AI-ifying Business Processes
AI applications range from simple rule-based systems to self-learning models, to complex multi-agent systems that...
3 min read
Synaptiq has spent the last decade studying data strategy and AI readiness across sectors and industries. We’ve consulted with clients, conferred with partners, collaborated with competitors, and conducted research to understand how and why organizations fail or succeed in their data and AI endeavors.
We channeled our expertise into AIQ: an innovative framework for AI-ready data strategy and AI implementation, focusing on 11 critical capabilities proven to effectively enhance workflow integration and return on investment.
In this blog post, we'll explore in detail one of these 11 capabilities: Data Engineering. For a broader understanding of AIQ, refer to our blog post titled "AIQ: What We Mean & What You Stand to Gain."
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
An organization’s data and 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:
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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.
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Practices. The Data Engineering team needs to have defined practices for designing and building systems that collect, store, and analyze data at scale.
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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.
Photo by Aaron Burden on Unsplash
About Synaptiq
Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation.
Contact us if you have a problem to solve, a process to refine, or a question to ask.
You can learn more about our story through our past projects, blog, or podcast.