Data Engineering in Process Development

Data engineering is a crucial component of the modern data-driven world and its role in process development cannot be overstated. In order to succeed, process development requires capabilities to measure, analyze and come to the right conclusions in the right situation. All this is heavily dependent on connected high-quality data.

These claims are easy to make but not always self-evident in practice. Fortunately, our sister company Vuono Group happens to know a thing or two about developing and automating business processes. Together, we explored the significance of data engineering in process development and how it helps organizations gain insights and make better decisions.

Starting with the basics

In state-of-art organizations, process development is a continuous activity taking place at all levels. The process improvement and design initiatives are based on real-world data that processes generate, and the business and financial benefits are easy to validate with data. However, only a few organizations have achieved the state. Companies struggle to find the data, and often it is not trustworthy. Therefore, many companies rely on project-by-project data collection efforts.

Data engineering is the process of systematically collecting, storing and processing data to make it usable for analysis. In practice, it involves the use of various tools and technologies to transform raw data into a format that can be easily consumed by data analysts and data scientists and essentially help end users in their daily operations and work for a better, more optimized future. In the context of process development, data engineering plays a critical role by providing the necessary infrastructure to collect, store, and process data generated by different processes.

In other words, data engineering enables proper process development.

One of the key benefits of data engineering is the ability to automate data processing. By automating data processing, organizations can reduce the time and effort required to analyze data. This, in turn, allows organizations to make decisions faster and more efficiently.

Another key benefit of data engineering in process development is the ability to integrate data from different sources. Data engineering allows organizations to bring together data from various systems and applications, making it easier to analyze and gain insights. This integration of data can lead to better decision-making and improved organizational performance.

In addition to the different abilities that data engineering brings to process development, there are two components that could be seen as hygiene factors in all data work: The quality and security of data. By implementing data validation and cleaning processes, data engineers can ensure the data used is accurate and reliable. This strengthens the reliability and quality of decisions that process development is built on.

Data engineers are also responsible for implementing security measures to protect sensitive data from unauthorized access or misuse. This includes ensuring compliance with data privacy regulations. To conclude, data quality and security are directly linked to the trustworthiness of data. After all, there is no use in praising data-based activities including process development if the data itself is corrupted or plain misleading. 

What makes the difference?

Alright, now that we have gotten the obvious stuff out of the way, it’s time to dive a bit deeper. Elemental technical aspects of data engineering are of course crucial in designing, building, and maintaining data systems. What differentiates this from experienced data engineering, however, is the ability to go beyond technical proficiency. In the context of process development, this differentiates time and cost savings case by case from implanting continuous development of processes as a part of a company’s everyday routines and making sure different functions are synced. As mentioned above, process development is ideally a continuous activity taking place at all levels. This requires an understanding of these levels and how they are linked to one another.

As our Data Architect Sami Helin once put it, talking data through systems enforces siloed data thinking and distracts communication. It also switches business focus from making data-related business problems and objectives tangible. The focus is then on understanding systems and technologies – not necessarily what would be best for the business and stakeholders in reality. 

While Sami’s thoughts stem from the larger context of data-driven business, it is applicable to data engineering in process development as well. As processes become more complex, so does the need for a broader understanding of data engineering. Thinking outside the box is one hell of a cliché but coming up with creative solutions for complex data challenges is something that truly impacts the overall development of processes.

Experienced data engineers grasp the business context in which data is being used and can guide the development in the right direction. They are able to design systems that meet technical requirements (some of which were listed in the previous section) but also align with the strategic goals of the organization. They also have a deep understanding of the data and the sources it comes from which they also can communicate to different stakeholders in a way that is easily understandable.

Furthermore, bridging the gap between business needs and systems requires understanding business terminology and being able to turn requirements into data solutions. In addition to the larger context, effective data development requires good communication and alignment with business language.

It is easy to agree that processes should exist for a need, not just for the fun of developing processes. Similarly to this, it can be argued that building data platforms with all the bells and whistles “just because” is a waste of time and energy. 

Moving forward

In conclusion, data engineering plays a vital role in process development by providing the necessary infrastructure to collect, store, and process data generated by different processes. But it does not stop there. Data engineering also allows organizations to automate data processing, integrate data from various sources, ensure data quality and security, and make informed decisions based on data insights.

The level at which organizations can reach these aspects is highly linked to the ability of their data engineers to understand the business realities they operate in. All in all, we can clearly see that as the importance of data-driven decision-making continues to grow, the role of data engineering in process development will become increasingly critical.

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