At SAS, we believe analytics is the force that drives change across organizations. Today, as change has been further accelerated, digital transformation is happening faster than anyone planned. Amid these advances, the use of analytics has become even more crucial, especially as a role in mission-critical applications.

In 2020, even the definition of a mission-critical application is shifting. For some businesses, online sales that merely supplemented in-person sales are now vital. Likewise, communication tools that allow employees to work from home are now essential instead of optional.

The same is true of analytics. What used to be a nice-to-have application for optimizing decisions is now a must-have. Analytics and artificial intelligence are at the forefront of the mission-critical conversation. Today, organizations need to make decisions faster. They also need their decisions to be bolder and more effective.

Let's look at a few current examples of mission-critical applications that rely on analytics to be effective:

  1. Customer call centers are mission critical for many customer-centric businesses, and we work with call centers in many industries, including insurance, retail and banking. Their operations have become more analytically driven over the years to improve engagement, customer satisfaction and call times. Today’s call centers rely on analytics not only to route and respond to requests optimally, but many use AI technology for chatbots and call scripts.
  2. Grocery supply chains are under a lot of stress. Consumer demand has shifted, product shortages are common, and online ordering changes purchasing habits. Analytics is critical for demand planning, inventory optimization and forecasting. Grocers around the world have called on us to optimize their supply chains to keep customers happy and shelves full.
  3. There has never been a stronger case for hospital resource optimization. As COVID-19 cases continue to spike in different regions, hospital decision makers need to understand ICU capacity, equipment inventory and staff optimization. We have partnered with hospital systems to create predictive models that are essential for planning not only what is needed today, but what will be needed next week and next month.

The key to success with these applications is not just equipping data scientists, but empowering all relevant users to make decisions with data and integrating analytics into operational applications. You need more than just the algorithms. You need the customer specialists, demand planners, epidemiologists and clinicians involved in using analytics too.

For analytics to become mission critical, decision making with data needs to make its way into the systems and processes where decisions are made. It starts with data collection, data governance, data cleansing, data quality, then visualization reporting, statistical modeling and machine learning. And then it moves into call centers, inventory planning systems and resource planning programs.

Mission critical moves to the cloud

For many of these mission-critical applications, we are seeing a growth of data sources in the cloud and on the edge. When data moves to the cloud, the analytic workloads need to move with it. Our customers have been with us for a long time, many of them for decades, and they have very complex operations.

Since we work with the largest enterprises, a move to the cloud is not just about lifting and shifting one application. Rather, it can involve a list of very complex operations, many being real-time, mission-critical operations that need to run uninterrupted during the migration. Customers do not want to talk to 20 different vendors to find a solution for that.  They want us to join forces and work together as trusted partners in the cloud, presenting a compelling path forward for them.

The transition of enterprise to the cloud is not stoppable. What we are seeing is a need for more enterprise-level, mission-critical solutions that solve business problems in the cloud world. This is why the collaboration between SAS and Microsoft is a “better together” story. When we began talking, we realized there is incredible potential in digital transformation. What really excites me about the collaboration is that shared long-term vision of what artificial intelligence, machine learning and digital transformation can be like.

SAS working with Microsoft can create an operating system for digital transformation. So, any organization that wants to quickly transform has the business tools, the services and the products needed to succeed.

For more information, read Microsoft’s blog and learn how you can run your business-critical applications on Microsoft Azure.

Learn more about the empowered cloud from SAS and Microsoft

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