Data-Driven Workflow Improvement

Ever wonder why we tend to solve problems the hard way? 🤔 The key is in how we connect the dots. A cancer hospital was facing a major challenge. Patients, often anxious, needed timely care without added delays. Doctors relied on quick access to medical images to make this possible. For most hospitals, loading images within three seconds is the standard. But cancer patients often have extensive imaging records, making this target a significant challenge. This created escalating pressure in an environment that's already stretched to its limits The hospital consulted several firms. They all suggested the same thing: a costly network upgrade that would disrupt daily operations and inconvenience patients even more. The proposed solution was out of the question, the hospital needed something affordable that wouldn’t disrupt patient care. A consulting firm graciously recommended me for the task. I saw the problem from a different angle. IT experts looked at the network. But as a Health Informatician, I focus on using data and technology to design health services that support optimal care delivery. Instead of waiting for doctors to request images, why not load them in advance? By preparing the images during the patient’s wait time, we created a seamless workflow without costly upgrades. The results were immediate and impactful. 😊 The hospital easily met the three-second target, and patients noticed the improvement with shorter wait times. The cost savings were substantial, all without any disruption to care. "Adam, you literally performed magic!” shared the hospital’s clinical operations lead. Sometimes, the simplest solutions make the biggest difference. The key was understanding how health services connect and using technology to support these connections. These days, as a digital health transformation coach, I continue to co-design sustainable, human-centered innovations that improve how information is used to advance health outcomes. Ever found a simple solution to a complex challenge? I’d love to hear your insights and share approaches that make an impact. #HealthcareInnovation #LeadershipLessons #DigitalTransformation

🚀 ABCs of Data Engineering: E is for Efficiency in Data Pipelines Diving deeper into the ABCs of Data Engineering, we've hit 'E' for Efficiency. It's not just about speed; it's about how you, as a data engineer, optimize resources, scale your systems, and maintain the reliability of your data processes. ▶ Choosing the Right Tools: Your toolbox matters. Picking the right technologies for each part of your data pipeline, like Apache Kafka for real-time streaming and Apache Spark for processing, can significantly improve your workflow's efficiency. ▶ Optimizing Storage: Keeping only the necessary data not only cuts down on costs but also speeds up processing. Your approach to data retention plays a critical role in keeping your storage efficient and your pipeline streamlined. ▶ Automating Processes: Automating routine tasks in your pipeline, like checking data and managing errors, not only makes your work faster but also minimizes the chance of mistakes. Tools like Apache Airflow are lifesavers, automating complex workflows and making your life easier. ▶ Ensuring Flexibility and Scalability: Building your pipelines to be adaptable and scalable from the start means you're ready for growth without needing a complete overhaul later on, saving you time and resources in the long run. ▶ Continuous Testing and Optimization: Having someone else test your pipeline can uncover things you might have missed. Coupled with ongoing performance monitoring, this ensures your pipelines stay efficient as data volumes and complexities evolve. ▶ Improving Compute Use: In your data pipelines, using compute resources wisely can make a big difference. For instance, when you're merging a big dataset with a much smaller one, using broadcast joins can avoid unnecessary data movement and the it does not have to shuffle data around too much. This method is particularly efficient when there's a considerable size difference, as it broadcasts the smaller dataset to all processing nodes. Another strategy is sort and bucket joins. Here, you organize your data in a certain way before you start working with it. By sorting and grouping data into buckets, you make it easier for your system to work with the data. It's like setting up your workspace before starting a project, making everything run more smoothly and quickly. Efficiency is the key to turning large datasets into actionable insights quickly, giving you a competitive edge. 🔄 Over to You: How have you optimized efficiency in your data pipelines? Have you tried these methods, or do you have other tricks up your sleeve? Let's share our experiences and learn from each other. #DataEngineering #ABCsofDE #Efficiency #DataPipelines

