If your company is like many others in the life sciences industry, you are planning a Crucial Stages cloud migration. Today’s public cloud options offer more predictable infrastructure costs, improved cybersecurity, and increased flexibility to innovate and grow.
Building and managing data pipelines to deliver analytics-ready data to data consumers can still be time-consuming. Here’s some advice to help you make informed decisions at each stage of your cloud migration:
1. Your strategy Crucial Stages for cloud migration.
Once you’ve determined your infrastructure and cybersecurity requirements, you can finalise your migration strategy. Now is the time to decide whether to lift and shift or use a cloud-native solution stack. While lift and shift is less complicated at first, it does not allow you to fully utilise cloud capabilities such as built-in analytics and artificial intelligence.
This is also an excellent time to think about how the migration will affect users, both employees and customers. Choosing between existing and cloud-native applications based on price, for example, is not a good idea if user experience suffers. Involve stakeholders from across the organisation to ensure that your cloud decisions deliver the business benefits you expect.
2. Make the infrastructure more automated.
You’ve left the world of managing physical servers in a data centre by moving to the cloud, but you can still overwhelm your team with infrastructure work if you’re not careful. Managing recurring data movement and preparation necessitates task and dependency scheduling, compute cluster provisioning, cost and performance optimization, and more. There are various options for relieving your engineering team of this time, ranging from open source orchestrators and serverless options to fully managed pipeline tools.
3. Make data production more democratic.
It’s common to think of data democratisation as primarily the result of a successful CDW project. Giving more data consumers access to dashboards and data sets is unquestionably important for a data-literate organisation. It is also critical to enable the data’s creators, who are the most familiar with its meaning and history. In the absence of this, a central team is left in charge of selecting data and delivering it to data consumers with meaning and value. They’ll either spend countless hours researching each domain and data source, or they’ll produce a CDW that users don’t understand or trust. Giving domain experts no-code tools to directly build pipelines and prepare data for analytics is a better approach.
4. Make a list of your infrastructure requirements.
Running an IT environment in the public cloud is not the same as running a data centre on-premises. You must inventory your current environment and understand how it will translate to a cloud deployment. You can set requirements and avoid disruptions by considering how the differences will affect connectivity, application availability, and customer and market data.
Keep in mind that you’re dismantling an on-premises infrastructure that grew as a result of positive company change and growth. Understanding all system and application integrations will help you determine what you can “lift and shift” to the cloud and what must be deployed as cloud-native for the first time.
These evaluations will also indicate whether you want to learn the intricacies of cloud infrastructure on your own or work with an Experienced Managed services provider.