As the founder of modern management, Peter Drucker, once said, “If you can’t measure, you can’t improve it.” In an effort to modernize their enterprise data and analytics systems, many businesses are considering moving to cloud-based enterprise data warehouses. But to get there, they face challenges around legacy data and increases in disparate data sources, as well as in data volume, velocity, and integration silos. As companies develop their cloud journey roadmaps, data transparency becomes critical for data agility and data quality for business acumen. In the first of two posts on setting the foundation for an enterprise cloud data warehouse, I’ll describe the initial steps of the cloud journey that has proven to be the most successful path for many of our customers.
Most organizations have some semblance of a data warehouse—either an operational data store (ODS), an enterprise data warehouse (EDW), or a combination of the two. For companies that don’t have anything, commonly referred to as “greenfield” since they are starting fresh, the journey is the same: build value first!
The combination of an enterprise cloud platform and intelligent, automated data management enables you to quicklyembarkonyourjourney in an agile ecosystem, allowing for a new start or extending uponanexisting solution. By assessing your current landscape, defining your future state, and taking an iterative, high-value approach, you can bridge your legacy solution with your modern cloud data warehouse or data lake—without disrupting your data consumers.
The following steps outline a high-value, iterative path for your cloud journey that sets the data foundation across the enterprise:
Data’s value is determined by the business, and the insight it can provide accelerates better business acumen. Any new data journey not only must ensure that you are building a solid data foundation, but also that the data consumers are NOT disrupted.
When building an enterprise cloud data warehouse, think of remodeling a master bedroom in your home. Most remodels are done because the current space doesn’t meet the homeowners’ needs; some of those needs are critical and some are nice to have. If the existing master bedroom doesn't have the functionality or space, you’re better served by a new addition. This helps change the focus for the homeowner to the new benefits by quickly extending upon their home, versus trying to rebuild in an unscalable environment.
The architect provides a roadmap of the new addition and collaborates with the homeowners to prioritize the build. Once the new addition is completed, the backlog of needs is satisfied, and the developer can focus on remodeling the legacy space without disrupting the homeowners. This same approach works for your cloud journey:
Similarly, your cloud journey can be a daunting task if you don’t know where to start. It’s important to assess your current state, the backlog of business needs, and what your future needs will be by collaborating across the enterprise. Assessing where you are will help you better navigate to quicker value—having the ability to take departmental “tribal knowledge” and share it across the enterprise will be essential for end-user adoption.
Leveraging an enterprise data catalog can help facilitate this process in a more transparent and efficient manner:
For example, say your leadership team is trying to determine which opportunities lead to successful implementations, but their legacy solution cannot access the data in Salesforce and their current analytical tools are limited. By leveraging an enterprise data catalog and centralizing integrations, you can quickly migrate existing information into your cloud data warehouse or data lake and enrich it with the simplified connectivity to Salesforce. Now your organization has new analytical tools with better context to improve outcomes!
In my next post, I’ll explore how to find and build on quick wins in a cloud data warehouse or data lake and provide step-by-step guidance on creating a cloud strategy plan.
Learn more about Informatica data management for cloud data warehouses and data lakes.