Build a strong data foundation for AI-driven business growth

In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.Data silos, lack of standardization, and uncertainty over compliance with privacy regulations can limit accessibility and compromise data quality, but modern data management can overcome those challenges. By partnering with industry leaders, businesses can acquire the resources needed for efficient data discovery, multi-environment management, and strong data protection. To fully leverage AI and analytics for achieving key business objectives and maximizing return on investment (ROI), modern data management is essential.The power of modern data managementModern data management integrates the technologies, governance frameworks, and business processes needed to ensure the safety and security of data from collection to storage and analysis. It enables organizations to efficiently derive real-time insights for effective strategic decision-making.Some of the key applications of modern data management are to assess quality, identify gaps, and organize data for AI model building. It’s also useful in countering the pressing IT talent shortage, in many cases providing the deep and broad expertise that few organizations can maintain in house.Partnering for greater value generationSAS and Intel customers have found that the strengths of each company – SAS’s advanced analytics and Intel’s high-performance computing – are magnified through their “better together” approach. Together, they offer complementary tools and services to achieve data discovery, gain access to real-time insights, implement multi-environment data management, and embed data protection at the chip level.“Tasks such as data analysis, machine learning, and predictive analytics require high performance, which Intel’s latest processors provide,” noted Bruno Domingues, CTO for Intel’s financial services industry practice. “The faster data is processed, the quicker actionable insights can be generated.”And that processing speed need not be hampered by the quest for perfection. The goal of modern data management is not to make data pristine. “It’s impossible,” says Shadi Shahin, Vice President of Product Strategy at SAS. “Trying to clean the data and make it perfect is not going to work. Understanding the use of the data is critical – it must be fit for purpose.”Achieving ROI from AI requires both high-performance data management technology and a focused business strategy. Organizations that are determined to control costs, minimize risk, and maximize productivity in their execution of an AI strategy should start small, leverage state-of-the-art technology, and work with trusted partners. Getting trusted resultsThere’s no need for any organization to rely on traditional data management, data prep, and algorithms. “You can get value out of data much faster,” notes Shahin, “whether through recommendation engines, automated machine learning pipelines, or other modern techniques designed to solve legacy problems.”Together, SAS and Intel accelerate the journey to value realization. “You can start quickly and show value quickly,” adds Shahin. “You don’t need a multiyear project to show value in your data.”Check out this webinar to learn more tips and strategies for building a data foundation for AI-driven business growth.

In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.

Data silos, lack of standardization, and uncertainty over compliance with privacy regulations can limit accessibility and compromise data quality, but modern data management can overcome those challenges. By partnering with industry leaders, businesses can acquire the resources needed for efficient data discovery, multi-environment management, and strong data protection. To fully leverage AI and analytics for achieving key business objectives and maximizing return on investment (ROI), .

Modern data management integrates the technologies, governance frameworks, and business processes needed to ensure the safety and security of data from collection to storage and analysis. It enables organizations to efficiently derive real-time insights for effective strategic decision-making. Some of the key applications of modern data management are to assess quality, identify gaps, and organize data for AI model building.

It’s also useful in countering the pressing IT talent shortage, in many cases providing the deep and broad expertise that few organizations can maintain in house. and customers have found that the strengths of each company – SAS’s advanced analytics and Intel’s high-performance computing – are magnified through their “better together” approach. Together, they offer complementary tools and services to achieve data discovery, gain access to real-time insights, implement multi-environment data management, and embed data protection at the chip level.

“Tasks such as data analysis, machine learning, and predictive analytics require high performance, which Intel’s latest processors provide,” noted Bruno Domingues, CTO for Intel’s financial services industry practice. “The faster data is processed, the quicker actionable insights can be generated.” And that processing speed need not be hampered by the quest for perfection.

The goal of modern data management is not to make data pristine. “It’s impossible,” says Shadi Shahin, Vice President of Product Strategy at SAS. “Trying to clean the data and make it perfect is not going to work.

Understanding the use of the data is critical – it must be fit for purpose.” Achieving ROI from AI requires both high-performance data management technology and a focused business strategy. Organizations that are determined to control costs, minimize risk, and maximize productivity in their execution of an AI strategy should start small, leverage state-of-the-art technology, and work with trusted partners.

There’s no need for any organization to rely on traditional data management, data prep, and algorithms. “You can get value out of data much faster,” notes Shahin, “whether through recommendation engines, automated machine learning pipelines, or other modern techniques designed to solve legacy problems.” Together, SAS and Intel accelerate the journey to value realization.

“You can start quickly and show value quickly,” adds Shahin. “You don’t need a multiyear project to show value in your data.” to learn more tips and strategies for building a data foundation for AI-driven business growth.

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