In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.
The rise of cloud has allowed data warehouses to provide new capabilities such as cost-effective data storage at petabyte scale, highly scalable compute and storage, pay-as-you-go pricing and fully managed service delivery. Companies are shifting their investments to cloud software and reducing their spend on legacy infrastructure. In 2021, cloud databases accounted for 85%1 of the market growth in databases. These developments have accelerated the adoption of hybrid-cloud data warehousing; industry analysts estimate that almost 50%2 of enterprise data has been moved to the cloud.
What is holding back the other 50% of datasets on-premises? Based on our experience speaking with CTOs and IT leaders in large enterprises, we have identified the most common misconceptions about cloud data warehouses that cause companies to hesitate to move to the cloud.
Misconception 1: Cloud data warehouses are more expensive
When considering moving data warehouses from on-premises to the cloud, companies often get sticker shock at the total cost of ownership. However, a more detailed analysis is needed to make an informed decision. Traditional on-premises warehouses require a significant initial capital investment and ongoing support fees, as well as additional expenses for managing the enterprise infrastructure. In contrast, cloud data warehouses may have a higher annual subscription fee, but they incorporate the upfront investment and additional ongoing overhead. Cloud warehouses also provide customers with elastic scalability, cheaper storage, savings on maintenance and upgrade costs, and cost transparency, which allows customers to have greater control over their warehousing costs. Industry analysts estimate that organizations that implement best practices around cloud cost controls and cloud migration see an average savings of 21%3 when using a public cloud and a 13x5 revenue growth rate for adopters of hybrid-cloud through end-to-end reinvention.
Misconception 2: Cloud data warehouses do not provide the same level of security and compliance as on-premises warehouses
Companies in highly regulated industries such as finance, insurance, transportation and manufacturing have a complex set of compliance requirements for their data, often leading to an additional layer of complexity when it comes to migrating data to the cloud. In addition, companies have complex data security requirements. However, over the past decade, a vast array of compliance and security standards, such as SOC2, PCI, HIPAA, and GDPR, have been introduced, and met by cloud providers. The rise of sovereign clouds and industry specific clouds are addressing the concerns of governmental and industry specific regulatory requirements. In addition, warehouse providers take on the responsibility of patching and securing the cloud data warehouse, to ensure that business users stay compliant with the regulations as they evolve.
Misconception 3: All data warehouse migrations are the same, irrespective of vendors
While migrating to the cloud, CTOs often feel the need to revamp and “modernize” their entire technology stack – including moving to a new cloud data warehouse vendor. However, a successful migration usually requires multiple rounds of data replication, query optimization, application re-architecture and retraining of DBAs and architects.
To mitigate these complexities, organizations should evaluate whether a hybrid-cloud version of their existing data warehouse vendor can satisfy their use cases, before considering a move to a different platform. This approach has several benefits, such as streamlined migration of data from on-premises to the cloud, reduced query tuning requirements and continuity in SRE tooling, automations, and personnel. It also enables organizations to create a decentralized hybrid-cloud data architecture where workloads can be distributed across on-prem and cloud.
Misconception 4: Migration to cloud data warehouses needs to be 0% or 100%
Companies undergoing cloud migrations often feel pressure to migrate everything to the cloud to justify the investment of the migration. However, different workloads may be better suited for different deployment environments. With a hybrid-cloud approach to data management, companies can choose where to run specific workloads, while maintaining control over costs and workload management. It allows companies to take advantage of the benefits of the cloud, such as scale and elasticity, while also retaining the control and security of sensitive workloads in-house. For example, Marriott International built a decentralized hybrid-cloud data architecture while migrating from their legacy analytics appliances, and saw a nearly 90% increase in performance. This enabled data-driven analytics at scale across the organization4.
Misconception 5: Cloud data warehouses reduce control over your deployment
Some DBAs believe that cloud data warehouses lack the control and flexibility of on-prem data warehouses, making it harder to respond to security threats, performance issues or disasters. In reality, cloud data warehouses have evolved to provide the same control maturity as on-prem warehouses. Cloud warehouses also provide a host of additional capabilities such as failover to different data centers, automated backup and restore, high availability, and advanced security and alerting measures. Organizations looking to increase adoption of ML are turning to cloud data warehouses that support new, open data formats to catalog, ingest, and query unstructured data types. This functionality provides access to data by storing it in an open format, increasing flexibility for data exploration and ML modeling used by data scientists, facilitating governed data use of unstructured data, improving collaboration, and reducing data silos with simplified data lake integration.
Additionally, some DBAs worry that moving to the cloud reduces the need for their expertise and skillset. However, in reality, cloud data warehouses only automate the operational management of data warehousing such as scaling, reliability and backups, freeing DBAs to work on high value tasks such as warehouse design, performance tuning and ecosystem integrations.
By addressing these five misconceptions of cloud data warehouses and understanding the nuances, advantages, trade-offs and total cost ownership of both delivery models, organizations can make more informed decisions about their hybrid-cloud data warehousing strategy and unlock the value of all their data.
Getting started with a cloud data warehouse
At IBM we believe in making analytics secure, collaborative and price-performant across all deployments, whether running in the cloud, hybrid, or on-premises. For those considering a hybrid or cloud-first strategy, our data warehousing SaaS offerings including IBM Db2 Warehouse and Netezza Performance Server, are available across AWS, Microsoft Azure, and IBM Cloud and are designed to provide customers with the availability, elastic scaling, governance, and security required for SLA-backed, mission critical analytics.
When it comes to moving workloads to the cloud, IBM’s Expert Labs migration services ensure 100% workload compatibility between on-premises workloads and SaaS solutions.
No matter where you are in your journey to cloud, our experts are here to help customize the right approach to fit your needs. See how you can get started with your analytics journey to hybrid cloud by contacting an IBM database expert today.