The search to maximize our own productivity is never-ending. We all want to be more efficient in our work and carve out more time with loved ones.
As you assess data and AI technology to automate processes and maximize efficiency, you may wonder if it’s truly possible for you or your organization to adopt a data and AI platform and make huge gains in human productivity, while also curbing costs and building trust in outputs.
The answer is yes. It’s possible.
The quick take
We engaged a third-party research firm, The Futurum Group, to conduct performance and productivity studies on our data and AI platform.
- The first study evaluated the performance of AI training models.
- Conducted over 1,500 tests across various datasets.
- Used the same compute resources for all tests.
- Findings: SAS® Viya® is 30 times faster than alternatives and 49 times faster than a commercial Spark-based platform.
- The second study evaluated the productivity of people completing data and AI tasks working on a real-life outcome.
- Findings: SAS Viya enables significant productivity gains for data engineers, data scientists, MLOps and business analysts.
As well, low-code/no-code capabilities and productivity for non-technical users in the data and AI life cycle offer huge gains. This is a big win for solving the talent gap.
“Testing showed that an end-to-end data and AI lifecycle can be achieved with more than four times greater productivity in SAS Viya than in competitive solutions,” said Russ Fellows, VP and analyst at The Futurum Group. “The ability to quickly begin working, together with SAS Viya’s productivity enables AI teams to rapidly produce business results and insights from their data.”
So, how did we do it? Here are six quick tips for maximizing human productivity while lowering costs and improving data and AI outputs.
Tip 1: Finetune data access and prep
Enable teams to discover data, analyze it and build an inventory with high-quality data that is accurate, complete, consistent and timely. This data must be trusted to make critical business decisions. Poor-quality data without proper data processing can lead to misguided strategies and costly mistakes. The platform must support robust processes for handling data throughout its life cycle – from acquisition and integration to cleansing, governance, storage and preparation for analysis.
The Futurum Group found data engineering tasks, like data management, on SAS Viya were:
- 16 times more productive versus the commercial platform alternative.
- 16 times more productive versus the non-commercial platform alternative.
Tip 2: Have a plan for AI ethics and governance
Data privacy, data sensitivities and compliance issues are always-on considerations – we must know the accuracy of the data going in, feel confident it’s free from bias and ensure decisions based on models are explainable. In other words, your strategy needs to be built on trust. Use a platform that automatically looks for and flags data such as age, race and gender. Embed data quality checks so you can make data privacy decisions like masking values within the data.
A data and AI platform should support transparency to gauge model accuracy and fairness. SAS Viya provides a feature called “model cards” where you readily see the at-a-glance health of a model – its intended use with critical factors to know if a model is a viable candidate for deployment.
Tip 3: Solve the talent gap
Talent gaps exist everywhere and at multiple levels, particularly in the scope and nuance of more technical roles. Imagine an organization where a variety of individuals and teams, both technical and non-technical, are all empowered to execute tasks like harvesting and analyzing data, or even building analytical models.
Why force your data and AI environment to always require coding, limiting the productivity of diverse teams? The Futurum study showed that non-technical users can complete 86 percent of data life cycle tasks using SAS Viya, compared to 56 percent in the comparative commercial environment and 47 percent in the non-commercial environment.
Tip 4: Curb cloud costs
In the cloud, time is money. Team efficiency isn’t just important, it’s mandatory.
"With Viya, you gain a competitive advantage," said Jay Upchurch, Executive Vice President and CIO at SAS. "Because your AI runs faster and more efficiently on Viya, your teams learn faster and are more productive, so you see results faster. Viya gives you agility and resiliency that empowers your organization to see opportunities before your competitors do, whether those opportunities involve approving customers for car loans, keeping trains safe or distributing merchandise from a retail distribution center."
The Futurum Group findings support Viya's performance with training AI models can lower your cost by more than 86 percent compared to alternatives.
Tip 5: Build a life cycle for continuous improvement
The world is always changing, and that means your data evolves, business objectives shift and models decay. Keep iterating and evaluating your models in production – monitor their performance, check in on data quality, and retrain/tune them on a regular basis. With a data and AI life cycle management system in place, your teams can quickly iterate and learn so the best models are in production. SAS Viya supports this.
Tip 6: Prep for innovative technology on the horizon
Be flexible and open to generative AI capabilities and tools, particularly ones that are easily accessible or naturally integrated into your current environment and existing workflows. Understand that you aren’t hamstrung by a lack of data or technical skills. Innovations like synthetic data and pre-built models are taking center stage with broad applicability.
According to Forbes, artificially generated datasets will become the preferred training ground for machine learning models. SAS recently acquired the principal software assets of Hazy, a pioneer in synthetic data technology, to equip customers with critical and timely synthetic data generation capabilities.
Kathy Lange, Research Director, AI Software at IDC, shared, “Synthetic data is a game-changer for companies implementing AI solutions, especially in sectors with strict privacy regulations like health care and finance. SAS’ acquisition highlights the growing requirement for synthetic data as an integral component of a modern AI toolkit, addressing data scarcity and privacy issues, and improving model accuracy while reducing biases.”
Results that matter in scaling human productivity
Hear Bryan Harris, Chief Technology Officer at SAS, share the results from The Futurum Group.
See SAS Viya in action
Watch Jared Peterson, Sr. Vice President, Platform Engineering, demo our end-to-end data and AI platform.
