Data labeling and/or data annotation has long been a critical component of many machine learning and AI initiatives. In recent years, the demand for accurate and reliable data labeling has risen dramatically as the process becomes increasingly vital to the success of numerous projects. But what is data labeling exactly? Data Labeling 2023 – how will it impact businesses? And what trends should we be aware of now that will shape the future of data labeling?

Just as it has been difficult for us to predict the course of this pandemic, so too have healthcare organizations been challenged to predict and evolve their operations to optimize patient care -- as well as revenue. In the initial wave, healthcare's technology needs shifted rapidly. Some organizations immediately shifted as clusters emerged, increasing bed capacity, converting non-clinical spaces to intensive care units and expanding telehealth programs. Meanwhile, others prepared for overflows that did not materialize, leading them to lose their predictable revenue streams from "regular" business. Radiology has become even more stretched thin, facing long- and short-term challenges and revealing just how unsustainable our current ways of working are. Healthcare leaders know the answer is to innovate.

Artificial intelligence (AI) and analytics are providing clinicians and researchers with actionable insights, from early detection to end-of-life-care, and by changing the way research is done and diagnoses are made. However, unlocking the data treasure trove is not a simple exercise for any healthcare organisation. With Asia-Pacific (APAC) expected to become the global leader in IoT spending according to IDC1, healthcare is unsurprisingly becoming increasingly connected in the region. However, it is this connectivity that adds complexity to the data challenge. Healthcare data is now growing at a rate of 48 per cent every year.

Federal IT managers, focused on modernizing government in a climate where the long-standing mantra is "Do more with less," are increasingly optimistic about the potential of artificial intelligence to transform the IT landscape. As with any technology, success is not a given. Instead, AI requires a strong foundation -- a data-centric architecture that optimizes compute power, storage and data to create both a powerful innovation incubator and a transformation accelerator. IT managers within the federal government see the potential of AI. According to a recent study, 77 percent say this technology will change the way government thinks about and processes information, while 61 percent say the technology could solve one or more of the challenges their agency face today.

As data continues to pile up, enterprises that maintain flexible approaches to managing and mining that data are the ones most likely to achieve competitive success, according to Gartner, which recently released its top 10 analytics technologies and trends for 2019. The Global Datashere currently measures 33 zettabytes, according to a recent IDC report, and is predicted to grow to 175 zettabytes by 2025. Navigating this data deluge is no simple matter, as the volume and velocity exceeds the capabilities of existing data analytics rigs running atop legacy architectures. "The size, complexity, distributed nature of data, speed of action, and the continuous intelligence required by digital business means that rigid and centralized architectures and tools break down," explains Donald Feinberg, vice president and distinguished analyst at Gartner. "The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change."

Pure Storage has unveiled a new data-centric architecture in a bid to enable businesses to put machine learning at the heart of their operations. The US firm made a suite of announcements at its annual Pure/Accelerate summit in California this week, as part of plans to help customers' build storage solutions around their data and applications. "Our customers aggressively seek to use data to improve their customer experience and outdistance their competition," said Pure's CEO Charles Giancarlo. "[Our] data-centric architecture enhances and simplifies their ability to use data for intelligence and advantage." The company unveiled a new FlashArray//X product line equipped with NVMe to make databases, virtualised environments and test/dev initiatives faster.