Is Your Company’s Data Ready for Generative AI?
CDOs are excited about generative AI (genAI), but a recent survey reveals a gap between enthusiasm and action.
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CDOs are excited about generative AI (genAI), but a recent survey reveals a gap between enthusiasm and action.
The quality and reliability of the data used to train AI models directly impacts the model’s performance and effectiveness.
The rise of generative AI challenges data governance practices, necessitating adaptation to ensure responsible and trustworthy use of this powerful technology.
New survey data uncovers the alarming trend of unauthorized use of sensitive corporate data in generative AI tools, posing persistent risks.
Experts Reveal Strategies to Bridge the Gap” explores challenges and solutions in leveraging data for AI adoption.
Experts warn of a potential data gap in the age of AI. Is your organization prepared? Find out how to ensure your data is ready for the AI revolution.
The dawn of the digital era has marked a significant transformation in the way businesses operate. Central to this change is the rise of data science and data operations (Data Ops), two fields that are reshaping the landscape of various industries.
The Terrapin attack takes advantage of several techniques and vulnerabilities in the SSH protocol to compromise the confidentiality and integrity of communications.
The implications of the stolen employee data on Okta’s overall security remain a concern. Going forward, Okta must focus on strengthening their security infrastructure, increasing transparency, and implementing robust measures to prevent future breaches.
When considering your data architecture, it is essential to carefully evaluate the scalability, performance, data integration, flexibility, costs, and return on investment of data warehouses, data lakes, and data marts.