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Managing risk in machine learning

O'Reilly on Data

At the recent Strata Data conference we had a series of talks on relevant cultural, organizational, and engineering topics. Here's a list of a few clusters of relevant sessions from the recent conference: Data Integration and Data Pipelines. Data Platforms. Model lifecycle management.

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CIO insights: What’s next for AI in the enterprise?

CIO Business Intelligence

“Our internal data and adherence to process is where our focus is, and we don’t necessarily want to leap ahead until we feel like we have a stable footing there.” Ensuring data integrity is part of a broader governance approach organizations will require to deploy and manage AI responsibly.

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7 steps for turning shadow IT into a competitive edge

CIO Business Intelligence

Ask IT leaders about their challenges with shadow IT, and most will cite the kinds of security, operational, and integration risks that give shadow IT its bad rep. That’s not to downplay the inherent risks of shadow IT. There may be times when department-specific data needs and tools are required.

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What is Integrated Business Planning (IBP)?

IBM Big Data Hub

By integrating financial planning with strategic and operational planning, organizations can evaluate financial profitability, identify potential gaps or risks, and make necessary adjustments to achieve financial targets. Data integration and analytics IBP relies on the integration of data from different sources and systems.

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AI Technology is Invaluable for Cybersecurity

Smart Data Collective

AI poses a number of benefits and risks for modern businesses. A successful breach can result in loss of money, a tarnished brand, risk of legal action, and exposure to private information. Cybersecurity aims to stop malicious activities from happening by preventing unauthorized access and reducing risks.

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Why you should care about debugging machine learning models

O'Reilly on Data

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. 9] See: Teach/Me Data Analysis. [10] Sensitivity analysis.

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Best BI Tools Examples for 2024: Business Intelligence Software

FineReport

Moreover, BI platforms provide the means for organizations to harness their data assets effectively, leading to improved customer satisfaction through personalized services and targeted marketing initiatives. This includes structured, unstructured, and real-time data, ensuring that the platform can handle diverse data types effectively.