Building Future-Ready Data Architectures: The Power of the “Done Once, Done Right” Mindset in Data Engineering
As data engineering rapidly evolves, we’re confronted with a familiar tension: deliver quickly or deliver correctly?
In our rush to meet deadlines, the “quick fix” is often prioritized, but this mindset is inherently shortsighted. We must shift toward a “done once, done right” mentality—a principle that focuses on building data architectures robust enough to not only serve today’s needs but also scale for tomorrow’s innovations like machine learning (ML), artificial intelligence (AI), and real-time streaming through Kappa architectures.
What Does “Done Once, Done Right” Mean?
At its core, “done once, done right” embodies a disciplined approach to data engineering. It requires thoughtful upfront planning, adherence to best practices, and a commitment to future-proofing our solutions.
This mindset does not reject speed outright; rather, it focuses on minimizing long-term inefficiencies and technical debt by investing time and effort in designing systems that are scalable, reliable, and flexible from day one.
In an era of exponential data growth, healthcare organizations cannot afford to revisit their architecture every time a new challenge arises. By adopting this mindset, we ensure that our solutions evolve as needs shift, avoiding the costly cycle of rework that can cripple data teams in critical industries.
The Importance of Shifting from Reporting to Innovation
Traditionally, data engineering in industries like healthcare has focused heavily on building for reporting and business intelligence. However, the landscape is shifting. Now, the focus is on enabling advanced analytics and predictive insights through ML and AI, where the quality and structure of data pipelines are foundational.
It’s not just about historical reports anymore; it’s about using real-time data to drive immediate, life-saving decisions.
In this context, a “done once, done right” approach doesn’t just impact efficiency—it impacts outcomes. For example, the ability to stream real-time patient data and feed it into ML algorithms can enhance early detection systems, potentially saving lives by predicting complications before they escalate. This is the future we should be building toward.
The Core Pillars of “Done Once, Done Right”
1. Quality Over Speed
In healthcare, the integrity of data can affect treatment outcomes. When working with systems like Epic and integrating with Snowflake using tools like Fivetran, we must ensure that every step—from extraction to transformation to load—is carefully calibrated.
Rushing to meet short-term deadlines may deliver reports faster, but at what cost? Ensuring accuracy upfront reduces the risk of erroneous data entering predictive models or clinical dashboards.
2. Comprehensive Planning with Future Architectures in Mind
When architecting our data flows, particularly from legacy systems like Epic into Snowflake, we need to think beyond immediate reporting needs.
The shift toward real-time architectures like Kappa must begin with foundational planning. Start by understanding which parts of your data pipeline need to be real-time and which don’t. Build with modularity in mind so that adding real-time elements later on doesn’t require re-engineering the entire system.
3. Building for ML and AI from Day One
AI and ML aren’t just buzzwords—they are powerful tools that transform raw data into actionable insights.
Designing data models and pipelines that efficiently feed into ML workflows should be part of the initial build. This includes:
Robust data cleansing
Enrichment processes
Feature engineering baked into the pipeline
For example, ensuring that healthcare data adheres to HIPAA while still enabling ML experimentation is a balancing act—one that must be considered from day one.
4. Documentation and Governance as Key Enablers
Documentation is often an afterthought, but in “done once, done right,” it becomes a pillar.
Comprehensive documentation ensures that future team members or stakeholders can understand, replicate, or extend the work. In healthcare, where regulatory compliance is strict, clear data lineage and audit trails are non-negotiable.
Governance is equally critical. Creating robust policies for data management ensures that all parts of the pipeline are secure, reliable, and compliant—without sacrificing agility.
5. Prioritize Reusability and Automation
Reusability and automation are allies in the “done once, done right” mindset.
Building reusable components—whether for data ingestion or transformation logic—prevents reinvention and promotes scale. Automation brings consistency and reliability across the pipeline:
Real-time error handling
Automated data quality checks
CI/CD pipelines for models and infrastructure
Auto-triggered model retraining and deployment in ML systems
These investments allow your engineering team to scale without adding manual overhead.
6. Reducing Technical Debt: Invest Now, Save Later
Every time a quick fix is applied, technical debt accumulates.
While manageable in the short term, this debt eventually slows the system and stifles innovation. Teams end up spending more time maintaining legacy work than creating new value.
With the “done once, done right” mindset, we invest now—through testing, optimization, and architectural foresight—to save later.
Building Toward the Future with “Done Once, Done Right”
As we look to the future of data engineering—particularly in healthcare—the shift from reactive to proactive data systems is clear.
We no longer gather data just to report what happened. We now predict what’s coming. Whether it’s through ML models forecasting patient outcomes or real-time systems monitoring ICU metrics, the infrastructure we build today will determine how well we harness tomorrow’s technologies.
If we commit to a “done once, done right” approach—prioritizing long-term quality over immediate speed—we will not only improve our data systems but also create meaningful, impactful change in the healthcare outcomes of the patients we serve.
The data we engineer could be the difference between reactive and preventative care.
Let’s build with that in mind.
P.S.
This piece builds on ideas explored across my prior work:
In How ‘Who Moved My Cheese?’ Shaped My Approach to Data Leadership, I revisit a business fable that became a surprising cornerstone of my leadership philosophy.
In The Operating Manual: Leading with Standards, Not Ego, I argued that real leadership isn’t about comfort or compliance—it’s about setting the bar and building teams capable of rising to it.
In The HBR Framework Falls Short in Data and Engineering Leadership, I showed why soft approaches often collapse under the weight of technical complexity and misaligned incentives.
In Ambition Is Not the Problem, I defended the kind of intellectual drive and ownership mindset that brittle systems and legacy cultures tend to suppress.
In The Maginot Mindset: Why Data Organizations Fail in the Age of AI, I explored how historical patterns of over-engineered defense and underdeveloped adaptability are repeating themselves in how we approach tooling, governance, and strategy.
This isn’t just about architecture or tooling. It’s about survival.
You can’t build adaptive systems without adaptive people—and you can’t lead adaptive people with outdated thinking.