𝗠𝗮𝗽 𝗧𝗼𝗱𝗮𝘆’𝘀 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲 𝘁𝗵𝗲 𝗣𝗮𝗶𝗻 𝗣𝗼𝗶𝗻𝘁𝘀: 𝗨𝗻𝗰𝗼𝘃𝗲𝗿 𝗛𝗶𝗱𝗱𝗲𝗻 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻 𝗕𝗲𝗳𝗼𝗿𝗲 𝗔𝗱𝗱𝗶𝗻𝗴 𝗧𝗲𝗰𝗵 Every successful transformation starts by seeing your current state with crystal clarity. Too often, we rush to evaluate software features before understanding how work really flows and where it grinds to a halt. Imagine treating your processes like a road trip: you wouldn’t choose a new vehicle until you know which roads are blocked. The same goes for systems. A mid‑market manufacturer struggled with late shipments. Leadership blamed their ERP’s lack of functionality, but frontline teams knew the truth: manual handoffs and conflicting spreadsheets created bottlenecks. In addition, 40% of delays stemmed from manual cross‑checks between dispatch and finance, a step invisible on org charts but glaring on the shop floor. By facilitating honest, workshop‑style mapping sessions (complete with sticky notes and whiteboards), they uncovered redundant approvals and invisible handoffs that no feature list could solve. Involving the people who do the work isn’t optional; it’s essential. Their day‑to‑day insights highlight subtle delays, workarounds, and “exceptions” that hide in plain sight. An unbiased facilitator ensures every voice is heard and prevents solutions from being biased by existing hierarchies. The result? A process map that reveals root causes, not just symptoms, and creates a shared baseline for improvement. By critically analyzing your current state, you build a precision roadmap: automate the highest‑impact tasks, redesign workflows to remove dead ends, and close compliance gaps before they escalate. This targeted, human‑centric approach avoids wasted investment, earns frontline trust, and lays the groundwork for sustainable process improvement.    Once you’ve charted reality, you can make targeted changes, whether that’s simplifying an approval step, automating a data transfer, or selecting a tool that fits the way your teams operate. This honest approach prevents costly rework and builds trust across the organization. Ready to uncover hidden friction and chart a focused transformation path? With Digital Transformation Strategist, let’s discuss how a structured pain‑point diagnosis can drive your next wave of operational excellence. #digitaltransformation #operationalexcellence #processimprovement #processmapping #changemanagement

Academic research moves slowly—until it doesn't. At Northwestern, I faced a data nightmare: 15 separate longitudinal studies, 49,000+ individuals, different measurement instruments, inconsistent variable naming, and multiple institutions all trying to answer the same research questions about personality and health. Most teams would analyze their own data and call it done. That approach takes years and produces scattered, hard-to-compare findings. Instead, I built reproducible pipelines that harmonized all 15 datasets into unified workflows. The result? 400% improvement in research output. Here's what made the difference: ➡️ Version control from day one (Git for code, not just "analysis_final_v3_ACTUAL_final.R") ➡️ Modular code architecture—each analysis step as a function, tested independently ➡️ Automated data validation checks to catch inconsistencies early ➡️ Clear documentation that teams could actually follow ➡️ Standardized output formats so results could be systematically compared The lesson: I treated research operations like product development. When you build for scale and reproducibility instead of one-off analyses, you don't just move faster—you move better. This approach enabled our team to publish coordinated findings on how personality traits predict chronic disease risk across diverse populations. The methods we developed are now used by multi-institutional research networks. The mindset shift from "getting it done" to "building infrastructure" unlocked value that compounded across every subsequent analysis. Whether you're working with research data, product analytics, or user behavior datasets, the principle holds: invest in the pipeline, and the insights flow faster.

