Many times, I see clients who have no current cloud footprint use the power of analytics to pull the rest of the organization toward the cloud. These new adopters leverage many powerful tools without making a massive, upfront investment. This can seriously accelerate their analytics abilities and their cloud maturity.
The Cloud and analytics maturity curve explained
The graph shown above illustrates a typical analytics maturity curve. As you can see, an analytics department that’s on the lower end of the maturity curve is typically relying upon “descriptive” analytics that can provide the business with information about the past. An analytics department that’s on the mid-range of the maturity curve can provide a more “predictive” (insights-driven) view of the enterprise that offers a glimpse into what the future may look like for the business. An analytics department that’s on the mature end of the curve, however, is positioned to provide “prescriptive” analytics that offer deeper, actionable insights that enable a business to operate with greater agility and plan for the future with a higher degree of accuracy.
You can take the same concept of the analytics maturity curve and apply it directly to the cloud. For example, an enterprise with an immature cloud analytics platform can only source and provide descriptive analytics. An enterprise working with a cloud analytics platform in the mid-range of maturity, on the other hand, has typically migrated a critical mass of data but is only just starting to dive into all of the analytical capabilities the cloud can provide. The enterprise with a high degree of maturity on a cloud platform, however, is typically operating with proven storage and processing capabilities and is now starting to look towards cloud-native tools (e.g. Amazon Web Services Sagemaker, Azure Machine Learning, or Google’s Cloud AI) that can significantly accelerate an analytics platform. It’s only when a platform is mature enough that an organization can begin to fully leverage groundbreaking analytics concepts, such as Machine Learning (ML) and artificial intelligence (AI), to which every enterprise aspires.
Accelerate your journey along the maturity curve
For many organizations, the journey along both the analytics and cloud maturity curve can seem incredibly daunting, expensive and unfeasible. When a business finally reaches a point where they must start along the journey (or risk falling behind the competition) they generally experience trouble getting past the initial hurdle, which is resistance to change. Or, the business doesn’t want to provide any resources to help launch journey. It then becomes purely an a IT initiative, which often lacks the support to match the velocity needed.
So, how can an organization accelerate themselves along the cloud maturity curve and get to advanced analytics faster? By leveraging strong, cloud-native tools like Snowflake. What is essential, however, is a plan.
Too many times, a cloud analytics platform falls down because there is no concrete plan to constantly innovate. I’ve seen enterprises invest in a single solution, such as an on-premises data warehouse that’s been shifted to the cloud, because they think it will solve all their problems. They soon realize, however, that it’s not that simple. The problem with a “cloudified” version of a data warehouse solution is that it flies directly in the face of what a modern data architecture is. In fact, one of the key components of a modern data architecture is that it’s decoupled. That means you should be able to replace any one piece of your architecture, such as your ETL solution, with the newest, best-of-breed tool and do so with minimal impact to the rest of your architecture.
This is where a data warehouse built for the cloud like Snowflake becomes such a great accelerator. Snowflake’s multi-cluster, shared data architecture separates storage and compute, making it possible to scale up and down on-the-fly, without downtime or disruption. By providing a central storage repository that’s separate from your computing resources, only then can you move quickly through to the higher levels of the maturity curve. When you’re moving towards the prescriptive and predictive phases, Snowflake provides a foundation that allows you to build out a truly modern data architecture, leveraging all of the benefits of the cloud with tremendous benefits to the enterprise.
No matter where you are on the cloud maturity curve, data storage and warehousing will always be a central component of your architecture. By leveraging cloud-based solutions like Snowflake, you remove much of the developmental overhead that you would have with a solution that had originally been built to be deployed on-premises. And, by working with implementation partners like Slalom, you can get the most out of your cloud data warehouse, accelerate your journey to the top of the cloud maturity curve, then begin using truly revolutionary analytics, such as ML and AI, to which every enterprise aims.
Author Bio: James Anderson is a Solution Architect at Slalom, a Snowflake Implementation Partner headquartered in Seattle. James specializes in analytical data platforms built in the cloud, helping to enable enterprises get the most out of their data, and unlocking new and exciting technologies for them. James is based out of Slalom Boston, and is a frequent contributor to the Slalom technology blog.