To achieve maximum value, artificial intelligence (AI) should be applied and combined in ways that best fit each organisation’s needs, says Jim Goodnight, co-founder & CEO, SAS. Computer vision, natural language, optimisation and machine learning should be embedded in solutions and workflows to maximise productivity and drive competitive advantages, he tells Sudhir Chowdhary in an interview. Excerpts:
Tell us about SAS’ strategy to leverage cloud and AI?
Businesses around the world have realised that digital transformation is about more than just digital enablement. To truly transform themselves, businesses must be both digitally enabled and data-driven in all their actions. SAS is data agnostic and cloud agnostic. We meet our customers where they need us the most, and the cloud is one of the tools we use to get there. Robust business analytics capabilities help with this data-driven decision-making. And running analytics on cloud technology multiplies its potential impact to create a competitive advantage.
The SAS Cloud provides ease of access, ease of use, and ease of consumption for SAS customers around the world. To assist our customers in their digital transformation efforts and expand our install base, SAS is focusing on three areas: One, we will meet customers wherever they are in their cloud adoption journey, whether in the SAS Cloud or in the customer’s cloud of choice. Two, we will continue to introduce more cloud-based open integrations to expand our cloud ecosystem. Three, we will continue to work with our customers and our public cloud partners to better optimise analytic workload performance versus the cost to run.
What is SAS’ strategic roadmap for 2022-23, including the major areas of investment?
Businesses are turning more and more to analytics, MI and AI to improve their decision-making while balancing a changing economic environment. But with inflation, rising interest rates and increased demand for AI in the cloud, their bottom lines are being disrupted. To help address this, SAS is delivering a platform and solutions portfolio that maximises performance and scalability and simplifies data processing and AI, while also minimising cloud consumption costs.
We do this byinvesting substantially in optimising our analytics as a function of cloud economics, investing in technology and integrations that minimise data movement, and building a SAS Container Runtime that executes SAS and open-source models as extremely lightweight APIs. Our focus areas for this year to support this strategy include advancement in cloud, strengthening of solutions and stronger technology partnerships. Cloud remains a major focus area for us. In 2021, our global cloud revenue increased by 19%, driving about 10% of our overall revenues.
For solutions, we see an opportunity to build on last year’s growth as well. In 2021, our fraud and security intelligence solutions, as well as retail and IoT, all grew. And we saw strong third-party validation for solutions like SAS Customer Intelligence, our cloud-based solution for intelligent decision-making in marketing.
SAS is working with government bodies across India – what have been your key contributions in the sector?
The Indian government, like most national governments, possesses massive amounts of historical and current data, with more coming in regularly. Data tends to exist in disparate silos, with widely differing levels of quality. Combining data sources and improving data quality unlocks new insights and improves transparency and collaboration between departments. Then, reports can be more easily created and shared internally and with the public.
These factors all came into play in a public health project executed by the government of Odisha. We worked with Odisha to create a Covid-19 dashboard to track cases, allocate medical resources and prepare for future crises.
Various states including Odisha, Rajasthan, Maharashtra, Andhra Pradesh, Tamil Nadu, Chhattisgarh, and Jharkhand leverage our analytics for state-wide activity, since governments are increasingly being asked to become more transparent, collaborative, and participatory.
What are the emerging trends in data and AI? How can enterprises across sectors unlock their potential?
Within AI specifically, there is a trend of focusing on the broader ecosystem, going beyond just model development. In the past, the conversation around AI was generally limited to model building and performance. Now, we hear much more about the importance of the full ecosystem, including data management capabilities and decision-making and governance. Organisations are realising that each of these pieces plays an important role in their productivity and success with AI initiatives.
We are also seeing organisations realise that AI comprises more than just one technology. To achieve maximum value, AI should be applied and combined in ways that best fit each organisation’s needs. Computer vision, natural language, optimisation and machine learning should be embedded in solutions and workflows to maximise productivity and ultimately drive competitive advantages.
Across industries, we see these trends helping organisations unlock immense potential – from banks using conversational AI and natural language processing to improve marketing and sales to manufacturers turning to computer vision to identify quality issues and reduce waste.
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