Data Privacy During Economic Downturn: How to Make It Work With Limited Resources?

Data Privacy During Economic Downturn
The global spread of the novel coronavirus (COVID-19) and the economic impact that followed has prompted many businesses to furlough the workforce or migrate from the traditional office to remote-working environments. The volatile landscape has created incremental risks, especially for organizations heavily relying on IT Sec/Ops teams to monitor security and privacy and enforce regulatory compliance.
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These Trends Will Shape BI in 2019

CASERTA

Join us for this on-demand with Looker where we explore the top Business Intelligence trends for 2019. In this 50-minute webinar, you will learn about the issues affecting BI, the role of analytics as a critical success factor for organizational success, and how companies are leveraging BI and analytics for high-value insights.
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Delivering Real-Time Business Insights from ERP Data in the Cloud

Businesses succeed when they can extract more contextual, relevant, and predictive insights from ERP data in real time. ERP analytics can deliver more powerful data-driven insights if deployed in a cloud-based platform. The scalability of cloud-based data repositories—such as data lakes and data warehouses—can allow more users to concurrently query, explore, correlate, analyze, and visualize more ERP data more rapidly than would be possible on many on-premises analytics platforms.
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The evolution of analytics: From BI to AI

AVORA

Analytics have come a long way – from data warehousing and manual report building to centralised data in the cloud and machine learning driven analytics. Business Intelligence (BI) tools were revolutionary when they arrived: they made analytics more accessible to the wider business users. Their speedy and powerful analytics provided insights that businesses only dreamt off in the past. But as technology advances, so has the analytics world evolved.
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Overcoming Data Management Challenges in AI/ML

The ever-growing data landscape drives initiatives to automate many aspects of the analytics lifecycle; such as data access, enablement of semantics, BI and others. Automation has become an integral part of our daily lives in the enterprise data fabric. The AI-driven initiative to automate the data access and provide guidance to the right data assets, correlates with the initiatives of the data scientists to get access to more curated data.
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