Data is difficult to use
Important information is spread across systems. Define source ownership, quality and intended use before choosing a data service.
Platform / Productivity & Cloud
Connect cloud choices to the work they support.
Plan projects, services and operating responsibilities around the applications and data you need to run on Google Cloud.
Explore the fit
01
Projects
Organise resources and ownership.
02
Applications
Choose the services a workload needs.
03
Data
Plan access, movement and ongoing use.
Where to begin
A clear starting point for Google Cloud.
Define your requirements first, then evaluate the platform and implementation scope.
01
Define project boundaries
02
Review application and data needs
03
Plan deployment and monitoring
Connected work
The platform is part of a wider plan.
Google Cloud should be considered alongside your existing systems, team workflows and the support needed after launch.
Explore Data & analyticsQuestions worth answering
What should change for the people who use it?
Which data and systems must connect?
Who will own the platform day to day?
Applications and data
Start with the outcome.
Google Cloud offers infrastructure, application and data services. The best starting point is a specific workload and a clear understanding of its data, access and operational needs. Resource organisation then gives the work a manageable foundation.
What a good plan produces
A Google Cloud plan that keeps applications, data and ownership connected.
When it fits
Recognise the problem before choosing the platform.
These situations help identify where Google Cloud may be useful and what needs a closer look.
Application teams need a deployment path
A new service needs repeatable environments and visibility after launch. Plan deployment and monitoring alongside development.
Projects grew without structure
Resources and access are hard to understand. Establish project boundaries, naming and responsibilities.
Focus areas
The work behind the technology.
A platform decision affects data, teams and everyday operations. These workstreams help define a useful scope.
Resource organisation
Structure projects and environments around teams, workloads and governance needs.
Application architecture
Review runtime, storage, network and deployment requirements before selecting services.
Data foundations
Map data sources, movement, quality, retention and the people allowed to use them.
Operations
Set monitoring, access review, cost visibility and support ownership for ongoing use.
Practical approach
From first question to a workable plan.
Define the use case
Choose an application or data outcome with a clear owner.
Map dependencies
Review inputs, outputs, access and operational expectations.
Prepare the structure
Plan projects, environments, permissions and cost controls.
Deploy and learn
Validate a bounded workload and improve the operating model before expanding.
Before you decide
Three questions that shape the scope.
Use the answers to assess fit and understand what needs to happen around the platform.
01
How should projects and environments be separated?
02
Who owns data quality and access?
03
What monitoring and support does the workload need?
Common questions
About Google Cloud.
Is Google Cloud only for data analytics?
No. It provides services for applications, infrastructure and data. The right combination depends on the workload.
Why plan project structure early?
Project boundaries influence access, ownership, billing visibility and how teams manage resources.
Can we start with one workload?
Yes. A focused first workload makes it easier to test the architecture, permissions and operating process.
In Productivity & Cloud
Explore related platforms.
Next step
Talk through the right fit.
Tell us what you need Google Cloud to support.
