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Analytics can turn disruption into opportunities, says Bryan Harris

In an exclusive interview, Bryan Harris, Executive Vice President and Chief Technology Officer, SAS, says the new currency for growth is scaled and accurate decision-making.

Analytics can turn disruption into opportunities, says Bryan Harris
[Source photo: Anvita Gupta/Fast Company Middle East]

Ask any C-suite executive of any company what they most want, and they would probably ask for growth. But while looking for it fundamental changes around how companies use analytics are needed.

Data plays a critical role, which will only increase in the years ahead. Marketing needs data to understand past, current, and future customer trends, while procurement uses the information to reduce costs and manage suppliers. Sales rely on its data to understand its pipeline and where deals are in the sales cycle. 

Combining these silos and managing these processes across multiple line-of-business teams requires a new approach — using real-time analytics to make data-driven decisions to help spot new opportunities for the future.

“Since 2000, many people have said that we’re in the information age. I would argue that we’ve now moved on to the analytics age,” says Bryan Harris, Executive Vice President and Chief Technology Officer at SAS, in an exclusive interview.

“From computing big data to creating decision models, we’re all using analytics in bold new ways. Even amid geopolitical risk, climate change, supply chain breakdowns, and economic inflation, analytics can turn disruption into opportunities. Our new currency for growth and survival is scaled and accurate decision-making,” he adds.

Across all industries and business areas, he says, there is a renewed focus on data and analytics today, driven by increased computing power, a hyper-connected world, and powerful technologies like AI and machine learning.

As data analytics becomes more ingrained in each business process, more businesses expand their use of data as a strategic asset within their business strategy in the future. This goes beyond finance and into broader areas of the business sector. 

For example, Harris says that an insurance company that uses AI throughout its organization “can reduce the cost of servicing claims and improve customer satisfaction” compared to the rest of the industry.

Harris, who was instrumental in releasing the latest advancement of SAS software, SAS Viya, says SAS is uniquely positioned to help its customers accelerate the adoption of analytics, machine learning, and AI. “We believe that analytics helps companies become more resilient and agile.” 

Today’s consumer looks for trust, authenticity, and transparency from brands, and demand for privacy and control over data is at an all-time high. But the demand for control, he says, along with data deprecation, is limiting the quality and granularity of data that brands possess and can collect. “As a result, they continually evaluate what ‘value exchanges’ they create with a consumer, to collect data transparently and authentically.”


While AI innovation can provide insights about the future and help companies gain a competitive advantage, all agree on one thing: that it must be done responsibly, with awareness of the bias that AI may inadvertently introduce. 

Over the next decade, while it is easy to contemplate the advancements in natural language processing, conversational AI, predictive modeling, complex simulations, and computer vision, which will positively impact businesses and society, Harris says the volume of data created daily is exceeding the collective human capacity to make sense of it.

Overcoming this information overload will lead organizations to ramp up their adoption of AI models, and trust and explainability will be the number one expected feature. “AI-driven decisions must be explainable, especially when AI makes a recommendation or decision that is surprising or unintuitive.”

Harris, who has more than 20 years of experience researching and developing analytic techniques and distributed computing and cloud architectures, says industry-specific AI model marketplaces that enable businesses to easily consume and integrate AI models without having to create and manage the model lifecycle are the future. 

“Businesses will subscribe to an AI model store. Think of the Apple Music store or Spotify for AI models broken down by industry and data they process.”


Perhaps, even data management will become automated with AI as organizations struggle to keep up with the speeds and feeds of their data, spending 80% of their time wrangling data and 20% of their time performing analysis and modeling. “Over the next decade, one of the largest impacts AI can make to overcome the information overload is to automate data management processes. This will allow customers to spend 80% of their time analyzing and deploying more models into production.

Organizations with large amounts of data, which is most organizations today, are using AI in new and different ways to discover insights and make better decisions. As these capabilities are being applied to traditional approaches of segmenting, forecasting, customer service, and other areas, organizations are “finding better results,”  says Harris. 

“For example, manufacturers are using computer vision to identify quality issues and reduce waste; retailers are using machine learning techniques to improve forecasts and save on inventory and product waste costs, and banks are using conversational AI and natural language processing to improve marketing and sales.” 

In the healthcare sector, specifically in the UAE, Emirates Health Services partnered with SAS to address critical challenges brought on by COVID-19. “SAS AI and analytics were used to develop various predictive models for mortality risk, hospital capacity, and physician and staff utilization in hospitals. Using data and analytics, EHS’ COVID-19 response and resilience strategy have been one of the best in UAE,” Harris adds.

Talking about the rising trends in the industry, Harris says SAS is exploring synthetic twins, where machine learning capabilities are applied to digital twins to run simulations to see and test various outcomes. “The next generation of the analytics life cycle, or ModelOps life cycle, will simulate complex systems to help prepare for any possible scenario or disruptive event. From there, businesses can make rapid and resilient decisions to minimize risk and maximize profits.”

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Suparna Dutt D’Cunha is the Editor at Fast Company Middle East. She is interested in ideas and culture and cover stories ranging from films and food to startups and technology. She was a Forbes Asia contributor and previously worked at Gulf News and Times Of India. More

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