Cloud Power
The keys to an effective cloud modernization plan
Congratulations: you’ve moved to the cloud. What next?
To stay competitive, companies all over the world have migrated to the cloud, enjoying increased speed and agility, less guesswork around infrastructure capacity needs, and the benefits of trading capital expenses for variable expenses. But how can they further reduce costs, gain efficiencies, and make the most of their existing investments? And what are the additional benefits to product roadmap acceleration that come with rapid digitization?
The answer is modernization.
Hold on to your “why”
For an effective modernization process, Potloff recommends that companies not lose sight of their “why.”
“Start with the ‘why,’ then break down your suite of products and applications that help solve for that why,” Potloff says. Since systems at large companies can have so much technical debt—and this technical debt can be worked on for years—he recommends company leaders instead focus on working backwards from the customer. Here’s a hypothetical example: A company wants to increase their global direct sales by 30%. By working backwards from customer needs, it has to narrow its focus on things like reducing cycle time, increasing deal flow, improving order inventory, or optimizing pricing. “Then, you can map those goals to specific applications and capabilities that you need to improve,” says Potloff.
To help define “why,” he recommends enterprises ask themselves these questions:
How Liberty Mutual modernized, reduced costs, and embraced speed with the cloud
—Rahul Pathak, vice president of analytics, AWS
We’re just scratching the surface of what customers can do once they modernize their data infrastructure and move to the cloud.
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Stay nimble—with limits
In order to unlock an organization’s speed and make it more competitive when modernizing, Potloff recommends setting a “nimbleness metric” for teams after migration. The nimbleness metric is set by looking at the current high watermarks an organization has for speed across multiple functions, like how long it takes them to deploy code or refresh their databases.
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A myriad of opportunities present themselves once companies modernize their infrastructure and move to the cloud, says Rahul Pathak, vice president of analytics at AWS. Pathak’s team focuses on helping businesses solve big data problems “at petabyte-scale and beyond,” he says, and managing such vast real-time data can offer especially intriguing opportunities.
“AWS provides a number of services, such as Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka, that make it easy and cost-effective for customers to work with massive volumes of real-time data for advanced analytics, machine learning, edge computing, Internet of things, and more,” he says.
Pathak uses the example of car manufacturers, which can use Amazon Kinesis to stream real-time data from connected vehicles into a cloud data lake (a central repository of their structured and unstructured data). “These companies can then build machine learning models using Amazon SageMaker, and deploy those models back to their cars to enable real-time, low-latency predictions in the vehicle itself. Then, those predictions and the driver inputs can be fed back to their lake to refine their models.”
Embrace the possibilities of data
As a result of their modernization efforts with AWS, Liberty Mutual was able to reduce computing costs per million transactions to $60, remove the burden of infrastructure maintenance from staff, reduce onboarding time for engineers, and decrease application build time from one year to three months.
Not only is cloud modernization the next logical step after migration,
the pandemic has increased many organizations’ appetite for it.
Philip Potloff, head of enterprise strategy at Amazon Web
Services (AWS)—who leads a team of CIOs who have guided
successful cloud migrations at some of the world’s largest
companies, including Airbus, Coca-Cola, and McDonald’s
—meets with more than 1,500 customers a year and
helps them in the early stages of their cloud
strategy. “Especially during the past year and a half,
companies are trying to not only just survive, but
accelerate their transformation and thrive in a new era of
constant change,” he says.
Is your goal to rehost in the cloud—essentially, to take a copy of what you have and run it in a cloud environment? This is often a first step in a company’s cloud strategy.
Is your goal more oriented toward replatforming? Are you going to move from a commercial application container or commercial database to an open source one?
Is your goal to rearchitect (or refactor)? Rearchitecting involves taking a monolithic application and breaking it down into smaller pieces that run separately. “You can scale the pieces separately,” Potloff says. “You can observe them separately, and you can iterate on them separately, which is usually quicker because you don’t have to do a very large integration of all this huge code base every time you want to make a change, even if it’s a trivial one.”
“Organizations can give their teams the latitude to modernize using the
products and services that they want to use, but tell them that they can’t go
above that high watermark,” says Potloff. “So if they want to add new
functionality or use a new software package, they need to consider if it
increases their cycle time, and if so, find other ways (such as
leveraging scalable, resilient cloud services) to reduce that overall
cycle time. One of the long-term goals of modernization
should be to adopt services and methods that make
systems faster and more reliable to deploy often—in
other words, trending toward zero.”
These enterprises can also take advantage of offerings such as AWS Snowball Edge or AWS Outposts to pre-aggregate data there before sending it back to the cloud, he says. “We’re just scratching the surface of what customers can do once they modernize their data infrastructure and move to the cloud.”
As organizations begin to accelerate their transformations, making proper use of their data will only gain importance. So, too, will modernizing their application architecture: IDC predicts that by 2025, nearly two-thirds of enterprises will actually be software producers. These enterprises will not only deploy code daily, but over 90% of their new apps will be cloud-native, and 80% of their application components will be externally sourced.
“Modernization means taking advantage of the cloud architecture and services to make applications more resilient in operation, more transparent in cost, and quicker to deploy,” says Potloff. “There’s just this incredibly long tail of things that you can do.”
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How cloud migration prevents your company from losing resources and agility
It’s time to move to the cloud: Strategies for staying ahead during rapid digitization
How to unlock the value of data with cloud modernization
Explore more
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Cloud Power
How cloud migration prevents your company from losing resources and agility
It’s time to move to the cloud: Strategies for staying ahead during rapid digitization
Explore more
—Sandy Carter,
vice president, Worldwide Public Sector Partners and Programs, AWS
Therefore, machine learning has become a necessary component of advanced data analytics systems. Machine learning systems evaluate data, assess the quality, predict missing inputs, and provide recommendations.
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”
vice president of analytics, AWS
We’re just scratching the surface of what customers can do once they modernize their data infrastructure and move to the cloud.
“
”
—Rahul Pathak,
Liberty Mutual used AWS to move to a serverless architecture
starting in 2015. The company trusted AWS to handle
infrastructure management tasks like capacity provisioning and
patching; as a result of their ongoing transformation journey with AWS,
they reduced their operational burden, realized substantial cost savings, and
became much faster in building event-driven systems—ultimately allowing
more time to focus on the customer. Potloff notes that, during their work with
AWS, Liberty Mutual was building a product that had a delivery timeline of
approximately six months but delivered it in 12 weeks. “What did they do with that extra
time?” says Potloff. “They asked: What other features would be useful and add value to the
customer? They spent the next period of additional development cycles focused solely on improving the quality of the product.”
