, tax issues and sustainability/carbon emissions)? Data Overload : How do we find and convert the right data to knowledge (e.g.,

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12 Cloud Computing Risks & Challenges Businesses Are Facing In These Days

datapine

It provides better data storage, data security, flexibility, improved organizational visibility, smoother processes, extra data intelligence, increased collaboration between employees, and changes the workflow of small businesses and large enterprises to help them make better decisions while decreasing costs. Security issues.

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