AIQ: What Data Engineering Means for You

Synaptiq has spent the last decade studying data and artificial intelligence (AI) across a wide range of industries. We’ve consulted with clients, conferred with partners, collaborated with competitors, and conducted research to understand how and why organizations leverage these technologies.

Our ultimate takeaway?

AIQ: a novel approach to data and AI, centered on 11 key capabilities shown to facilitate successful workflow integration and return on investment (ROI).

This blog post will deep-dive into one of the 11 capabilities: Data Engineering. We’ll discuss what it is, how it’s done (when it’s done right), and why it matters. Or, you can read an overview of AIQ, including all 11 capabilities, in our blog post, “AIQ: What We Mean & What You Stand to Gain."

What is Data Engineering?

Data Engineering is the practice of developing software for data collection, storage, and analysis with the ultimate purpose of value generation at scale. To put things simply, Data Engineers design, develop and optimize the flow of data within and between an organization's systems. They serve as the glue between application development and Model Selection and Training. How? They process internal data or data acquired via Data Sourcing through the standards and blueprints defined by Data Architecture & Governance to fulfill the requirements outlined by Data Product Management for ready-to-use applications.

Data Engineering Done Right

The nature of an organization’s data and its applications are always-changing, and Data Engineering must follow suit. One department might use “ABC” systems to collect, store, and analyze data; another department, “XYZ” systems. That’s ok. In fact, it’s good. It’s rational for an organization to tailor its tools to its unique resources and objectives. 

That said, there are some core requisites of Data Engineering for all organizations:

• Tools. Data Engineers need to have experience with modern data-centric tools needed to move, ingest, store, query, transform, and clean data to support analytics and data modeling.

• Practices. The Data Engineering team needs to have defined practices for designing and building systems that collect, store, and analyze data at scale.

• Programming. Data Engineers need to have experience with programming languages such as SQL, Python, R, Scala, and Julia to support analytics and data modeling.

Why Data Engineering Matters

Data Engineering is one of the fundamental pillars of data maturity.  It makes data useful and accessible for consumers: employees, partners, customers, etcetera. Without it, organizations cannot scale efficiently because data “flow” is non-existent, problematic, or sluggish between systems. So, an organization without Data Engineering is an organization without the means to compete. In other words, it’s severely handicapped.

You can learn about Data Engineering and how it fits into AIQ by reading our blog. Or, take our AIQ assessment to determine where your organization stands for each of the 11 capabilities.