What is driving the evolving change in data analytics and increasing its pace?
- Data volume,
- The growing demand for real-time insights and
- The need for cost-effective, scalable solutions.
Our goal is to give you a short and pragmatic overview of the relative advantages, limitations, and contextual applicability of each solution.
SAS has been an established and respected name in the analytics market for decades. The platform is renowned for its robust statistical capabilities, with SAS9 widely used in industries requiring high-level data analysis, particularly finance, healthcare, and government. Mainframes running SAS software have historically played a pivotal role in large-scale data processing, bringing reliability and performance to mission-critical applications.
SAS9 and SAS Mainframes and their extensive statistical procedures offer vast analytical capabilities, including:
- Predictive modelling,
- Econometrics, and
- Data mining.
These are employed for many highly specialised applications, like risk modelling and clinical trials. As a platform, SAS provides a unified suite of tools for data access, management and reporting.
This integration reduces dependence on third-party software, streamlining the analytical workflow.
When we think about modern Data & Analytics platforms, cloud based systems with their incredible flexibility quickly come to mind. Modern platforms like AWS, Google Cloud and Azure provide flexible, scalable resources tailored to current and future data workloads. They incorporate the ability to scale computing resources based on demand, ensuring cost-efficiency and performance.
Thanks to their modern architecture, they are also set up to deliver (near) real-time insights and integrate to levels impossible with SAS9. Solutions like Snowflake, Databricks, and Apache Spark excel in real-time data processing, enabling organisations to derive near-instantaneous analytics. This is helped by the highly flexible integration of various data sources, data lakes, and third-party tools, facilitating a seamless data flow.
Finally, these platforms are designed for ease of use, with user-friendly interfaces and broad compatibility with popular programming languages like Python and R, making sophisticated analytics accessible to non-specialists.
1. Cost & Efficiency
SAS9 and SAS Mainframe systems typically involve higher upfront and ongoing costs. These systems require significant licensing, hardware, and maintenance investments, making them less cost-effective than modern platforms.
In contrast, cloud-native solutions operate on flexible pricing models, enabling organisations to pay only for the resources they use. This pay-as-you-go approach reduces initial capital expenditure and provides a cost-efficient scaling mechanism.
2. Performance & Scalability
While SAS9 systems are known for their performance and reliability, particularly with large and complex datasets, their scalability can be limited by older architecture. Conversely, modern data platforms offer superior scalability through elastic cloud environments.
These platforms can handle massive, varied data loads and support real-time analytics, allowing businesses to gain insights more quickly
3. Usability & Flexibility
Modern platforms score higher in terms of usability and flexibility. They feature user-friendly interfaces, making sophisticated analytics accessible to a broader range of users, including those without deep technical expertise. Their modular design and integration capabilities with various data sources further enhance flexibility, allowing quicker adaptation to changing business needs.
Consideration 4: Advanced Capabilities
SAS9 provides rich analytical functionality and advanced statistical procedures that are prized in specific industries.
However, modern platforms are closing this gap rapidly. With integrated support for machine learning (ML) and artificial intelligence (AI), platforms like Databricks and Apache Spark offer powerful analytics, often with the additional benefit of accelerated processing through GPUs, making them suitable for contemporary data science needs.
5. Security & Compliance
Security is a crucial factor, particularly for industries like finance and healthcare. SAS systems have extensive security features and compliance with strict regulatory requirements.
Cloud providers have made significant strides in securing their environments, offering robust data protection measures and compliance certifications. Organisations considering modern platforms should evaluate these vendors' specific security offerings and compliance assurances.
The Business Case for SAS Modernisation blog
With data landscapes constantly evolving, staying on top of the most efficient and powerful analytics platforms is not merely advantageous but extremely necessary.
Watching the SAS Platform Modernisation Webinar On-Demand
In this webinar, we specifically focus on SAS Modernisation Paths, exploring the various options, opportunities, and challenges involved.
