Enterprises see embracing AI as a strategic imperative that will enable them to stay relevant in increasingly competitive markets. However, it remains difficult to quickly build these capabilities given the challenges with finding readily available talent and resources to get started rapidly on the AI journey.
Cloud transformation is ranked as the cornerstone of innovation and digitalization. The legacy IT infrastructure to run the business operations—mainly data centers—has a deadline to shift to cloud-based services. Agility, innovation, and time-to-value are the key differentiators cloud service providers (CSP) claim to help organizations speed up digital transformation projects and business objectives.
We’ve all heard that data helps businesses make better decisions. The good news? This isn’t just speculation: research shows that companies who use data to drive decision making increase revenues by an average of more than 8%, are 23 times more likely to attract new customers, and are 19 times more likely to be profitable as a result.
The AI technologies of today—including not just large language models (LLMs) but also deep learning, reinforcement learning, and natural-language processing (NLP) tools—will equip telcos with powerful new automation and analytics capabilities. AI-powered automation is already driving significant margin growth by reducing costs.
AI is the next revolutionary technology that will accelerate the mission of the Department of Defense. Newly boundless in its applications, “AI” joins “cyber” and “cloud” as the most important information technologies that have arrived in the last 25 years.
Telecommunications companies are currently executing on ambitious digital transformation, network transformation, and AI-driven automation efforts. While navigating so many simultaneous data-dependent transformations, they must balance the need to level up their data management practices—accelerating the rate at which they ingest, manage, prepare, and analyze data—with that of governing this data.
In the ever-evolving landscape of the financial services Industry, change is a constant and transformation is a requirement—to stay at pace with new regulations, risk mitigation, and the technological developments that support transformation. And just as financial services experiences its cycles, this time of year I find myself returning to the topic of cost reduction.
Cloudera recently released a fully featured Open Data Lakehouse, powered by Apache Iceberg in the private cloud, in addition to what’s already been available for the Open Data Lakehouse in the public cloud since last year. This release signified Cloudera’s vision of Iceberg everywhere. Customers can deploy Open Data Lakehouse wherever the data resides—any public cloud, private cloud, or hybrid cloud, and port workloads seamlessly across deployments.
Your company collects data from different sources and then you analyze the data to help make the right decisions. But you aren’t quite getting the results that you expect. Maybe the insights aren’t accurate. Perhaps the process is time consuming and cumbersome. Or you are only currently using data for a few use cases and struggle to implement organization wide.
Fast moving data and real time analysis present us with some amazing opportunities. Don’t blink—or you’ll miss it! Every organization has some data that happens in real time, whether it is understanding what our users are doing on our websites or watching our systems and equipment as they perform mission critical tasks for us. This real-time data, when captured and analyzed in a timely manner, may deliver tremendous business value.