Unlocking Excellence in Hospital Operations with Data-Driven Insights In the complex world of healthcare, where every second counts and resources are stretched thin, data-driven decision-making is a game-changer for hospital operations. By leveraging data to track key performance metrics, hospitals can uncover inefficiencies, optimize workflows, and deliver superior patient care. Inspired by Lean principles, this approach fosters a culture of continuous improvement that transforms challenges into opportunities. Let’s dive into how data can revolutionize hospital operations and drive meaningful change. Why Data Matters in Healthcare Data acts as a clear lens, illuminating the inner workings of hospital processes. By systematically tracking metrics like patient wait times, bed turnover rates, and medication error rates, administrators and clinicians gain actionable insights into inefficiencies. These insights enable hospitals to prioritize improvements that enhance patient outcomes, reduce costs, and improve staff satisfaction. The key is moving from reactive fixes to proactive, data-informed strategies. Key Areas Where Data Drives Impact Optimizing Patient Flow Bottlenecks in patient flow—such as delays in lab result processing or slow discharge procedures—can frustrate patients and strain resources. By analyzing admission-to-discharge data, hospitals can pinpoint where delays occur. For example, one hospital discovered that lab result delays stemmed from manual data entry. By automating this process, they cut turnaround times by 25%, improving patient satisfaction and freeing up staff for other tasks. Streamlining Resource Management Overstocked supplies tie up capital, while shortages disrupt care. Data on supply usage patterns helps hospitals maintain optimal inventory levels. For instance, tracking bandage or IV fluid consumption can prevent over-ordering, saving costs without compromising care quality. One healthcare system reduced inventory waste by 15% through data-driven forecasting, redirecting savings to patient care programs. Enhancing Staff Scheduling Understaffing during peak times or overstaffing during lulls can harm efficiency and morale. By analyzing patient volume data, hospitals can align staffing plans with demand. For example, an ER department used historical data to predict busy periods, adjusting nurse schedules to ensure adequate coverage. This reduced wait times by 20% and eased staff burnout. Building a Data-Driven Culture To maximize impact, hospitals must integrate data into daily operations: - Engage Frontline Staff: Train nurses, physicians, and administrators to interpret data and suggest improvements. A nurse’s insight into workflow hiccups can spark transformative changes. - Conduct Regular Reviews: Monthly or quarterly data reviews keep teams focused on continuous improvement, ensuring gains are sustained and new inefficiencies are caught early.

Claude is quietly changing how analysts work - from manual steps to intelligent workflows. Data analysis is no longer just about writing queries. It’s about building systems that think, plan, and execute with you. This breakdown shows what that actually looks like in practice. Instead of jumping straight into queries: → Claude plans the entire analysis before execution Instead of struggling with messy data: → It auto-loads CSV, Excel, JSON, and understands schema instantly Instead of switching tools constantly: → It connects directly to databases, sheets, and warehouses Instead of writing everything from scratch: → It runs Python, SQL, and bash in real time and iterates with you Instead of static reports: → It generates charts, dashboards, and full reports automatically And where it gets really powerful: → Tracks your transformations and lets you rewind anytime → Saves workflows so you can rerun full analyses in one command → Uses specialized sub-agents for SQL, stats, and visualization → Converts one prompt into complete outputs (Excel, PPT, PDF, docs) This is the shift happening: From analyst → to AI-powered decision engine The people who adapt to this workflow will move faster, test more ideas, and deliver better insights.

Here’s the ML monitoring / post-deployment data science workflow with some detail. It’s all about maximizing the time the model performs well and minimizing (or eliminating) negative business impact due to data drift. It starts after the model has been deployed to production and goes as follows: 1. Model performance monitoring. We need to know it before model predictions are used; otherwise, a drop in model performance will have negative business consequences. The key point here is estimating model performance. In other words, estimating the impact of data drift on model metrics. If we somehow get the targets before the model predictions are used, we can monitor realized performance, but that’s a rare situation. If the performance is consistent, we’re done; go back to point 1. If we observe the performance drop, go to point 3, the root cause analysis. 2. Root cause analysis. Here, we use three families of tools and algorithms: ▪ Concept drift detection. This tells us if the concept - P(y|X) has changed. We need ground truth to do that, so we will generally look at past data. ▪ Covariate shift tells us if the joint model input distribution has changed and if so, in what way. Here, we leverage both univariate and multivariate drift detection tools. ▪ Data quality. Data quality is often the underlying root cause of change in model performance. 3. Issue resolution. Each action directly corresponds to the root cause we determined earlier: ▪ Concept shift is the root cause → Retrain your model ▪ Covariate shift is the root cause → Adjust the decision thresholds and business processes. Thresholds should be adjusted to protect critical metrics, such as keeping the false positive rate under a certain level. In addition, adding a “not sure” model output that’s not acted upon without human supervision is the best way to protect the business impact. Retraining will not fix performance drop due to covariate shift, as the model still captures the concept correctly. ▪ Data quality is the root cause → Investigate data collection processes and the preprocessing pipeline. Typically, changing the schema of one of the data sources is the problem. Return to performance monitoring, evaluate if the issue was resolved successfully, and continue monitoring. This flow ensures model issues are discovered, understood, and resolved quickly, protecting the model's business impact.

