3 principles that emphasize productivity for your analytics platform | 7wData

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More digital channels are bringing greater connectivity and more data is bringing added complexity to organizations. All this can feel chaotic or like a fog of information warfare. As a result, the pace of disruption and data expansion require visual tools that accelerate data wrangling and modeling.

To overcome complexity, organizations need an analytics and AI platform that drives productivity and business outcomes. The hyperconnectivity and complexity of data today demand productivity.

But what does this mean for employees?

If you’re in IT, you need to configure and optimize environments for agility.

If you’re a data engineer, you need to collect, manage and convert data to be analytically ready. If you’re a data scientist, you need to work with large data sets to solve complex problems as quickly as possible. If you’re a business analyst, you need tools to gain insights quickly and solve the biggest challenges for your business.

During the SAS Explore Opening Session, Chief Technology Officer Bryan Harris spoke about the value of working with a persistent data and analytics strategy to drive results, and he left listeners with three principles for achieving productivity: simplicity, transparency and efficiency. In short, model development must be efficient, automated and explainable to drive adoption. Think of these as critical components of

Since collaboration enables productivity, too, it was fitting that other SAS research and development leaders joined Harris to demonstrate the end-to-end capabilities of SAS® Viya® . The demos further emphasized how these principles can help organizations quickly overcome complexities in implementing analytics. Keep reading to see photos from the event and to read quotes from the demonstrators about the value of productivity.

The goal is to overcome complexity with simplicity. When it comes to analytics, simplicity is accomplished with comprehensive capabilities. Harris talked about the many ways simplicity can help overcome complexity and improve the use of AI throughout the organization. In the quotes below, you'll read how he and Shadi Shahin discuss the benefits of simplicity, especially when it comes to deploying analytics.

"Complexity must be matched with simplicity. It starts with the simplicity of discovering connections in your data, the simplicity to improve the quality of your data, the simplicity of visualizing and understanding which data matters, the simplicity to build sophisticated models at the click of a button and the simplicity to deploy analytics and AI everywhere throughout your enterprise." – Bryan Harris, CTO

"Productivity requires a collaborative environment. We refer to this as the team sport of analytics. That means adding capabilities in the platform that speak to the different roles in your organization. This is why we provide  low-code and no-code capabilities, so each role can contribute appropriately as they collaborate." – Shadi Shahin, VP, Product Strategy

Shahin believes that as we remove barriers to entry to analytics, barriers between practitioners of separate disciplines should be removed. That means adding capabilities in your analytics platform that speak to the different roles in your organization. For example, as Shahin continued, "The handoff between a data engineer and a business intelligence engineer needs to feel natural."

Overcoming complexity with simplicity starts with comprehensive capabilities across the analytics lifecycle.

If you are more efficient, chances are you can see the future faster. But how does this translate to the world of ModelOps and AI? Today, 90% of models never make it to production. In this day and age, you can’t afford prolonged IT projects and science experiments. "Speed to production is critical.