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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.
Organisations that use legacy systems like SAS9 and SAS on Mainframe are faced with a situation where they need to navigate a path to modernisation, to ensure existing capabilities stay up-to-date. 

Our goal is to give you a short and pragmatic overview of the relative advantages, limitations, and contextual applicability of each solution.

Legacy Systems: SAS9 and SAS Mainframe

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.

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SAS9 and SAS Mainframes and their extensive statistical procedures offer vast analytical capabilities, including: 

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.

Advantages

  1. Proven Reliability
    Stability: SAS systems are known for their dependability in handling large datasets, an essential trait for industries where data integrity is crucial. 
    Performance: Their performance in executing complex statistical analyses has been proven over time, making them a trusted choice for sophisticated analytics tasks.
  2. Advanced Analytics
    Rich Functionality: SAS9 offers a comprehensive suite of analytical techniques that are difficult to match. Its tools for high-end statistical analysis are especially valuable for specialised needs.
  3. Enterprise-Grade Security
    Compliance: The security features of SAS9 are robust, making it suitable for industries with stringent regulatory requirements, such as finance and healthcare.
    Data Protection: High levels of data protection are integrated, ensuring that sensitive information remains secure.

 

 

 

 

Disadvantages

  1. Cost
    High Ownership Costs: The total cost of ownership for SAS9 and mainframe systems can be significant, including licensing, hardware, and ongoing maintenance expenses. A large portion of this cost comes from preparing for peak loads.
  2. Complexity and Learning Curve
    Specialised Skills Required: Getting the most out of SAS requires substantial training, which can be a barrier to its wider adoption within organisations.
  3. Scalability
    Scaling Challenges: Mainframe systems may struggle with the scalability demands of modern data analytics, particularly real-time or big data applications, due to their older architecture.
  4. Flexibility
    Less Agile: Compared to modular and cloud-native solutions, SAS mainframe systems are less adaptable, making them slower to respond to changing business needs.
  5. Support & Innovation
    Legacy Platform: SAS Institute is slowly winding down support for the SAS9 platform, with specific Mainframe solutions being out of support very soon. There are no new innovations taking place, which is rapidly increasing the gap between SAS9 and modern solutions.

Modern Data and Analytics Platforms

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.

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Advantages

  1. Scalability
    Elastic Compute: The ability to scale resources on the fly, accommodating varying data loads without significant capital expenditures.
  2. Cost-Efficiency
    Flexible Pricing Models: Pay-as-you-go pricing models reduce initial investments and operational costs, making advanced analytics accessible to organisations of all sizes.  
  3. Agility and Speed
    Rapid Deployment: Modern platforms are designed for quick deployment, allowing organisations to adapt swiftly to new business requirements.  
  4. AI and ML Integration
    Advanced Analytics: Built-in support for machine learning and deep learning workflows, often leveraging specialised hardware such as GPUs for accelerated processing.  
  5. Ongoing Innovation & Support
    Future proof platforms: Many cloud environments are constantly innovating, delivering new features and capabilities to their users, as an extension of an existing platform and often delivered on demand. This is coupled with a far longer support window from their vendors.

Disadvantages

  1. Security and Privacy
    Data Concerns: Cloud environments may pose potential data security and privacy concerns, particularly in multi-tenant architectures
  2. Skill Requirements
    New Expertise Needed: Transitioning to modern platforms may require new skills and training for managing cloud infrastructures and leveraging contemporary programming tools.  
  3. Initial Migration
    Complex Migration Processes: Migrating from legacy systems to modern platforms involves substantial planning and resources and can be complex and time-consuming. 

Considerations and Evaluation

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Consideration 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.

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Consideration 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

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Consideration 3: Usability & Flexability

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.

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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.

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Consideration 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.  

What Should You Do?

Evaluating SAS in the modern analytics market requires a nuanced approach, balancing the strengths and limitations of legacy systems against the advancements offered by contemporary platforms. SAS9 and SAS Mainframes have demonstrated reliability and deep analytical capabilities, which are particularly valuable in sectors with stringent data demands and regulatory compliance needs.  

However, their high costs and scalability challenges may deter organisations looking for agility and cost-efficiency. Modern data and analytics platforms offer compelling advantages, particularly scalability, cost-efficiency, usability, and integration capabilities.  

Cloud-native environments provide flexible resources and advanced analytics, making them highly suitable for real-time data processing and AI-driven insights. Nevertheless, potential concerns around security and the complexity of migrating from legacy systems require careful consideration.  

Ultimately, the choice between legacy systems like SAS9 and modern analytics platforms hinges on:

  • Organisational needs,
  • Budget constraints, and
  • Future scalability plans.

A hybrid approach, leveraging the reliability of SAS systems while adopting the agility of modern platforms, may offer a balanced path forward, combining the best of both worlds.

At Analytium we regularly get involved in modernisation exercises and have seen a wide range of SAS installations. Moreover, we have implemented a variety of solutions, ranging from simple updates, to complete migrations away from SAS altogether.

Regardless, with the development of SAS9 halted and support minimised, it is only a matter of time before SAS9 system will have to be modernised.

If you are considering when to start, we recommend:

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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.

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Attending the SAS Platform Modernisation webinar

In  this webinar, we will specifically focus on SAS Modernisation Paths, exploring the various options, opportunities, and challenges involved.

Vasilij Nevlev
Post by Vasilij Nevlev
May 16, 2024
Vasilij is the founder of Analytium. His career is characterised by a data driven mindset, which he now uses to achieve great results for others.