Five Steps For A Successful Migration To Cloud-Based SaaS Applications

Manish Patel

digital transformation can deliver improved flexibility, faster speed to market, and reduced costs, but only if you go about things in the right way. One path to a successful digital transformation is to move traditional software to cloud-based SaaS applications, a migration that requires taking a data-driven approach and using technology in strategic new ways.

Traditionally, most business application migrations start with business process reengineering, whiteboard sessions, offsite process walkthroughs, process mapping, and so on. All these are fine, but to achieve success, you need to take a fresh, data-driven approach that focuses on fact-based views of current processes and lays out unbiased options.

A data-driven approach that uses advanced technologies, such as machine learning and predictive intelligence, can provide opportunities to reduce costs, improve quality, and boost innovation. These five steps can help you successfully migrate legacy applications to a cloud-based SaaS environment:

  1. Establish a digital baseline. Before implementing SaaS, you should deploy data-discovery tools to identify the current state of business processes and build a digital blueprint of all baseline activities. Tools such as Hadoop, Spark, and Google TensorFlow can be used to construct machine-generated process maps, automated metrics calculations, and intelligent “hot spot” analysis. This will show what process areas need to be fixed and where the fixes should be applied.
  1. Simplify and standardize. Once the digital baseline is in place, the next step is to simplify and standardize. This can be done by analyzing each process area that has been customized and comparing it with modern best practices, while leveraging modern technologies including cloud, mobile, analytics, social, Internet of Things, and Big Data. This helps you visualize future-state processes, identify process-improvement opportunities, and mitigate risks with the right organizational change-management approaches and training strategies.
  1. Deploy diverse migration tools. The path to SaaS migration relies on process discovery, rapid deployments, and automation. Enterprises should deploy a wide array of migration and testing tools to perform extracts and upload setups and master data. Once deployed, you should engage in end-to-end automated functional testing of applications and other critical tasks.
  1. Closely monitor the migration. Be prepared to generate detailed reports and dashboards that allow you to review configuration uploads to ensure that they are all loaded and verify that they are correct and supported. Testing is also key. You should establish a test repository with assets such as scenario descriptions, test scripts, and user-configurable workbooks and provision testing-as-a-service (TaaS) to reduce testing time and costs.
  1. Automate and optimize. After your migration is complete, your focus should turn to automation and optimization. For example, you can use data from before and after migration to identify candidates for automation to make sure your digital workforce (bots) is executing each automation step as planned. Also, your organization can drive continuous innovation and improvement via lean methods to optimize workflows and team performance.

Successfully migrating to cloud-based SaaS applications involves changing your business, your processes, and even your people across the enterprise. A data-driven approach is effective only when technology, people, and talent – business and IT, along with leadership – are integrated with the right balance to execute cohesively with a clearly defined end goal in mind.

This article was originally published by DXC Technology and is republished by permission. DXC Technology is an SAP platinum partner.

Manish Patel

About Manish Patel

Manish Patel is digital transformation advisor at DXC Technology. He is responsible for building and driving adoption of digital cloud solutions — including robotic process automation, artificial intelligence, machine learning, Internet of Things, and analytics — to help customers transform their business models to the next generation of digital.