Expect the cloud to play a key role as companies look to better leverage their data. That’s judging by presentations Amazon Web Services, Google Cloud, and Microsoft Azure gave at Tamr’s DataMasters Summit. From increased usage of cloud-native capabilities to adoption of machine learning to tying cloud migrations to business projects, here are the top three things we learned from our cloud partners.
Rethinking business fundamentals in a cloud-native world
The next evolution of cloud computing will lead to companies tapping into cloud-native capabilities to reshape key business functions, said Pallab Deb, Head of Partner Solutions & AI Partnerships at Google Cloud.
This marks an evolution from how organizations first used the cloud, he said. The cost savings generated by not having to setup and maintain servers on-premise initially attracted organizations to the cloud. But migrations weren’t carried out “in the most elegant way. [They] were almost always designed around the constraints that were prevalent the day those applications were written,” Deb said.
But these architecture constraints prevent companies from tapping into cloud-native services to scale compute and storage usage accordingly. Companies, especially consumer-focused ones where user experience is key, are now looking to better leverage cloud-native capabilities by re-writing applications.
“We just don’t take the stuff that we have, but we try to transform what we have” is the outlook organizations are starting to adopt, said Deb.
Other enterprises are looking beyond re-writing applications and are rethinking how they can use cloud-native capabilities to improve how they do business. For example, Deb noted that a cloud-native approach may make applying for and approving mortgages a less “excruciating process.”
Instead of having applicants fill out multiple documents that are then passed along to loan officers who put that information into a spreadsheet for underwriters, companies “are thinking about how they’re going to write mortgages in the future, where perhaps not a single document needs to get printed.”
“People are rethinking how to build [the process] from the ground up in a cloud native world. All [companies] are headed there, but some of them are faster than the others in regards to their maturity,” Deb said.
The cloud will spur machine learning usage
Using the cloud for machine learning is gaining interest at enterprises, said Shashi Raina, Partner Solution Architect at Amazon Web Services. Raina predicted that while “virtually every application will be infused with ML” a majority of the cloud provider’s customers are just starting to determine how to use the technology.
“While an incredible amount of progress has been made in organizations using ML and we are seeing a lot of traction, this is still very early for most of the organizations. We are still in the beginning of this journey,” he said.
The reason that initially attracted organizations to AWS — agility, elasticity, and cost savings — are the same factors that are contributing to enterprise interest in running machine learning workloads in the cloud, Raina said.
Cloud infrastructure provides immediate access to the compute and storage resources that machine learning workloads demand. And while companies could run servers on-premise, the “heavy lifting of managing infrastructure and data centers” tends to prove expensive. “If you look at how people end up moving to the cloud, almost always the conversation starter ends up being cost,” he said.
Companies typically over provisioned data centers to ensure that they had the capacity to handle large workloads. “Now they can provision the amount of resources that they actually need knowing they can instantly scale up or down with the needs of the business, which also reduces cost,” Raina said.
Using cloud migrations to get more value from data
Customers are using cloud migrations to better use their data for business initiatives, said Mike Flasko, Partner Director of Product Management at Microsoft. Even discussions that start as “a pure migration conversation” eventually turn to how the move is part of a greater business goal around using data.
“For a lot of customers, they look to the cloud as part of migration projects or broader ambitions with data, oftentimes combining data in new ways or leveraging data in new ways. And they often look into the cloud for that,” he said.
Cost and scalability have prevented companies from pursuing projects like data mastering and cleaning that allow them to get more value from their data, Flasko said. The cloud helps overcome these challenges by offering the ability to scale these resources up and down as needed. Paying only for the services an organization saves money and makes workloads around integrating, cleaning, and mastering data at scale technically and fiscally possible. For customers, this provides an opportunity to combine disparate data and obtain more holistic views of their data by mastering it as part of the migration process, said Flasko.
Solutions that answer questions like “how do you master data at scale, how do you clean data at scale, how do you offer elasticity” can help customers master their data as they migrate it.
Those solutions “fit really well with the ambitions people have for cloud becoming; providing an opportunity to work across data silos that they couldn’t before, either because of scale or cost or other challenges,” he said.