The transformative impact of data and data-driven insights has never been more clear. But unlocking insights involves nagging fixed costs we can never seem to do away with. Namely, it takes time and resources to prepare data for AI & analytics. By some estimates a staggering 80% of an organization's time is spent cleaning and preparing data¹, leaving only 20% for the actual analysis and generation of business insights.

Data engineers are the unsung heroes, doing all the heavy lifting when it comes to transforming data. This makes sense, since it’s usually the data engineers that have the specialized coding skills required to transform raw data into a usable format. When faced with increasing demands from the business it's tempting, then, to default to simply hiring more data engineers. But is this the right answer?

The problem of scaling with more data engineers

One of the biggest problems with this approach is a scarcity of data engineering talent, which creates a competitive environment for organizations seeking to hire skilled professionals. But with the demand for data engineers expected to grow another 21% by 2028², it’s going to get even harder to find talent. No one is immune as this affects businesses of all sizes and maturity levels. 

The supply-demand imbalance for data engineering expertise means that salary requirements for these roles are only going up, making it even harder for many organizations to retain as well as attract top talent. 

Further compounding this issue is attrition. When experienced data engineers move on to other roles or worse, leave, the institutional knowledge they take with them is often lost. It can be time-consuming for new hires to decipher code, which is usually where all that knowledge lives. This results in significant and expensive onboarding delays that limit new engineers' ability to immediately contribute.

Do you want to know the hard truth? Even if you’re able to hire all the data engineers you want, there is no promise that this will actually solve your data challenges. As demand for business data continues to grow, you’ll find yourself with the same data preparation and transformation needs. And you’ll find yourselves overly dependent on data engineers.

Data engineers will remain a bottleneck, limiting decision makers’ ability to get trusted data quickly. Equally important, the time data engineers spend on preparing and transforming data means time spent away from higher value tasks like:  optimizing infrastructure, ensuring data quality, and building standards.

All of this combined points to a model that is broken and highlights the need for solutions that move beyond simply hiring more data engineers. This is where the idea of self-service data transformation emerges as a game-changing alternative.

The answer is a self-service solution for all data users

Instead of adding headcount to address your data engineering challenges, organizations should explore the potential of a self-service data transformation solution. This would allow for the democratization of data engineering (i.e., citizen data engineering), allowing a broader range of users to participate in data transformation.

Fundamental to a successful self-service solution are visual development and low-code tools that can meet business users where they are. By adopting these types of user-friendly interfaces, business users can transform data without needing extensive coding expertise. This allows them to work directly with the data relevant to their area of focus, fostering collaboration and reducing dependence on the central data engineering team.

Prophecy as an alternative for democratizing data engineering

Prophecy’s low-code data transformation platform delivers a powerful, no-compromise solution that removes the complexities of data engineering. 

By standardizing data operations, Prophecy enables all data team members to collaborate and build performant and reliable data pipelines. Prophecy’s visual, low-code development interface automatically produces high-quality open-source code that can be easily reviewed, optimized, and debugged as needed.

Additionally, Prophecy plays a critical role in enforcing software best practices. By seamlessly integrating with functionalities like Git for version control, automated testing, and continuous integration and continuous delivery (CI/CD), you can be confident that data pipelines are built with maintainability and scalability in mind. This not only reduces the risk of errors but also fosters a collaborative, transparent, and productive development environment. By automating repetitive tasks and streamlining data pipeline development, this frees up valuable time for data engineers to focus on more complex and strategic initiatives. 

By adopting Prophecy, organizations can realize the power of data across their teams - without having to hire yet more data engineers.

Organizations that have succeeded by choosing Prophecy

The choice to hire more data engineering resources versus pursue a self-service solution is one that many organizations face. In the case of the Texas Rangers baseball operations team, their decision to move forward with Prophecy was simple. The team was struggling with trying to scale their data engineering resources to support their analysts and help them get the player insights they needed to put the highest quality product out on the field. They turned to Prophecy and have not looked back. Productivity has greatly improved now that their analysts are capable of building high quality data pipelines themselves quickly through Prophecy’s visual, drag and drop interface. The results have been impressive with a 7x increase in pipeline development without the need for more data engineers. 

Another organization that opted for Prophecy once they tried to achieve their goals by hiring more data engineering resources is Waterfall Asset Management. As a global investment management firm they are highly dependent on data insights to improve investment performance and mitigate risk for clients. In order to keep up with the demands of the business, the operations team was hiring new data engineers. Unfortunately, this ended up slowing workflows and impacting data quality due to the time required to onboard these new engineering resources. Waterfall chose Prophecy’s low-code data engineering platform in order to give their business users intuitive, self-service tooling to visually transform data without needing to code or depend on engineering. Equipped with a low-code data platform that all of their data users could use, Waterfall fast-tracked data engineering workflows, reducing the time to prepare and transform data from up to 3 weeks to about ½ a day — a 42x performance gain.

Invest in self-service, with Prophecy

The need to transform raw data into actionable insights can lead organizations to the seemingly obvious choice of simply hiring more data engineers. But as we’ve seen, this choice brings with it a long list of challenges, not the least of which a variety of higher costs that outweigh the potential benefits of this approach. Keep in mind that companies currently spend, on average, $520k every year paying data engineers to manually build and maintain data pipelines³. 

A more sustainable strategy lies in adopting Prophecy, which is a cloud-native, low-code data transformation platform. Prophecy meets business data users where they are, making them more productive, while also easing the burden on data engineering. With its visual interface, automated workflows, and built-in best practices, Prophecy is designed to increase productivity of all data users, from pipeline development to insights delivery.

By moving away from an over reliance on specialized data engineering expertise and towards democratization of data engineering tasks, organizations can unlock the true potential of their data and gain a competitive advantage - all while reducing costs.

Resources:

1. https://techcrunch.com/2021/08/19/companies-betting-on-data-must-value-people-as-much-as-ai/ 
2. https://www.zippia.com/data-engineer-jobs/trends/ 
3. https://get.fivetran.com/rs/353-UTB-444/images/2021-CDL-Wakefield-Research.pdf