As companies of all sizes and in all industries learn to cope with a new “business-as-usual” approach that includes all-remote workforces and intense pressure to cut costs, faster innovation and data agility may appear out of immediate reach. Industry leaders who look to data analytics, artificial intelligence (AI), and Machine Learning are demonstrating that you can have it all, with the right planning and infrastructure.
Data-intensive applications—such as image processing, data analytics, and AI—depend on rapidly growing enterprise data. With that growth comes architectural considerations. As datasets grow, applications are required to be closer and closer to that data due to network latency. As more applications are added to the environment, they start to generate data faster than it’s possible to move this data elsewhere without great cost and interruption, making migration almost impossible. This is the data gravity paradox that creates lock in and introduces future business risk: the more you gather your data together, the harder it comes to change how you handle it.
So how are businesses unlocking the power of innovation without being tied to a single cloud? Successes (and challenges) exist across a variety of industries. Here, we’ll look at a few dynamic examples of how multi-cloud accelerates innovation, enhances data agility, and reduces costs.
Today’s media and entertainment landscape is increasingly composed of relatively small and specialised studios that meet the swelling content-production needs of the largest players, like Netflix and Hulu. To deliver the blockbuster movies and award-winning TV shows, these geographically dispersed studios require efficient collaboration on animation, color correction, special effects, and editing. Multi-cloud solutions enable these teams to work together on the same projects, access their preferred production tools from various public clouds, and streamline approvals without the delays associated with moving large media files from one site to another. A high-throughput, low-latency data lake eliminates concerns that lag will inhibit productivity. Additionally, a central storage solution that attaches to multiple clouds reduces the large egress fees often associated with taking enormous video files out of public clouds.
Beyond the need for collaboration, other factors drive the growth of data and of multi-cloud within media and entertainment. Cameras and viewing devices have greater resolution, meaning that file sizes are larger than ever, requiring greater bandwidth in dispersed data centers than what can be achieved on-premises. Streaming services rely on data analytics to programmatically understand content popularity, divine what new content should be created, and which content should be shelved. Many of the processes related to these workflows are increasingly utilising the public cloud due to the availability of complementary data sets and use-case-specific tools for handling different types of analytics.
Connected car and autonomous driving projects generate immense amounts of data from a variety of sensors. For example, Tesla’s autopilot utilises eight cameras, twelve ultrasonic sensors, and one radar to interpret the car’s surroundings and make decisions about its path and how to avoid potential obstacles. Researchers in this field are trying to accommodate the 100s of petabytes of video and still image-generated data that are used to retrain algorithms. These are still the early days for autonomous vehicles (AVs). When 20–50x more are on road, handling more variants in driving situations (manoeuvring around any city street, any parking garage, etc.), an even greater amount of deep learning will be required. By 2030, autonomous vehicles on the road will create a predicted 1 Zettabyte of data.
