Businesses are increasingly turning to advanced technology tools to facilitate seamless data flow, which in turn provides a visible track of compliance through the entire length of their value chain—nEnter DLT.n
It is absolutely necessary to break away from the traditional mindset of viewing applications and operations as siloed services. Transformational Leaders must view their businesses from beyond the “services” prism—and execute swiftly.
By Andy Nallappan, Chief Technology Officer and Head of Software Business Operations, Broadcom Software – A couple of decades ago, when nearly all centralized computing ran in data centers, companies began talking about how to accelerate decision-making and reduce latency issues that frustrated users (commonly referred to as the "world wide wait"). This problem became more acute as the increasing use of mobile and IoT devices put new strain on existing internet infrastructure.
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The ransomware threat is evolving faster than people’s ability to keep track. A common misconception is that payloads are usually delivered by phishing emails. While that may be true for many cases, the new breed of ransomware is much more likely to be launched by an intruder who has already breached the network. In fact, the battle is now focused on monitoring activity within your environment rather than preventing users from clicking unknown links.
While software introduces new ways of doing business, it can also introduce serious new risks. When it comes to digital transformation, don’t let inherit risks stand in the way of the competitive advantage your business seeks.
As VMware gains momentum with their sovereign cloud initiative , we turned to leading partners AUCloud, Datacom, STC, NxtGen, TietoEVRY, ThinkOn, and UKCloud and their customers to find out why a sovereign cloud is essential.
Many enterprises around the world are discovering new insights, revenue and efficiencies through the use of artificial intelligence (AI). At the same time, companies are discovering that they can accelerate their projects by adjusting their infrastructure approach. These changes have helped to create new opportunities and growth options, as well as preventing a trip to the pile of AI failures.
Even with the best planning, companies should be ready to adjust projects when faced with unexpected challenges or as parameters change due to business needs. This is no difference in the development of AI models, where initial results may lead teams down a new path that requires a rethinking of their AI infrastructure.
The use of artificial intelligence (AI) continues to transform enterprises by creating new products, boosting revenues, cutting costs and increasing efficiency. But getting to those successful implementations has been tricky for some organizations, given the complexity of the technology and potential for a high failure rate for those who jump in without a plan.
By George Trujillo, Principal Data Strategist, DataStax – Think about your favorite recipe. You might have all the ingredients for an apple pie, but there’s no guarantee all the elements will come together to produce a delicious dessert. Similarly, many organizations have built data architectures to remain competitive, but have instead ended up with a complex web of disparate systems which may be slowing them down.
Enterprises are betting big on machine learning (ML). According to IDC, 85% of the world’s largest organizations will be using artificial intelligence (AI) — including machine learning (ML), natural language processing (NLP) and pattern recognition — by 2026.