4-minute read

Quick summary: We explore the strategic shift from SAS to cloud-based analytics, emphasizing the benefits of modernization, cost efficiency, and enhanced flexibility, plus practical steps to ensure a seamless migration.

In the rapidly evolving landscape of data analytics, the ability to adapt to technological advances has become paramount for sustaining growth and staying competitive. Historically, SAS has been a cornerstone for organizations around the globe, offering powerful analytics capabilities to mine, alter, manage, and retrieve data from various sources and perform statistical analysis. However, as the digital world shifts towards more modern, cost-effective, and flexible solutions, many organizations are re-evaluating their traditional reliance on the platform.

This growing trend points to cloud migration as a strategic pathway for businesses looking to modernize their analytics processes. Transitioning from SAS to cloud-based platforms not only promises a reduction in overall costs, but also opens the door to embracing innovation with more scalable, accessible, and efficient data management solutions. In this article, we’ll explore how businesses can elevate their analytics capabilities by making the move to the cloud, leveraging the latest in cloud technology to foster a culture of data-driven decision-making and, ultimately, fuel their growth in the digital era.

The cloud advantage

The shift from SAS to cloud-based analytics offers multiple advantages that speak to the modern business’ needs for efficiency, innovation, and growth. Here’s a closer look at the key benefits driving this transition:

Cost and flexibility

Traditional reliance on SAS often comes with high costs and inflexibility due to the platform’s proprietary nature. Many businesses find themselves locked into long-term contracts with little room for adaptation.

In contrast, cloud platforms excel in overall cost efficiency and flexibility. The pay-as-you-go models and the ability to dynamically scale resources up or down as needed ensure that businesses only pay for what they use, significantly reducing overhead costs.

Innovation and integration

Cloud technologies are at the forefront of analytics, AI, and machine learning advancements. Migrating to the cloud provides businesses with easy access to the tools needed to stay ahead in their fields, leveraging cutting-edge technologies such as generative AI to uncover deeper insights and foster innovation.

Cloud platforms offer unparalleled integration capabilities with a broad ecosystem of services and tools. This connectivity enables more streamlined, innovative analytics processes that can easily evolve with the business’s changing needs.

Maintainability and talent availability

One of the more pressing issues with continuing SAS usage is the dwindling availability of professionals skilled in its specific programming environment. This can lead to challenges in maintainability and operational efficiency.

Transitioning to cloud-based analytics platforms allows businesses to tap into a much larger talent pool. Professionals proficient in widely used languages like Python are more readily available, making it easier for organizations to develop, maintain, and operate analytics processes effectively.

By embracing the cloud, businesses not only mitigate the challenges associated with traditional analytics solutions, but also position themselves to capitalize on the rapid pace of digital innovation.

The road to cloud migration

Transitioning analytics processes from SAS to cloud platforms necessitates thorough preparation and strategic execution. The following essential steps can contribute to a smooth transition:

Strategic assessment and planning

• Conduct an exhaustive assessment of existing SAS processes to pinpoint core logical requirements.
• Translate these requirements into cloud-compatible services and Python code with expert guidance, paving the way for not just a seamless transition, but also future process enhancements.

Cloud platform and tool selection

• Research the various cloud platforms (e.g. AWS, Azure) to identify the one that best fits your business’ specific needs.
• Decide on the most suitable tools, with a focus on Python, considering factors like scalability, cost, and available support.

Migration strategies and expert collaboration

• Outline the migration process, from evaluating SAS environments and planning the move to executing the actual data and application transfer.
• Advocate for collaboration with experts who bring a wealth of knowledge in SAS, cloud technologies, and Python to efficiently manage migration complexities.
• Incorporate generative AI tools to streamline the migration process.

By adhering to these considerations, businesses can set the stage for leveraging advanced technologies and methodologies for continued innovation and growth.

Charting a new course in analytics

The shift to cloud-based analytics platforms stands as a monumental leap towards modernization for businesses anchored in traditional SAS environments. This transition is not merely about maintaining pace with technological advancements; it embodies a strategic shift that unlocks cost savings, elevates operational flexibility, and broadens access to a diverse pool of talent skilled in contemporary tools like Python.

Businesses are viewing their journeys to the cloud as a gateway to innovation, efficiency, and a stronger competitive stance in the market. The promise of cloud migration extends beyond immediate benefits, offering a foundation for continuous improvement and adaptation in a rapidly evolving digital landscape.

Adam Cornille is Senior Director of Advanced Analytics at Logic20/20. He is a data science manager and practitioner with over a decade of field experience, and has trained in development, statistics, and management practices. Adam currently heads the development of data science solutions and strategies for improving business maturity in the application of data.