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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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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. That’s where model debugging comes in. Sensitivity analysis.

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. Informatica Axon Informatica Axon is a collection hub and data marketplace for supporting programs.

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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.

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Five areas where EA matters more than ever

CIO Business Intelligence

While resiliency has always been a focus of EA, “the focus now is on proactive resiliency” to better anticipate future risks, says Barnett. To meet this goal, it is redefining the EA components it measures to understand, for example, which data must be stored in which formats so it can be studied over time.