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GUEST ESSAY: 5 security steps all companies should adopt from the Intelligence Community

The Last Watchdog

And, in doing so, the IC has developed an effective set of data handling and cybersecurity best practices. Businesses at large would do well to model their data collection and security processes after what the IC refers to as the “intelligence cycle.” Collection. Gather raw data from various sources. Processing.

Security 149
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Top 7 Data Governance Blog Posts of 2018

erwin

The Top 6 Benefits of Data Governance. www.erwin.com/blog/top-6-benefits-of-data-governance/. GDPR went into effect for businesses trading with the European Union, including hefty fines for noncompliance with its data collection, storage and usage standards. Data Governance 2.0 for Financial Services.

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What can AI and generative AI do for governments?

IBM Big Data Hub

While that can mean hiring new talent like data scientists and software programmers, it should also mean providing existing workers with the training they need to manage AI-related projects. The goal is to free up time for public employees to engage in high value meetings, creative thinking and meaningful work.

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Developments in Health Privacy and Cybersecurity Policy and Regulation: OCR Issues Cybersecurity Warnings and New Health Data Legislation Is Introduced

Data Matters

OCR’s reminders and recommendations for regulated entities include to: assess and reduce risks and vulnerabilities to the availability of ePHI, which is defined as “the property that data or information is accessible and useable upon demand by an authorized person” pursuant to the HIPAA Security Rule. 45 CFR 164.308(a)(5)(i).

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Financial planning & budgeting: Navigating the Budgeting Paradox

IBM Big Data Hub

Why budgeting feels like a marathon Just like marathon training takes months of preparation, crafting a budget involves a lot of data collection, metrics analysis, resource allocation and collaboration. They help speed up work, give quick insights from data and make teaming up across different departments easier.

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Conversational AI use cases for enterprises

IBM Big Data Hub

In addition, ML techniques power tasks like speech recognition, text classification, sentiment analysis and entity recognition. When incorporating speech recognition, sentiment analysis and dialogue management, conversational AI can respond more accurately to customer needs.

Analytics 104
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The most valuable AI use cases for business

IBM Big Data Hub

Customer-facing AI use cases Deliver superior customer service Customers can now be assisted in real time with conversational AI. Voice-based queries use natural language processing (NLP) and sentiment analysis for speech recognition so their conversations can begin immediately.