Transform Your Data with Multicloud and AI

Make your data more accessible and valuable with the right cloud data management solution.

The cloud data management market has seen a significant amount of development toward multicloud and artificial intelligence (AI) solutions. Yet, sifting through the clutter to find the most valuable solution remains a challenge for many companies.

To help overcome that challenge, there are several considerations businesses should keep in mind when adding new clouds to their data management infrastructure. It is also important to seek out the right capabilities from providers who offer AI in their data management solutions.

It’s a hybrid, multicloud world

Offering deployment options on other clouds helps businesses develop applications with their technology of choice, even when they have standardized on a different provider. While this mitigates vendor lock-in, it adds a layer of complexity. Because so many providers are moving toward multicloud, your business needs to form a strategy around what cloud solution to integrate into its stack of solutions. Consider the following while shopping for the ideal multicloud data management solution:

Hybrid integration and enterprise performance

Organizations need to expand their thinking beyond cloud when looking for a solution. Optimizing analytics and application development requires not only choosing the best technology, but choosing the best deployment as well.

A data management solution that will operate seamlessly across on-premise, private cloud, and public cloud environments is, therefore, necessary. One way to promote this seamless integration is to choose a family of data management products built upon the same codebase, no matter where they are deployed.

Security and performance on cloud must also approach the level of its on-premise counterpart to help ensure high availability no matter where the technology sits. A good example is Db2 on Cloud, which not only has an on-premise and hosted option, but can deploy on both IBM Cloud and AWS as well.

Data transfer fees

One of the primary reasons many businesses use the cloud is cost efficiency. Many cloud providers tout their low-cost options, but businesses must investigate further to uncover all of the expenses incurred. Data transfer fees—whether cloud to on-premise or cloud to cloud—can quickly add up for enterprises as they conduct analytics. It’s best to look for data management options available on clouds that don’t charge these fees, such as IBM Cloud.

Migration support

Moving your data to the cloud for the first time can be a cumbersome process if a well-thought-out strategy is not put into place. Working with the right cloud vendor is necessary to make sure the migration is simple and fast.

Two primary types of migration should be considered: on-prem to cloud and cross-multicloud migrations. On-prem to cloud migrations must prioritize security and uptime. Failing at either could prove disastrous for a company’s bottom line due to lost productivity. Services such as IBM Lift CLI, with zero downtime and encryption of data in motion, set the standard.

Migrations between clouds in a multicloud environment must also be considered. If this data is not migrated quickly and easily, the benefits of having multiple integrated clouds can quickly break down.

Infuse your data with AI

Alongside multicloud adoption, modernizing information architecture for artificial intelligence has become a business imperative. Cloud data management solutions should be infused with AI to help businesses predict and shape outcomes by improving query performance and simplifying AI application development. In other words, they should be powered by and built for AI.

Solutions powered by AI will improve query speeds with machine-learning-based optimization of the routes queries take to data. They will also improve precision with confidence-based querying, which returns results based on predicted accuracy as determined by historical data.

Solutions built for AI fuel application development for AI initiatives by making it easier for developers and data scientists to perform their jobs. This includes support for popular languages and frameworks like Go, Ruby, Python, PHP, Java, Node.js, Sequelize, IBM Watson Studio, and Jupyter notebooks. The ability to perform complex modeling and visualization should also be available.

A good example of a data management solution that combines “powered by” and “built for” AI functionality is IBM Db2 11.5. Hailed as the AI database, the features present in Db2 11.5 extend to the entire family of Db2 offerings, including cloud options and the data warehouse. So, businesses that are ready to build predictive models and improve various business processes can train and run machine-learning models directly in the Db2 Warehouse on Cloud engine with no data movement or new skills required.