Case Study : Re-Architecting a Mission Critical Data Platform | Stand8
The applications that enterprise companies rely on to run their business require data. A LOT OF DATA. This data streams into company servers from both the cloud and on-prem locations. (If you need help with this hybrid model, check out our IT Operations services.)
Many companies face common data problems. Namely, the data is not organized, curated, or securely managed. A closer look reveals why.
Companies can easily stream tens of GBs of data into their servers daily. Dozens of applications use the same data for decision-making, marketing, content creation, ad sales, scheduling, and more. With so many teams, it's easy for the same data to be transformed and then transformed again, used once, and then forgotten. The duplicated data remains in storage. This contributes to quickly ballooning storage and processing costs.
To fix this problem, our team asked "why is duplicate data being created?" There are several problems to unpack here.
First, the client had huge volumes of data from different sources, of varying types, and for different use cases. These data stores were siloed, with no central data lake to act as a "single source of truth."
Second, the client did not have the resources to delegate who should have access to what data in a timely manner. The processes that did exist were simply too slow because the client didn't have enough data experts. Sound familiar?
Third, Data stored in different countries is subject to different regulations which makes processing times slow. Again, not enough resources.
So what to do? The answer to all of these problems was something called a
Data mesh makes data available and accessible to everyone who needs it, without needing approval from data teams. It's a faster solution that requires fewer resources. Using data mesh, we re-designed the data architecture to provide federated access to one data store that acts as a single source of truth. This solved all of the underlying problems. Data from different sources and regions was centralized and data teams did not have to delegate access.
All of this sounds well and good from a technical perspective, but what did it mean for the business?
From an initial 4 billion rows of data that was sitting in the client's Enterprise Data Warehouse, STAND 8 was able to cut costs 75% by removing unnecessary data from the data store.
The annual storage cost for one team on one platform was $870k. When scaled to other teams, the savings are clear. STAND 8 saved the company hundreds of thousands of dollars in the short term and millions in the long term.
Most importantly, teams had faster access to data without needing more resources, and data drove business decisions which led to further profit.
If you think top-tier data architecture is out of reach, think again. In this case, partnering with STAND 8 was a small investment with enormous operational and capital gains.