Cloud analytics migration: how to exceed expectations

A cloud analytics migration project is a heavy lift for enterprises that dive in without adequate preparation. If expectations around the cost and speed of deployment are unrealistically high, milestones are missed, and doubt over potential benefits soon takes root.

But this scenario is avoidable. The right tools and technologies can keep a project on track, avoiding any gap between expected and realized benefits. A modern data and artificial intelligence (AI) platform running on scalable processors can handle diverse analytics workloads and speed data retrieval, delivering deeper insights to empower strategic decision-making.

Clearing business strategy hurdles

Choosing the right technologies to meet an organization’s unique AI goals is usually not straightforward. Business objectives must be articulated and matched with appropriate tools, methodologies, and processes. “Traditional systems often can’t support the demands of real-time processing and AI workloads,” notes Michael Morris, Vice President, Cloud, CloudOps, and Infrastructure, at SAS.

They are often unable to handle large, diverse data sets from multiple sources. Another issue is ensuring data quality through cleansing processes to remove errors and standardize formats. Staffing teams with skilled data scientists and AI specialists is difficult, given the severe global shortage of talent.

The challenges don’t end once those requirements are met, as the project teams need to secure executive buy-in, which may be hindered by resistance to new technologies. “Successful migrations require alignment between IT and finance departments, as well as broader business stakeholders, to ensure that the migration delivers value,” notes Bruno Domingues, CTO for Intel’s financial services industry practice.   

Concerns around security and regulatory compliance are also critical hurdles – but modern cloud providers offer advanced security features and compliance frameworks that can mitigate these concerns.

Mitigating infrastructure challenges

Organizations that rely on legacy systems face a host of potential stumbling blocks when they attempt to integrate their on-premises infrastructure with cloud solutions. “These systems are deeply embedded in critical operations, making data migration to the cloud complex and risky,” says Domingues.

Controlling public cloud costs can also be problematic due to lack of visibility into cloud usage patterns, inadequate governance and cost management policies, the complexity of cloud pricing models, and insufficient monitoring of resource use. “Robust cloud cost management tools and practices that foster collaboration between IT, finance, and business units can help ensure alignment and effective optimization of cloud investments,” notes Morris.

Software limitations are another concern, especially when it comes to scaling AI and data-intensive workloads. “A cloud-first approach ensures better data security, compliance with regulations, and scalability for AI-driven innovation,” says Domingues.

More impactful cloud-first strategies

Intel and SAS have forged a partnership that provides organizations with high-performance processors and advanced software to leverage the latest advancements in cloud, AI, and data analytics technologies. Intel’s cloud-optimized hardware accelerates AI workloads, while SAS provides scalable, AI-driven solutions. Their collaboration enables real-time delivery of insights for risk management, fraud detection, and customer personalization. Together, they help organizations meet data security and infrastructure scalability challenges, ensure compliance, maintain agility, innovate faster, and maintain a strong competitive position in a rapidly evolving market.

Check out this webinar to get the most from your cloud analytics migration.