Data has become the lifeblood of any organization, but the sheer volume of it is making it more difficult to manage. In fact, Finances Online projected that the world would produce and consume 94 zettabytes of data in 2022.
At AWS re:Invent, Swami Sivasubramanian, vice president of database, analytics and machine learning at Amazon Web Services, presented a keynote devoted to the various shapes an organization’s infrastructure might take to optimize the value of its data. For some workloads and use cases, on-premises data centers are still appropriate, despite the fact that the move to multicloud continues at a steady pace. And in still other circumstances, edge computing can allow an organization to process data more quickly and effectively.
Sivasubramanian compared the harnessing of data by organizations to the way the human brain acquires and processes knowledge. “But unlike the human brain, there isn't one centralized repository to collect all our data, which often means it leads to data silos and inconsistencies across an organization,” he said.
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How to Develop a Future-Proof Data Strategy
Sivasubramanian said AWS has learned from working with its customers that there are three core elements to a sound data strategy. “First, you need a future-proof data foundation supported by core data services,” he said. “Second, you need solutions that weave a connective tissue across your entire organization. And third, you need the right tools and education to help you democratize your data.”
Sivasubramanian noted that the term “future-proof” is being widely used to mean very different things. “My definition of a future-proof foundation is clear: It means using the right services to build a foundation that you don’t need to be heavily rearchitecting or incur technical debt as your needs evolve,” he said. The volume and types of data will continue to change, and organizations must be prepared to evolve as well.
He listed four key elements he said should be included in a future-proof data foundation:
- Tools: “It should have access to the right tools for all workloads and any type of data so you can adapt to changing needs and opportunities.”
- Scale: “It should be able to keep up with the growing volume of data by performing at a really high scale.”
- Value: “It should remove the undifferentiated heavy lifting for your IT and data team so you can spend less time managing and preparing your data and more time getting value from it.”
- Security: “It should have the highest level of reliability and security to protect your data stores.”
RELATED: Find out more about how observability can make your data more resilient in the cloud.
How Expedia Is Extracting the Value of Data Through Machine Learning
Sivasubramanian was joined by Rathi Murthy, Expedia’s CTO and president of Expedia product and technology. No matter where an organization is handling the data it generates, Murthy said, data is the “key to drive our innovation and our long-term success.”
“Data is our competitive advantage,” Murthy said as she explained how Expedia has used automation to leverage the data it collects. “Earlier this year, we launched price tracking and predictions. This uses machine learning and our flight shopping data to map past trends and future predictions for the prices for your flight route.”
She offered another relatable example of how the travel site uses data analytics to enhance the customer experience. Expedia has attempted to remove some of the complexity involved in comparing hotel rooms by leveraging AI to gather information regarding room features, upgrades and amenities all together on one page so users can easily compare different hotel types and make more informed choices. “Every time a traveler interacts with us, we collect more data, our models become smarter and our responses become more personalized,” she said.
AWS Enhancements Will Make Machine Learning More Available
Sivasubramanian touted the latest capabilities added to Amazon SageMaker, AWS’ machine learning platform. The platform was the focus of multiple sessions at the event, and Sivasubramanian highlighted some of its key features: “It comes with built-in visualization tools, enabling you to analyze your data and explore model predictions on an interactive map using 3D-accelerated graphics.” He also noted that SageMaker “provides built-in, pre-trained neural nets to accelerate model building for many common use cases.”
Anna Berg Asberg, global vice president of research and development IT at AstraZeneca, joined the conversation to discuss the importance of democratizing data and machine learning for innovation.
“One of the most exciting advancements in the industry right now is that patients can choose from clinical trials to share the data with us from their own homes,” she said. AstraZeneca is able to collect data from a patient’s home on a daily or even continuous basis, and Asberg said the data is as reliable as data collected in clinical settings. It allows researchers to “collect data from underdeveloped regions and remote locations, moving us toward early diagnosis and disease prediction for all people, because our future depends on healthy people, healthy society and a healthy planet,” she said.
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