How to get your multi-cloud data architecture strategy on track for long-term success

This post was originally published on this site

Digital transformation continues to accelerate at pace as does the modernisation of data infrastructure. Almost without exception every digital process is improved by the speed and scale at which it can use large volumes of data, in order to make critical decisions.

Gartner tells us that by 2022 “75 percent of all databases will be deployed or migrated to a cloud platform, with only 5 percent ever considered for repatriation to on-premise.” Unsurprisingly the migration of data to the cloud is one of the most talked about aspects of data infrastructure modernisation. Clouds are the new data centers, the internet is the new network, and SaaS is the new application stack.

The cloud presents a huge opportunity to businesses and it’s unlikely that any modern application won’t leverage cloud infrastructure in some way. Yet to take advantage of its full benefits you need to give careful thought to your strategy. Don’t default to basic tools and strategies – think longer term. You don’t want to find your mission-critical application wedded to one vendor amidst mounting cloud costs and your application stack stuck because it can’t scale.

Here are five critical strategies to get your multi-cloud data architecture strategy stable, secure, and on the right track for long-term success.

13 Jan 2021

Facing strong headwinds, IBM hopes to find new life in the cloud

1 Prioritize flexibility

Technology innovation is constantly moving forward so don’t think single cloud. Even though you might feel that you’re saving time. Internal applications teams, and the databases and tools they leverage for data-rich applications, need to support multiple clouds. Take a long-term view towards resiliency when you might need to leverage multiple clouds for scale or times of duress for critical applications. Your strategy needs to work across multiple clouds and while you should pick the application that suits your immediate needs. Keep flexibility in mind so that you’re able to pick another cloud further down the line.

2 Standards are key

The cloud now has very clear standards as defined by the Cloud Native Computing Foundation (CNCF). You should demand the same of your database. Most proprietary innovations are now becoming open source and standards across multiple cloud vendors. A perfect example of this is Kubernetes, which originated from Google over ten years ago. Stick to the standards, reduce custom development, and set yourself up for multi-cloud success.

3 Invest time for long-term gain

We have a lot to thank the cloud platform vendors including Amazon, Google, Microsoft, and others for. Using a good dose of innovation, they have paved the way thus far. While they have their proprietary offerings such as database services, orchestration frameworks, and monitoring platforms they are generally not equipped for scale and remain, in the majority, for mild to moderate use. If you’re running mission-critical, large scale, big data applications that require extremely low latency like real-time fraud, financial transactions, instant decisioning, and AI they may not fit the bill. This doesn’t mean that you shouldn’t still fully embrace the techniques of cloud simplicity and management that are exemplified by these general-purpose applications stacks.

Almost all best-in-class database solutions are now fully optimized for the cloud and have similar levels of automation and simplicity of the “built-in” cloud provider options. You may have to invest more time up front but the long-term gain and independence you’ll achieve will be fully justified.

4 Where does your data reside?

The location of your data is one of the most important factors in a multi-cloud deployment. If you’re a large business, then you will have large volumes of data. It’s also likely that you will have a global footprint with any variety of industry, country, or even county-level compliance and privacy requirements. You will quickly see by reading any cloud provider’s SLA or user agreement that they won’t — and can’t — assume liability for privacy compliance. Privacy and compliance of user information is still your responsibility when you move data to the cloud

5 When it comes to data architecture think at scale

The cloud promises elasticity and to make this a reality you must architect across the whole stack. The speed of digital transformation shows no signs of slowing down. When you’re planning go big. Whatever is currently on your mind scale it at least 10x, and probably 100x that level. Our experience in large organisations an annual doubling of transaction and data volumes barely covers it. It doesn’t take long for mission-critical, data-intensive applications to come up against scalability problems. So think at scale and select a solution that optimizes your performance and matches your revenue model enabling you to easily retain control of your cloud costs.

To be well placed to handle the digital transformation and performance demands of global business and take full advantage of the cloud you need to architect your data infrastructure. Following the principles laid out above will provide you with a solid foundation that gives you maximum flexibility, stability, and the ability to innovate even faster.