As a data architect navigating the relentless churn of tools, architectures, and hype in our industry, I’ve had my share of challenges. The explosion of options – from On-Premise Databases to Cloud Data Platforms, Iceberg formats to Spark alternatives – creates architectural chaos.
One day you’re untangling legacy Extract, Transform, Load (ETL) nightmares like SAS code bloated with redundant ingestion and one-off data quality hacks; the next, you’re evaluating yet another “revolutionary” framework that promises to fix everything but often just adds to the backlog.
It’s exhausting: Overwhelmed teams, vendor lock-ins, and the constant pressure to scale without breaking the bank or Service Level Agreements.
Take the ENL (Extract, Normalize, Load) approach outlined in “Rethinking Data Movement: A First Principles Approach” by Animesh Kumar and Darpan Vyas, published on the Modern Data 101 Substack. It’s a solid rethink of data ingestion, emphasizing incremental-first pipelines, CDC for real-time sync, API parity, baked-in observability, and extensibility to avoid bottlenecks. These principles address the “triple squeeze” of input complexity, timeliness demands, and cost pressures that plague legacy batch jobs. But in practice, such left-shifted rigor can feel overly engineering-focused, risking upfront perfectionism that delays value.
That’s where insights from real-world CDOs like Malcolm Hawker resonate deeply. In “The Data Hero Playbook,” Hawker urges treating data as a P&L, quantifying its direct impact on revenue, cost savings, or risk reduction to position it as a strategic asset. He advocates balancing “shift right” for quick wins (delivering messy but immediate KPIs to build trust) with “shift left” for AI reliability (foundational controls like data contracts). This customer-obsessed mindset overcomes limiting “villain” habits, fostering collaboration and turning data into a business driver rather than a cost center.
The good news? At evolv Consulting, we cut through this noise to guide clients toward sustainable success. By starting with business-aligned MVPs for fast ROI, then layering in principled architectures like ENL-inspired pipelines, we help overwhelmed teams escape the hamster wheel, delivering reliable results without the chaos.
What frustrations are you facing in your data journeys? Let’s connect and share strategies!
