Data has long been at the heart of what makes business in the digital age move forward. As the world of business and commerce has moved into the digital sphere over the last several years, data has continued to become one of the most valuable commodities. Every interaction and event that takes place within the digital plane creates data, and as you can imagine, this kind of data can accumulate rather quickly.
With the rise in technology and the advancements that this technology gave to the world, also came quite a bit of new challenges. One of the greatest challenges has always been how to understand and utilize data in an effective, and profitable way. While these challenges have been very real for the world of business and commerce to tackle, recent years have seen a lot of progress. While there will always be better tools and solutions being constantly developed to help businesses better understand and use their data, one of the most powerful solutions is known as the modern data stack.
If you have been curious about what exactly the modern data stack is, and how it’s helped businesses rid themselves of data silos, here is everything you need to know.
What is a Data Silo?
The idea behind a data stack is nothing complicated, however, its execution is far from simple. In order to understand what the modern data stack is, you need to understand why it was necessary. This means talking a little bit about what a data silo is and why it posed such a big problem.
When the digital age started to really get going, businesses found that they were having trouble accessing data that could help drive their department forward. While data was being created it was either inaccessible completely or hard to access and inconvenient. This aggregation of valuable data became known as a data silo. So one of the first challenges that businesses had to tackle was taking data out of a silo, and making it available.
The Beginning of the Data Stack
This is where the data warehouse became, and still is, the most popular solution to the data silo problem. A data warehouse, essentially, is an aggregation of data that has undergone certain modifications that make it accessible to data engineers and scientists. The process of moving data from a silo into a data warehouse is known as ETL. ETL stands for Extract, Transform, and Load, which are three steps that data must be taken through in order to be moved to a warehouse.
First, the data is extracted from a silo. Data is typically created through disparate sources, and extracting the data into one aggregated source is important. Before being loaded into a data warehouse, it has to be formatted so that it can be accessed. The format is crucially important for data because small variances can have a big impact on productivity and access. Formatting all data from a silo into one single uniform format is a crucial part of the process. Once it’s been extracted, and formatted, it’s then ready to be loaded into a respective warehouse.
The Purpose of a Data Stack
As the name implies, a modern data stack can have more than one component. The whole point of stacking tools in a data stack is to give your business the ability to retrieve and use data in effective ways. That’s why the core components of a data stack need to focus on three important things, simplicity, speed, and scalability.
If data is not stored in a way that makes retrieval simple and easy to do, then it will face the same problems it did while in a data silo. Having tools that allow for easy access to your data is one of the main reasons for investing in a data stack. With cloud-based services and innovative tools like Reverse ETL, this has never been more accomplishable.
This also plays into why a data stack needs to be performance optimized for the needs of your company. Data needs to be accessed in real time, so finding tools to activate your data is crucial and should be a part of your modern data stack. This is one way that operational analytics, powered through reverse ETL, can improve your data stack’s impact.
Lastly, scalability is important for a data stack. Using tools and services that can scale is valuable because you don’t want to limit your success and progress.
Finding ways to optimize your data stack to meet the needs of your business is something you can do. Focusing your goals when creating or optimizing your data stack around simplicity, speed, and scalability is a powerful way to ensure you give your company the tools it needs to be successful.