I recently unearthed documentation from a 2018 study on McDonald's Drive-Thru performance. I was struck by just how much has changed in the way we work. The artifact is a snapshot of our typical data science workflows at the time: -Data Extraction: SQL queries run directly on AWS Redshift to pull drive-thru transaction data. -Local Processing: Data downloaded as flat files and analyzed in R Studio on a desktop. -Simulation Logic: Manual sampling and iteration loops coded in R to simulate customer behavior changes. -Performance Bottlenecks: Long runtimes (e.g., 6 hours for 100 iterations), limited scalability, and manual file handling. -Collaboration: Mostly siloed—hard to share reproducible workflows or scale across teams. Compared to how we're operating today: - Data Access: Unified access to Redshift, Oracle, Delta Lake, and other sources. - Cloud-Based Processing: Python and PySpark enable distributed computing, dramatically reducing runtime. - Scalable Simulation: Vectorized operations, ML libraries (e.g., scikit-learn, pandas), and parallelization make simulations faster and more robust. - Automation & Reproducibility: Version-controlled notebooks, scheduled jobs, and CI/CD pipelines for our production workflows. - Collaboration: Real-time co-authoring, commenting, and dashboarding across global teams. This ongoing transformation is one of the ways we're turning our data into a winning advantage. We still have a long way to go, but I'm proud of how far we've come. #AcceleratingTheArches #DataScience #Databricks #DigitalTransformation #McDonalds #DriveThru #Python #CloudComputing #MultiCloud

While data producers—like data engineers—have benefitted significantly from applying engineering principles, data consumers have been largely left behind. Data teams have been building infrastructure, creating sophisticated pipelines and data models to make data reliable and accessible. But for the majority of users, even with these technical advancements, data consumption still remains a fragmented and inefficient experience. We need to apply a different set of engineering principles designed specifically for data consumers. Here are some ideas: 1) No-Code Abstractions The first principle for data consumers is the need for no-code abstractions. If we're still exposing unnamed datasets and reams of sql code with custom logic, it's impossible for business users to consistently derive value from their data. Metrics and metric relationships must become the new units of abstraction for business users. By utilizing metric trees, we can structure data into abstractions that align with how the business works, and how the business users naturally conceptualize, think and act. 2) Out-of-the-Box Calculations and Algorithms But simply having structured data isn't enough. We also need out-of-the-box calculations and algorithms to support common questions and workflows for end users. Data consumers often have to resort to manual computations or spend excessive time trying to answer repetitive business questions. To address this, we must provide ready-made calculations and algorithms that are tailored to common business needs like revenue tracking, retention analysis, or customer segmentation. 3) Clear Definition of Common Workflows Even with no-code abstractions and built-in calculations, the process won't work unless we clearly define common workflows that users follow. Business reviews, metric root-cause analysis, variance analysis against budgets, pacing metrics—these are all common workflows that data consumers engage in. By defining the scope of these workflows, and tools built with these in mind, we will make it significantly easier for users to quickly jump into their work without struggling to piece together these workflows from scratch every time. 4) Workflow Automation Finally, to truly unleash the power of data for consumers, we need to integrate workflow automation into the process. The goal should be for the software to do the work proactively. Imagine systems that automatically flag anomalies, surface insights, and highlight areas where actions are needed. In summary, if we are to truly empower data consumers across an organization, we must rethink how we design data systems and workflows. By focusing on no-code abstractions, providing out-of-the-box calculations, defining common workflows, and automating these processes, we will transform data consumption into a streamlined, actionable and delightful experience that drives better business decisions and outcomes.