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Resiliency, the Edge, and the Future of AI: A Conversation with SAS CTO Bryan Harris
What is the future of analytics and AI? And how can organizations thrive in an era of disruption? We asked Bryan Harris, Executive Vice President and Chief Technology Officer of analytics software company SAS, for his perspective.
Q: What is your advice to technology leaders for improving organizational resiliency?
A: Right now, we are all in a race against disruption and data. Customer buying habits have changed. Work-life balance has changed. The financial climate has changed. So, how do you establish a data-driven culture to identify and adapt to change? Or, in other words, what is the learning rate of your organization?
This is why executing a persistent data and analytics strategy is so important. It allows you to create a baseline of the past, identify when change happens, adapt your strategy, and make new, informed decisions. This process is your organization’s learning rate and competitive advantage.
Q: How can AI and analytics help business and technology leaders anticipate and adapt to disruption?
A: We are creating data that is outpacing human capacity. So the question becomes: how do you scale human observation and decision-making? AI is becoming the sensors for data in the world we’re in right now. So, we need analytics, machine learning and artificial intelligence (AI) to help scale decision-making for organizations.
Q: What best practices do you recommend around developing and deploying AI?
A: When we talk to customers, we first show them that the resiliency and agility of the cloud allows them to adapt quickly to the changing data environment.
The second step is lowering the barrier of entry for their workforce to become literate in analytics, modeling and decision-making through AI, so they can scale their decision-making. Everyone has a different maturity spot in that curve, but those who achieve this outcome will thrive – even in the face of disruption.
I recommend the following best practices:
- Think about the ModelOps life cycle, or the analytics life cycle, as a strategic capability in your organization. If you can observe the world faster, and make and deploy insights and decisions as part of AI workloads faster, you can see the future ahead of time. This means you have a competitive advantage in the market.
- Innovate responsibly and be aware of bias. We give capabilities and best practices to our customers that allow them to understand the responsibility they have when scaling their business with AI. And we are taking a practical approach to helping customers adhere to the ethical AI legislative policies that are emerging.
- Ensure explainability and transparency in models. You won’t have adoption of AI unless there is trust in AI. To have trust in AI, you must have transparency. Transparency is critical to the process.
Q: What does the future hold for AI and analytics?
A: Synthetic data is a big conversation for us right now. One of the challenges with AI is getting data labeled. Right now, someone must label, for example, a picture of a car, a house or a dog to train a computer vision model. And then, you must validate the performance of the model against unlabeled data.
Synthetic data, in contrast, allows us to build synthetic data that is statistically congruent to real data. This advancement represents a huge opportunity to help us create more robust models — models that aren’t even possible today because conventional data labeling is too challenging and expensive. SAS lowers the cost of data acquisition and accelerates the time to a model.
If, because of this innovation, they get insights about the future, companies gain a competitive advantage. But they must do it responsibly, with awareness of the bias that AI may inadvertently introduce. That is why we provide capabilities and best practices to our customers that allow them to understand the responsibility they have when scaling their business with AI.
For more information, download the SAS report – “4 Winning Strategies for Digital Transformation” – here.
