Data Activation 101: Everything You Need To Know

3 Mins read

Data is one of the most valuable assets that any company can own. Data is created with each interaction and can give deep insights into customer profiles, trends, and predictions. In the modern world, advancements in technology have raised the bar of competition for businesses to not only be relevant but also necessary. 

The rise in prominence of the internet has shifted society to where the consumer now has more options than ever before. So staying ahead of the game and excelling in customer experience has never been so crucial. Data is one of the best ways that businesses can accomplish this. Being able to make data-driven decisions has the power to completely transform how businesses view and achieve success.

The biggest challenge that businesses in the modern era face are that data is often times viewed retroactively. That is, data is created, and by the time it gets in front of department heads or leadership, its relevancy has been dated. Accessing data in real-time to help leaders make strong data-driven decisions is something that the digital world is constantly working toward making a reality. While data can have a great usefulness for analyzing what has and what hasn’t worked – it can also give insight into the present. 

Data activation is the ability to not just aggregate, translate, and analyze data – but make data actively work in real-time to help push departments forward. This is something that takes a lot of tools, knowledge, and skill to do well, and at the heart of data, activation is the modern data stack.

If you have been wondering about what it takes to accomplish data activation, here is everything you need to know! 

What Is the Modern Data Stack and Why Is It Important?

One of the biggest problems that businesses face when it comes to data, is simply having it in one accessible location in a format that makes it easy to share across departments. Data is created from disparate sources and it’s created constantly which means that a lot of data can accumulate very quickly. The problem with this is that the data is hard, if not impossible to actually use. This creates what has been known as a data silo, which is a term used to define data that isn’t usable.

The data stack is the ultimate answer to this problem, in that it allowed companies a way to not only aggregate all of their data from disparate sources but turn it into one unified format. Initially, this happened in the physical world with legacy, or traditional, data stacks. These were on-premise services that used ETL to migrate data from a silo into a warehouse. 

ETL stands for Extract, Translate, and Load. This process and related tools extracted data from its sources, translated it into one usable format, and then loaded it into what is known as a data warehouse. The data warehouse became one of the most important solutions to the data silo because now an entire company’s source of truth can come from one location. 

The modern data stack still uses the same premise as the legacy data stack, however, modern stacks are now cloud-based. Technology to support this shift started to really hit the market around 2012 and then gained popularity until 2016 when it became the norm. 

What are the Benefits of a Modern Data Stack?

Being able to shift from on-premise tools to cloud-based tools gave companies and developers a lot of new freedom. Snowflake, in 2016, became one of the largest and most popular cloud-based data warehouses and continues to be to this day. Because of the flexibility that the cloud offers, SaaS tools are constantly being developed to help companies not only aggregate but activate their data in more effective ways.

Examples of this, are that legacy stacks were limited in a lot of ways to only using now traditional ETL tools. With advancements in the modern data stack, now developers can use tools like ELT, which transforms data once it’s already been loaded into the cloud-based data warehouse. Another exciting innovation in the data stack is the use of reverse ETL. 

Reverse ETL is a powerful tool that works to accomplish a few key things with data. Firstly, because of the fact that it’s using a reverse ETL process to push data back out to endpoints, it is enriching the data as it’s being used. Secondly, it helps to activate the data making it available in meaningful ways for teams across a company which empowers more data-driven decisions. 


A modern data stack is unique because it focuses on three categories: simplicity, speed, and scalability. Developers have a lot more freedom to create unique and powerful tools that can help hone the efficiency, simplicity, and speed of a data stack in ways they couldn’t with on-promise legacy stacks. 

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