In a world rich in data, data enthusiasts and problem solvers can have greater success and innovate faster with flexibility in choice. To code or not to code. The answer aligns with the problem and the data talent working to solve it.
What does innovation look like inside your organization? How will innovation secure resiliency in a rapidly changing world? Flexibility in choice will be key.
The future of data and AI tools
According to the U.S. Bureau of Labor Statistics, employment of data scientists is projected to grow 35 percent from 2022 to 2032, much faster than the average for all occupations.
The world has problems ready to be solved with data. Technology needs to meet data users where they are and how they want to work to accelerate innovation. There are several ways to support developers and modelers who want to code. There are also solutions for users without programming experience in a low-code, no-code environment on a larger data and AI platform. What’s important is flexibility in these choices and different tools to support them.
“Data scientists and AI developers want to build models with modern open-source packages and cutting-edge cloud compute, but they are under pressure to deliver fast results and manage costs. They want prebuilt, scalable infrastructures that allow them to focus their time on model development and analysis,” said Kathy Lange, Research Director, AI Software, IDC.
To meet the need, SAS® Viya® Workbench is specifically tailored for developers and modelers to use either SAS or Python within IDEs like Jupyter Notebook or Visual Studio Code. Users can rapidly spin up a new environment and code in minutes in their tenant with their data. They can conduct exploratory data analysis and machine learning models with intrinsic compliance and governance.
Data and AI have increased the need for programming language talent. ZDNet revealed the top five programming languages on job listings, including Python and SAS.
Real-world application
Think about the real-world problems that are affecting businesses and the private sector. These include issues like crime and public safety, complaints management at a bank, phishing detection in email for cybersecurity and finding life-saving organ matches in health care.
These common problems can take organizations thousands of manual hours to solve. Since coders can and will solve our world’s biggest problems, access to a familiar development environment where they can quickly and easily innovate, experiment and iterate is key. In this scenario, finding solutions to problems can be drastically accelerated with significantly faster time to value.
The need to curb costs
IT leaders continue to crunch the numbers to get the most from their technology investments. Having flexibility in choice and time to value is important. Working in the cloud can reduce costs with the ability to scale up or down based on the project to save computing power in a workbench or on a larger data and AI platform. Working in an on-demand, self-terminating environment also improves cost efficiency.
In a CIO article, today’s CIO is not just a technology leader, but a business executive. “That means taking a long hard look at costs and ensuring their spend is focused on the future and not just maintaining the past,” says Jay Upchurch, EVP and CIO at SAS.
Greater productivity, faster innovation
The world we live in is rapidly changing. New questions and problems emerge daily. Technology must keep up with market demands and the need to innovate safely and ethically with governance. Organizations must remove barriers in data and AI tools so that more people are equipped to solve problems at scale with cloud technologies and remain adaptive and resilient. Getting more data enthusiasts access to self-service tools to build high-performance models is the go-forward strategy for innovation.
