ENGAGEMENT
The Cloud organization for a large U.S. based telecommunications provider lacked the visibility required to make cloud technology decisions based on complete, up-to-date and accurate cost data. DayBlink Consulting developed a cost model and dashboard to easily estimate public vs. private cloud costs for proposed projects, allowing project leaders to make informed technology decisions based on the total cost of ownership of the proposed solution.
PROBLEM
The cloud organization’s internal customers and stakeholders were having difficulty determining the best cloud technology solutions for their various project needs. There was poor visibility of up-to-date cloud costing data and no tool to support making cloud implementation decisions, based on the total cost of ownership (TCO). In the absence of this information, project teams were choosing non-optimal cloud solutions for their projects, unintentionally driving up technology costs. Additionally, once a solution was deployed it was difficult to trace those costs back to the projects where they were incurred.
SOLUTION
DayBlinkConsulting was engaged to mobilize a program to gather the requisite cost data and build a user-friendly capacity planning financial costing model. The solution provided visibility to cloud customers allowing them to make informed decisions about various cloud technology solutions and enabling cloud leaders to appropriately assign costs back to the correct customers.
After confirming key stakeholders, model goals, and expected outcomes, the DayBlink Consulting team led client interviews to gather relevant inputs for each department and business group. Primary inputs focused on Compute (vCPU) & Storage (PB) vs. public cloud spend, including higher-level services.
The team then built a wireframe of the model, and mockups of the visualization dashboard, and reporting templates to confirm design choices and model outputs. After incorporating feedback, the team collected, aggregated and transformed the input data for the TCO model (i.e., data staging), and reviewed and validated the data inputs and calculations to refine model estimations. This model was then presented and approved by the Senior Technology Leadership Team and launched into the organization. We also documented and disseminated the processes to maintain, refine and true-up the model as cloud costs and enterprise priorities shift.
RESULT
As a result, the Cloud organization’s customers adopted a tool to more confidently predict the total cost of ownership (TCO) of a given cloud solution and make informed technology decisions. This drove more optimized spend within the cloud organization and higher overall satisfaction from cloud customers as total cost of ownership expectations were better managed and solution costs were in line with initial estimates and budget.