Data engineering is a term that is often thrown around synonymously with data science and advanced analytics and while it is often a step in analytical projects, it is also a concept of its own and a valuable competitive advantage. The availability of data is often a bottleneck within organizations and data engineering must to be a priority in order to create a sustainable and efficient analytics engine.

What is Data Engineering?

Data engineering is moving and transforming data from a source system into a form useful for analysis. This can vary in complexity from intricate and automated data pipelines that combine information from disparate systems across your organization, to manually joining two excel sheets. Most people involved in analytics have some experience and skills in data engineering, and mature organizations often create a specific group to manage this process company wide.

Data engineering is often used interchangeably with extract, transform, load (ETL) or data wrangling, but there are far more elements to it than just that. Data engineers are responsible for making valuable enterprise data available to the entire organization with the proper layers of access control. This can be accomplished through many means including creation and population of data warehouses, hydration of data lakes, building of data models and architecture, and leveraging streaming data to enable different types of real-time or alert based reporting.

There are many challenges that make data engineering a difficult and time-consuming effort. Most companies have disparate and disconnected systems from which the data originates in various formats. Often these are a combination of legacy and industry-leading systems that create their own difficulties in the frequency of data updates and the reliability of feeds. Data engineers also face data quality issues, which can have a direct impact on any analytics or models using it. A successful team must work closely with the business to identify these cases and perform validation while moving the data to the correct storage mechanism. Additionally, this requires coordination with security and organizational elements to maintain data privacy and access restrictions where needed. On top of all these efforts, the infrastructure created must be able to handle and scale to the large volumes of data that can flow through these pipelines and be able to process each set of records as required for analysis.

Data Engineering Role in Analytics and Data Science

Data engineering plays a crucial role throughout analytics projects as they work closely with analysts and data scientists to gather the appropriate data for reporting or machine learning. During the initial exploratory phase, data engineers ensure that analysts have access to the operational data and provide avenues to other sources of data required. For machine learning work, the data engineering group would be responsible for gathering the large volumes of data for modeling and provide insight into the feasibility of scaling a solution to production should the project proceed. In productionizing a solution, the data engineering group can play their largest role of ensuring the data flows reliably, all proper validations are in place, and the system can process and provide real time insights from the models developed by the data science teams.

It is often said that 80% of the time spent on machine learning projects is in preparing and manipulating the data. Having efficient processes and resources for accessing data is crucial to keeping those efforts from dragging on. If analytics projects are bogged down by data issues, it can be difficult to demonstrate the real value and impact analytics can have and endanger the investment; however, data engineering can be a valuable tool to uncover previously unknown trends.

For example, a consumer products client was looking into how they could help optimize their manufacturing processes and minimize loss on their production lines but lacked a central storage place for all relevant machine and quality data. With our guidance, this client was able to consolidate excel sheets, machine data, and other handwritten analysis into a single repository where they could then draw out valuable insights. Through this work, the team was able to establish baselines for manufacturing efficiency as well as identify sources of loss throughout the whole process. This value was proven over time, as the team was able to build the infrastructure so it could be used for regular checkpoints going forward to track improvements to that baseline and find further ways to maximize efficiency.

Another component of a mature data engineering team is taking data created through analytics and making it shareable back to the organization for efficient collaboration. Say an analytics team creates a useful data connection to pull in weather data to measure the impact of weather on consumer foot traffic. This weather data is likely useful in other analytics use cases throughout the organization, and the data engineering team can make that data accessible for other teams to build momentum.

How to Get Started

Data engineering is often a function of IT, but it needs to operate independently. Traditionally, IT’s focus is on applications and tools to help the business function more effectively, with data being a byproduct of each system. They are typically familiar with the data generated and have a good understanding of the integrations and touchpoints where data transitions between applications. However, this systems-first approach can fall short in addressing the broad challenges of providing data, reporting, and analytics capabilities to the entire enterprise.

Data engineering sees challenges differently. Their focus is solely on the data and enabling data access to the parts of the organization that need it the most. This requires dedicated resources that work across systems to form an enterprise wide view of data, defining the patterns of data delivery that most enable the success of the company and leveraging those throughout the organization to better enable business operations. Developing data dictionaries, establishing data governance procedures, and processes are other ways that data engineering can operate as a system agnostic steward of data throughout an organization. Being tool agnostic in data engineering is helpful in navigating today’s fast-moving environment of tool and capability evolution and M&A.

Conclusion

Data engineering in mature analytics organizations will play a role in most project work, as well as feeding self-service capabilities to the enterprise. Most organizations already have the right people within IT to start up a data engineering group but ensuring proper alignment on strategy and developing enterprise wide view of data are key in creating success. A great way to start this data engineering journey is to focus on a proof of concept project, showing real value to the organization overall and then expand the solution from there.

Co-author: Brandon Regnerus.