Machine learning is one of the biggest trends in data, with applications in areas including training, deployment, security, and prediction. In the context of machine learning, deploying infrastructure means creating or modifying clusters and data centers to meet the requirements of machine learning. For machine learning to work, the algorithms need a lot of data to learn from — building a new cluster that can handle large amounts of incoming data is important. Security is also becoming more important as many companies are using AI and ML for tasks like fraud detection and data analysis.
Infrastructure and security
Nowadays, a lot of us are spending more and more time worrying about the increasingly harsh environment for IoT devices. In 2019, “device hygiene” has become much more challenging. As consumers, we too often assume our devices will be phishing-free and secure. But this year, we saw that the stories of data discrimination were increasing by the day — even as companies were making more effort to address these issues for their cloud services. There are also some ongoing efforts by governments to make it easier for citizens to obtain their own data. While some people might hope this might lead to an end to data ownership, it could help increase trust between ordinary consumers and big tech companies. Trust is an important part of encouraging future technological innovation. Of all these trends there is the increased expectation that tech companies assure us their products won’t fail us, especially when they are used in important situations, like healthcare or city infrastructure.
What’s in store for the future of data management?
Technology analyst Gartner’s Hype Cycle details the typical ups and downs for emerging technologies—and data and analytics is definitely at a hype peak right now. So how can you channel the momentum into real business value? With so many companies going all-in on an AI-first approach, adoption is often happening at unprecedented speed. Big data, one of today’s most prominent buzzwords, was first mentioned in 1996 ; today, about half of all companies in the top 1,000 use it, according to digital consultancy Zettagrid.
A lot of these services are using machine learning on top of previously held datasets that are large in scale.
In the attempt to anticipate, plan and react in a strategic and rapid way to a global crisis and its aftermath, data and analytics combined with artificial intelligence (AI) technology will be paramount.
But despite the growth, even five years into this trend, things aren’t all smooth sailing. As other RD trends show, not every new innovation succeeds. It’ll be up to technology innovators to make data and analytics platforms consumable and easy to use from a design perspective—and drives the adoption curve even higher. To help you navigate this big data boom,. we turned to Red Hat, IBM, Sophos and SAS for their thoughts on where things stand currently—and which areas could use improvement..
Trends that are in focus
1. AI Smarter, quicker, more accountable
To protect against bad decisions, responsible AI that facilitates model accountability is important. It helps in stronger human-machine cooperation and confidence throughout the enterprise for greater implementation and coordination of decisions.
2. Intelligence in Perceptions
When decisions require numerous analytical and mathematical methods, they must be automated or semi-automated, or they must be reported and audited, incorporating the application of decision making and modeling technologies.
3. Force of data and analytics collide
Integrate all data and analytics methods and capabilities into the analytics stack to turn the clash into a positive integration. Focus on individuals and systems to facilitate connectivity and cooperation, beyond instruments. Using ecosystems of data and analytics enabled by an augmented approach that has the capacity to provide coherent stacks.
In order to discover secret trends and partnerships, data and analytics executives need to analyze ways to integrate graph analytics into their analytics portfolios and implementations. Furthermore, imagine exploring how the AI and ML initiatives can be improved by graph algorithms and technologies.Sitemap