Moving to the cloud? Start with a data strategy

December 8, 2021 - 5 minutes reading time
Article by François Zielemans

Moving to the cloud should be in the top 3 priorities for every organization. But where do you start? In many cases, this transition is still approached along the axis of the existing application landscape, with a separate migration strategy for each application. While this is the best time to migrate to a platform where data is central. Because not applications, but strategic goals and the data architecture required for them should be the basis for your cloud strategy.

By their nature, both business and IT departments often think from the existing application portfolio, with all its limitations, workarounds, and sub-optimizations. Because each application has its own database and that leads to multiple 'sources of truth', a whole landscape of point-to-point connections to tie them together and a long list of hard-to-maintain authorization schemes to monitor access.

The future success of a cloud strategy will largely be determined by chain-wide data that is real-time and actionable. And that requires more than a central data warehouse or "data lake" to which applications periodically send a set of new data. Instead of defining the cloud strategy by the existing set of discrete applications and infrastructure environments, it is good to use the following three tools.

1. Take a closer look at your business model

A maximally profitable cloud strategy starts with the future vision of the organization and the possible need from the market to adapt or radically change the business model. Almost every traditional business model is threatened by newcomers who act from a 'born-digital' or 'born-in-the-cloud' mentality. They are already exploiting the opportunities offered by cloud platforms, machine learning, low code and blockchain to provide added value to customers in radically different ways.

The first step of a future-proof cloud strategy is therefore to take a critical look at the current business model. Besides new insights, this also leads to an overview of the most important strategic priorities for the coming period. Operationalization can then be initiated.

When implementing these strategic priorities, data (sets) are crucial for action-oriented and real-time adjustments. The better the business can indicate which data is crucial for optimal decision-making within the business processes, the more effective the next steps will be.

A data-driven architecture is the starting point for a good cloud strategy.

2. Data first: create a data strategy

To get off to a good start with your cloud strategy, it is necessary to shift the focus from individual applications and workflows to data flows that transcend the chain. A data-driven architecture is the starting point for a good cloud strategy and for further operationalization.

With a vision on data that is aimed at proactive and strategic action, you develop a data strategy to ensure that:

  • money and resources are only spent on data that demonstrably contributes to the achievement of business objectives
  • projects and systems do not duplicate data individually
  • undocumented or inconsistent data does not lead to suboptimal decision making

Based on the new data strategy, it is then important to take a critical look at the business processes and the associated data and work flows. Questions arise such as: what customer and chain partner data is crucial and must be available in real time? Which data can be a day old? What does the business process look like if minimal friction for our customers is the starting point?

Transforming into a data-driven organization that works from the cloud is a major strategic step that requires vision and leadership at the highest level of the organization. Both business and IT managers need to join forces in order to create a strategic plan that fulfills the ambitions and goals of the organization.

3. Your cloud journey is a tailor-made journey

Because of the exponential growth of data combined with the cloud's ability to scale very quickly, the use of a cloud-based data platform is an obvious choice. It is quite possible that a significant portion of workflows can be modulated in the cloud rather than programmed in stand-alone applications. The more end-to-end workflows that are automated in this way, the smaller the number of legacy and SaaS applications left to rationalize.

What remains is a list of applications that must be assessed individually for their cloud-readiness and fit within a data-driven architecture. For each application, a choice of the following scenarios is obvious:

  • phasing out. Depending on the remaining time, the application continues to run on the existing on-premises infrastructure, or a "lift-and-shift" to the cloud takes place. In the meantime, use RPA or cloud-based microservices to best fit the application into the new architecture.
  • replace with a modern SaaS variant. This provides access to standard capabilities of both the cloud to scale up and down quickly, plus optimal integration with real-time data platforms.
  • modernize legacy customization so that the application can run in the cloud and exchange (real-time) data with the data platform via micro and web services. The first includes the rapid scaling up and down of resources, self-provisioning, and the use of cloud-based databases and other platform services.
  • replacing legacy customization with a custom native cloud application, using modern methods and techniques such as DevSecOps and DataOps. The latter involves not only an explicit focus on security from the initial design, but also on fitting it into the data architecture, resulting in a shorter feedback loop with all its benefits.

Which option is preferred varies by organization. But in all cases, a good cloud strategy begins and ends with data - not applications. Only a data-driven architecture and culture will lead to the right insights, decisions and actions required for a successful move to the cloud.

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