Cloud migration can enable innovation, help reduce your total cost of ownership, and operating costs, increase flexibility and agility, and reduce time to market for new products and services. But every business is different and has different goals.

Many businesses that are considering moving to the public cloud are slow or hesitant to make the move due to lack of clarity around its financial impact. It is crucial for businesses to fully understand the financial outcomes. How could moving applications and infrastructure to the cloud help save money?

The larger a business is, the larger and more complex its’ infrastructure footprint will be. Most of the larger organizations Motifworks has worked with have 1000s of physical and virtual servers in multiple locations, running 100s of different applications in a variety of hardware and operating systems.  And, the fact is that their IT infrastructure is growing, not shrinking.

One of the first steps we recommend is to use your own data to help you gain clarity. This data collection includes discovery and inventory of your servers. It will give you visibility into all the infrastructure related assets that exist in your organizational IT landscape – network, data center, servers, workstations, etc.

Data collection will help you create a consolidated view and help answer questions like:

  • How many servers do I have across all of my data centers?
  • How many are physical and virtualized?
  • How old is my server hardware?
  • What operating systems and software are running on each server?
  • How much are these servers utilized on a daily basis?
  • And more…

Motifworks has seen multiple instances where the collection of this data and using a good visual analysis tool like Power BI or even Excel has provided great insight and multiple “aha” moments.

Such data analysis helps you look at your infrastructure much more objectively. After looking at their own data, we have seen customers wondering – why do I have such complexity in our environment, I never realized that we still have servers running end of life/support OS like Windows 2003, or simply asking why do we even have so many servers in this data center?

The insights provide the opportunity to refresh the information about your infrastructure based on a much sharper understanding of the deployed assets.

Additionally, once you have this data, you can create your own decision matrix based on what’s most important to your organization. You can slice and dice this data in many different ways to create a hierarchy for cloud suitability.

You can also gain an understanding of your indicative costs of running these servers in a public cloud like Microsoft Azure. If you are using the Lift and Shift model for migration, it may be easier to do a cost analysis, but sometimes it may be misleading because you may not need the same number of servers in the cloud with exact same specs. One of the biggest benefits of the cloud is flexibility and on-demand scalability in comparison to on-premise infrastructure design, which is based on the worst-case scenario to mitigate your risks of business continuity.

With more and more organizations considering relocating applications to the cloud for operational efficiency, it is important to use a data-based analytical approach to rationalize the decision to migrate. Having used this approach for cloud assessment with large organizations with over 2000 servers in multiple data centers, and hundreds of applications, we have devised an assessment approach that helps you give a business case analysis and objective view of a migration plan.

How long has it been since you took a good hard look at your infrastructure?  Are you where you need to be?  Are there efficiencies to be gained? Would you save time and money by moving some or all of your infrastructure to the cloud?  If you can’t answer these questions straightaway, Motifworks can help!  Ask us about our unique approach today!

Free tools from Microsoft that we recommend for data inventory:

Paid tool for detailed data analytics and visualization: