How to Navigate the Complexities of Multi-Cloud Data Management
As cloud adoption grows, a “one cloud fits all” approach no longer works. Organizations are using a variety of IaaS, PaaS, and SaaS products to deliver applications for innovative operations. They are also likely running private cloud or/and on-premises infrastructures. According to the Flexera 2022 State of the Cloud Report, 89% of enterprises have a multi-cloud strategy. Seventy-nine percent have a hybrid cloud strategy. There are many benefits to running your infrastructure in a multi-cloud environment, including:
- Ensuring uptime
- Shifting workloads
- Gaining efficiency
- Avoiding vendor lock-in
The multi-cloud world has changed how we see cloud storage and compute power. It also enables many unique services. A multi-cloud environment helps you achieve more by letting you move workloads between clouds to get the best of each cloud service without disruption.
Multi-cloud strategies allow for adopting the best features among the major cloud platforms. However, organizations face challenges in realizing the full potential of cloud analytics in a multi-cloud world.
Challenges in a highly distributed multi-cloud world
Moving to the cloud doesn’t solve the data management challenges. It increases them. Data volumes and new data types crop up all the time. This means your company’s data is spread across on-premises systems as well as public and private clouds. This creates more data silos. This makes it difficult to connect, transform, manage and integrate all that diverse data to make it useful. Organizations working with multiple cloud service providers face common challenges around accessibility and integration. Other challenges include visibility, security and data governance.
- Integration – As applications are spread across multiple clouds, the new architecture needs to make sure the underlying data moves across the clouds, regardless of where it resides (on-premises, public or private cloud). This requires cloud data integration with cloud-native ELT and ETL. Data storage mechanisms and the database, as well as data lake choices, can play a key role in ensuring portability of the data.
- Security – In a multi-cloud environment, enterprises find it tough to monitor and secure all the different systems. This is because there is no single point of control to monitor security and compliance. Foundational architectural services are created for data backup, discovery, protection, replication and restoration across multiple cloud environments. So, capabilities should also be developed for cloning data, masking certain elements and securely storing replicated data. This can become helpful in creating an agile organization that delivers more applications and features.
- Accessibility – As data becomes fragmented, it creates data silos that are harder to manage. Architectures must be created that can ensure data is available, regardless of where it resides. It must also understand where your data is coming from and how it gets transformed at every step of the data pipeline.
- Visibility – As organizations start deploying multi-cloud, they lose visibility across divisions or regions. Without a central console, organizations struggle to gain information about cost, configuration, usage and performance.
- Governance/Compliance – Scaled, shared and automated IT platforms such as the cloud can hide the geographic location of data from the customer and the service provider. This can cause regulatory violations.
In larger organizations, there are complex requirements for handling data. For example, there will sometimes be a need to deliver different subsets of the customer data to different teams:
- In different applications
- In different formats
- All with different services level requirements or specifications
Imagine if the data of these different teams are in different clouds. You can see how this can get complicated. In the new multi-cloud world, it is important for organizations to include comprehensive data management in their architectures.
Avoid the data management mistakes of the past
- Manual approaches, such as hand coding, are expensive to develop. They are also difficult to maintain. While hand coding seems like a good idea, it has many drawbacks. Hand coding needs skilled developers and lacks reusability. Changes in technology, platform or processing engine need reengineering and recoding. These are time-consuming processes that slow down innovation.
- Using many point products that are not integrated increases cost and complexity. It can take an organization up to 10 different point products to achieve end-to-end cloud data management. Piecing together these products means that organizations must rely on do-it-yourself integration. They also must rely on changing roadmaps, project overruns and inconsistent data governance and quality.
- Limited solutions from platform-as-a-service (PaaS) or infrastructure-as-a-service (IaaS) vendors combine many of these downsides. They offer basic data integration and ingestion, are reliant on hand-coded development and provide capabilities that extend to their own platforms.
How Informatica can empower you to meet the demands of data-driven transformation in hybrid & multi-cloud ecosystems
Informatica’s Intelligent Data Management Cloud (IDMC) was designed to help businesses innovate with their data on any platform, any cloud, multi-cloud and multi-hybrid. This complete, AI-powered platform transforms your business for a digital world. IDMC is the industry’s first and only cloud focused on data management. A true multi-cloud environment should provide a way to seamlessly manage, share and collaborate with data. A multi-cloud data management platform can help organizations realize the benefits of working with multiple clouds without the risk.
Multi-cloud data management with IDMC
IDMC is designed to help businesses handle fragmented data and innovate with their data on any platform, any cloud, multi-cloud and multi-hybrid. IDMC has over 200 cloud services. This allows you to catalog, ingest and integrate. It also allows you to prep, cleanse, master and share all of your data, wherever it is. You can also process it in any way you want, ensure it is trusted and deliver key insights.
AI assistance for multi-cloud data management
Informatica’s IDMC has all these services on a microservices-based, API-driven platform that’s both elastic and serverless. CLAIRE, our AI technology, is in every service. We can bring data from any source, of any type, to serve any user anywhere. And we can help anyone, whether he or she is a business or technical user.
Watch this video to learn how Informatica allows you to access both reliable data and real-time analytics, across any cloud, anywhere, and at any scale.