In short, digital transformation concerns competition, speed, quality and costs. In the current connected world, the consumer has a lot of choices and is also less bound to a single provider. The customer expects immediate answers to rely on. Will someone wait two weeks for a loan approval, or will he or she work with the party that makes a clear proposal online immediately? Probably the latter, where both the buyer and the provider must be able to rely on the results.
Why digital transformation
That is why it is important that organizations use well-controlled and reliable models to make such decisions. Organizations will only make a true difference if they can actually use the insights they gain from their data within their business processes – the operationalization of analytics. It requires comprehensive control of the model management process of both SAS and open source models.
Deploying a model is a challenge in itself, but the process is not yet complete. It is important to continue to monitor the performance of the model and, if necessary, replace it with a challenger. Deploying a new model needs to be fast and robust, and it shouldn’t lead to a new (long-term) development project. Ideally, this is operational work, not development work.
What demands does digital transformation make on the analytics platform?
Organizations applying new technologies – such as AI, machine learning and open source technologies – demand a more flexible use of their analytics platforms. Many of our customers follow a cloud-first strategy. This requires flexibility in the way our software can be used. An on-premises or hosted infrastructure implementation is primarily designed for peak loads, while the cloud variant requires continuous monitoring and scalability of the platform. Only by working in this way can you efficiently use the resources.
In addition, the cloud makes it possible to quickly deploy all kinds of open source tools for performing analytics. Data scientists need a certain amount of freedom in their choice when it comes to tools, and many organizations certainly welcome this. However, they only get results when they can also deploy analytics quickly and continue to monitor them.
The SAS Analytics Platform offers organizations both freedom of choice and control. And it supports the entire analytics life cycle, from data, to discovery, to deployment. This gives our customers control over their entire process, no matter if it is about security, model management or model deployment, without sacrificing freedom of choice. The customer determines the language in which the models are developed, whether it is installed in a cloud or on-premises, how many resources are allocated at what time, and how the models are deployed in the production environment.Many of our customers follow a cloud-first strategy. This requires flexibility in the way our software can be used. #cloud #digitaltransformation #datascience Click To Tweet
Migrating analytics to the cloud
New forces are shaping the analytics ecosystem. Because of increased competition, rising customer expectations and new, emerging technology such as AI and machine learning, IT departments are challenged with evolving their analytics ecosystems to meet the demands of their business partners.
- How is your organization doing this?
- How does your analytics cloud strategy compare to the market?
- What do your peers think about migrating analytics to the cloud?
SAS conducted a survey on the topic and if you would like to receive an industry report with insights into how you industry compares to the market, please register here.
For more information about how to successfully mitigate analytics to the cloud register here.
The SAS Analytics Platform offers organizations both freedom of choice and control. And it supports the entire analytics life cycle, from data, to discovery, to deployment. #cloud #digitaltransformation #datascience Click To Tweet