As the founder of modern management, Peter Drucker, once said, “If you can’t measure, you can’t improve it.” In an effort to move towards a modern enterprise data and analytics ecosystem, many businesses are considering moving to cloud-based enterprise data warehouses, like Google BigQuery. But to get there, they face challenges around the increased disparate data sources, volume, velocity, legacy data, and integration silos. As companies develop their cloud journey roadmaps, data transparency becomes critical for data agility and data quality to excel your business acumen. This article discusses 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!
4 Steps to Move to Google BigQuery Without Disrupting Data Consumers
Cloud ecosystems, like Google Cloud Platform (GCP), enable organizations to quickly get started on their journey in an agile ecosystem, allowing for a quick new start or extending upon your existing 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 ecosystem – without disrupting your data consumers.
The following steps are a high-value and iterative for your cloud journey, that sets the data foundation across the enterprise:
- Simplify collaboration across the enterprise to quickly discover high-value data assets
- Streamline integration efforts with standardized and reusable integration assets
- Avoid persisting redundant or outdated legacy solutions by taking advantage of cloud ecosystem technologies
- Discover the quality of your data BEFORE integrating
Maximizing Value from Your Move to BigQuery Cloud Data Warehouse
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, like BigQuery, 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 are satisfied, and the developer can focus on remodeling the legacy space without disrupting the homeowners. This same approach works for the cloud journey:
Similarly, your cloud journey can be a daunting task if you do not know where to start. It’s important to assess your current state, the backlog of business needs, and what your future needs should be by collaborating across the enterprise. By assessing where you are, it 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, 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 ecosystem and enrich it with the simplified connectivity to Salesforce. Now your organization has new analytical tools with better context to improve outcomes!
I’ll go into more specific steps you can take to move to a data warehouse on Google Cloud Platform in my next post.