Google Cloud Platform

Three steps to prepare your users for cloud data migration

When preparing to migrate a legacy system to a cloud-based data analytics solution, as engineers we often focus on the technical benefits: Queries will run faster, more data can be processed and storage no longer has limits. For IT teams, these are significant, positive developments for the business. End users, though, may not immediately see the benefits of this technology (and internal culture) change. For your end users, running macros in their spreadsheet software of choice or expecting a query to return data in a matter of days (and planning their calendar around this) is the absolute norm. These users, more often than not, don’t see the technology stack changes as a benefit. Instead, they become a hindrance. They now need to learn new tools, change their workflows and adapt to the new world of having their data stored more than a few milliseconds away—and that can seem like a lot to ask from their perspective.

It’s important that you remember these users at all stages of a migration to cloud services. I’ve worked with many companies moving to the cloud, and I’ve seen how easy it is to forget the end users during a cloud migration, until you get a deluge of support tickets letting you know that their tried-and-tested methods of analyzing data no longer work. These added tickets increase operational overhead on the support and information technology departments, and decrease the number of hours that can be spent on doing the useful, transformative work—that is, analyzing the wealth of data that you now have available. Instead, you can end up wasting time trying to mold these old, inconvenient processes to fit this new cloud world, because you don’t have the time to transform into a cloud-first approach.

There are a few essential steps you can take to successfully move your enterprise users to this cloud-first approach.

1. Understand the scope

There are a few questions you should ask your team and any other teams inside your organization that will handle any stored or accessed data.
  • Where is the data coming from?
  • How much data do we process?
  • What tools do we use to consume and analyse the data?
  • What happens to the output that we collect?
When you understand these fundamentals during the initial scoping of a potential data migration, you’ll understand the true impact that such a project will have on those users consuming the affected data. It’s rarely as simple as “just point your tool at the new location.” A cloud migration could massively increase expected bandwidth costs if the tools aren’t well-tuned for a cloud-based approach—for example, by downloading the entire data set before analyzing the required subset.

To avoid issues like this, conduct interviews with the teams that consume the data. Seek to understand how they use and manipulate the data they have access to, and how they gain access to that data in the first place. This will all need to be replicated in the new cloud-based approach, and it likely won’t map directly. Consider using IAM unobtrusively to grant teams access to the data they need today. That sets you up to expand this scope easily and painlessly in the future. Understand the tools in use today, and reach out to vendors to clarify any points.. Don’t assume a tool does something if you don’t have documentation and evidence. It might look like the tool just queries the small section of data it requires, but you can’t know what’s going on behind the scenes unless you wrote it yourself!

Once you’ve gathered this information, develop clear guidelines for what new data analytics tooling should be used after a cloud migration, and whether it is intended as a substitute or a complement to the existing tooling. It is important to be opinionated here. Your users will be looking to you for guidance and support with new tooling. Since you’ll have spoken to them extensively beforehand, you’ll understand their use cases and can make informed, practical recommendations for tooling. This also allows you to scope training requirements. You can’t expect users to just pick up new tools and be as productive as they had been right away. Get users trained and comfortable with new tools before the migration happens.

2. Establish champions

Teams or individuals will sometimes stand against technology change. This can be for a variety of reasons, including worries over job security, comfort with existing methods or misunderstanding of the goals of the project. By finding and utilizing champions within each team, you’ll solve a number of problems:
  • Training challenges. Mass training is impersonal and can’t be tailored per team. Champions can deliver custom training that will hit home with their team.
  • Transition difficulties. Individual struggles by team can be hard to track and manage. By giving each team a voice through their champion, users will feel more involved in the project, and their issues are more likely to be addressed, reducing friction in the final stages.
  • Overloaded support teams. Champions become the voice of the project within the team too. This can have the effect of reducing support workload in the days, weeks and months during and after a migration, since the champion can be the first port of call when things aren’t running quite as expected.
Don’t underestimate the power of having people represent the project on their own teams, rather than someone outside to the team proposing change to an established workflow. The former is much more likely to be favorably received.

3. Promote the cloud transformation

It is more than likely that the current methods of data ingestion and analysis, and possibly the methods of data output and storage, will be suboptimal, or worse impossible, under the new cloud model. It is important that teams are suitably prepared for these changes. To make the transition easier, consider taking these approaches to informing users and allowing them room to experiment.

  • Promote and develop the understanding of having the power of the cloud behind the data. It’s an opportunity to ask questions of data that might otherwise have been locked away before, whether behind time constraints, or incompatibility with software, or even a lack of awareness that the data was even available to query. By combining data sets, can you and your teams become more evidential, and get better results that answer deeper, more important questions? Invariably, the answer is yes.
  • In the case that an existing tool will continue to be used, it will be invaluable to provide teams with new data locations and instructions for reconfiguring applications. It is important that this is communicated, whether or not the change will be apparent to the user. Undoubtedly, some custom configuration somewhere will break, but you can reduce the frustration of an interruption by having the right information available.
  • By having teams develop and build new tooling early, rather than during or after migration, you’ll give them the ability to play with, learn and develop the new tools that will be required. This can be on a static subset of data pulled from the existing setup, creating a sandbox where users can analyze and manipulate familiar data with new tools. That way, you’ll help drive driving the adoption of new tools early and build some excitement around them. (Your champions are a good resource for this.)
Throughout the process of moving to cloud, remember the benefits that shouldn’t be understated. No longer do your analyses need to take days. Instead, the answers can be there when you need them. This frees up analysts to create meaningful, useful data, rather than churning out the same reports over and over. It allows consumers of the data to access information more freely, without needing the help of a data analyst, by exposing dashboards and tools. But these high-level messages need to be supplemented with the personal needs of the team—show them the opportunities that exist and get them excited! It’ll help these big technological changes work for the people using the technology every day.