The world of data engineering is one that has a lot of promise and is a great career choice. Commerce and business are relying more and more on data and the demand for engineers and data scientists are growing at a steady pace. For those who can learn how to understand and even master data skills, there is a lot of job security.
If this field of work is something that interests you, or perhaps you are already in it but are looking for ways to improve, data operationalization is a valued skill. The process of operationalizing data is one that can bring a whole new level of value and productivity to any team.
If you have been wondering what data operationalization is and the tools you need to accomplish it, here is everything you need to know.
What Does Operationaliztion Mean?
Operationalization is a term used to describe the process of setting up systems that make measurable observations. This is often times used for more abstract, or harder to quantify concepts. At its heart, operationalization is the science of making understandable metrics and being able to accurately analyze data. From a data perspective, this kind of skill set is absolutely invaluable, and it’s something that a lot of teams can struggle to operate without.
Data is one of the most valuable commodities that any company can have. This element of a company has insight into value and resources and is ultimately the most powerful tool that leaders can use to make impactful decisions. In the digital age, one of the biggest issues that companies have faced is how to access and use the data that exists.
The simple truth is that data is constantly being generated with every action and interaction that a company makes. However, acquiring the data and then understanding the interpretation of that data poses a huge challenge to modern industry. Not only is simply aggregating or acquiring data a challenge due to the disparate sources that it’s created from, but then interpreting that data is a whole new challenge.
One thing that companies have to face, is the fact that data engineering takes serious skill, effort, and attention to do well. Not every team or department can afford to have its own engineer or data scientist, so companies are forced to think of creative ways to not over-extend their data teams and burn out their engineers or scientists.
One tool that has proven very useful for helping companies acquire data operationalization is reverse ETL. Reverse ETL is at the heart of operational analytics and is an awesome tool for helping to enrich data while at the same time pushing data back out to the teams and leaders that need it the most. This tool helps streamline the process of not only acquiring and enriching data but also providing data in operational ways that can help make real-life data-driven decisions a reality.
How Does Reverse ETL Work?
ETL stands for Extract Translate and Load and is one of the most commonly used methods of transferring data from a data silo to a data warehouse. The data warehouse is one of the most important aspects of a modern data stack. This is where data has been translated into a uniform format and is held for an entire company.
The only problem that can occur, is that data within a warehouse can take on several characteristics of a silo, namely that it can be hard to access. Data also needs to be enriched and can be enriched through interactions, however, this process also seems to rely on the skill of engineers and scientists.
Reverse ETL brings another solution that can save departments time and money while pushing their data analytics forward. As the name implies, data that has been loaded into a warehouse via ETL is now extracted out of the warehouse and pushed to the end tools that your teams use every day. This is what is known as data activation.
Data activation is all about taking the source of truth that lives in the warehouse and making it actually actionable and accessible to the teams that need it. Not only that, but because reverse ETL acts like an all-in-one data acquisition tool, it can make automation a breeze. All the data that a company needs lives inside a data warehouse, which is why reverse ETL can make this accessible through SQL.
While SQL is a skill to learn, it’s a skill that can easily be taught to department heads and leaders and improve the way they pull valuable data that they need directly to their department.
Becoming a great data team member means knowing how to get data out to the people that need it. With reverse ETL, this can be something that is streamlined to the point of revolutionizing a company’s relationship with data and improving data-driven decision-making.