How to Streamline Analytical Workflows

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.

Recently helped a client cut their AI development time by 40%. Here’s the exact process we followed to streamline their workflows. Step 1: Optimized model selection using a Pareto Frontier. We built a custom Pareto Frontier to balance accuracy and compute costs across multiple models. This allowed us to select models that were not only accurate but also computationally efficient, reducing training times by 25%. Step 2: Implemented data versioning with DVC. By introducing Data Version Control (DVC), we ensured consistent data pipelines and reproducibility. This eliminated data drift issues, enabling faster iteration and minimizing rollback times during model tuning. Step 3: Deployed a microservices architecture with Kubernetes. We containerized AI services and deployed them using Kubernetes, enabling auto-scaling and fault tolerance. This architecture allowed for parallel processing of tasks, significantly reducing the time spent on inference workloads. The result? A 40% reduction in development time, along with a 30% increase in overall model performance. Why does this matter? Because in AI, every second counts. Streamlining workflows isn’t just about speed—it’s about delivering superior results faster. If your AI projects are hitting bottlenecks, ask yourself: Are you leveraging the right tools and architectures to optimize both speed and performance?

You spend 80% of your time cleaning data and 20% analyzing it. What if you could flip that ratio tomorrow? I've spent years in the trenches: building financial models, running due diligence, and creating complex operational reports. The story is always the same. You spend 90% of your energy on the mechanics—pulling, cleaning, and formatting data. By the time you're finally ready to do the actual analysis, you're too exhausted to think straight. This is one of the most powerful use cases for AI agents we're implementing for clients. We flip the ratio. Agents do the grunt work. Your team spends 80% of its time on high-value analysis and 20% on fine-tuning. The result? Faster, more consistent reports. But more importantly, your best people are focusing their (fresh) brainpower on strategy and insight, not VLOOKUPs. Here’s a simple 6-step agentic workflow for automating monthly reports: 1. 🗓️ The Trigger: A simple calendar event (e.g., the 1st of every month) kicks off the workflow. 2. 📥 The Data Pull: The agent automatically fetches data from all your sources (HubSpot, QuickBooks, your LMS, etc.). 3. 🧹 The "Clean & Map": It validates the data and maps everything to your master data source. 4. ✍️ The First Draft: An LLM (we've had great results with Claude 3.5 Sonnet) writes the full narrative report. 5. 👨💼 The Human-in-the-Loop: This is the most critical step. The agent Slacks the draft report and the data workbook to the department manager for review. Your new job: Review this draft with the same critical eye you'd use for a new Jr. Analyst's work. This is supervision, not data entry. 6. 🚀 The Delivery: Once approved, the agent sends the final, polished report to all stakeholders. Stop being a data janitor. Start being the expert. What's the one report in your company that "breaks" a team member for three days every month? #AI #Automation #AgenticWorkflows #DataAnalysis #FinancialServices #FinServ #Operations #Productivity #nuDesk

Use this data science tool to simplify your data cleaning. In a recent CRM project, I worked with two messy datasets: one on betting transactions, the other on customer demographics. I didn’t want dozens of scattered .𝗱𝗿𝗼��(), .𝗿𝗲𝗻𝗮𝗺𝗲(), or .𝗿𝗲𝗽𝗹𝗮𝗰𝗲() calls cluttering my notebook. So I used 𝗣𝘆𝗝𝗮𝗻𝗶𝘁𝗼𝗿, a tool that extends pandas with chainable, declarative cleaning methods. No magic, Just cleaner, more structured code. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝗶𝘁 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗺𝘆 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄: 📍 𝗖𝗼𝗹𝘂𝗺𝗻 𝗻𝗮𝗺𝗲𝘀, 𝘀𝗼𝗿𝘁𝗲𝗱: clean_names() standardised all my headers to snake_case in one line, no manual renaming, no special characters to worry about. 📍 𝗥𝗲𝗱𝘂𝗻𝗱𝗮𝗻𝘁 𝗰𝗼𝗹𝘂𝗺𝗻𝘀 𝗮𝗻𝗱 𝗲𝗺𝗽𝘁𝘆 𝗿𝗼𝘄𝘀 𝗿𝗲𝗺𝗼𝘃𝗲𝗱: remove_columns() helped drop the auto-generated index field, while remove_empty() took care of fully blank entries. 📍 𝗖𝗵𝗮𝗶𝗻𝗲𝗱 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝘃𝗮𝗹𝘂𝗲 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘀𝗮𝘁𝗶𝗼𝗻: With transform_column(), I cleaned gender values ("M" to "Male", "F" to "Female"), and corrected inconsistencies like "United States of America" → "United States". 📍 𝗔𝗹𝗶𝗴𝗻𝗲𝗱 𝗮𝗻𝗱 𝗺𝗲𝗿𝗴𝗲𝗱 𝗰𝗹𝗲𝗮𝗻𝗹𝘆: After renaming customer_id to cust_id, I merged both datasets confidently, the result was analysis-ready with no silent mismatches. 🎥 Below is a short screen recording of the entire cleaning pipeline to help you get started. It's clear, repeatable, and fully readable. ♻️ Feel free to repost if you’re tired of cleaning code that looks like a checklist of exceptions.

Let me share an example of the "90% automation trap" I talked about yesterday: differential gene expression analysis in RNAseq studies. Consider a typical RNAseq pipeline: raw data flows automatically through quality control, alignment, and expression quantification - maybe via a nextflow pipeline (think of ETL but for biological data). Then comes that crucial manual step: evaluating whether expression changes reflect true biology or technical artifacts. While tools like DESeq2 and limma calculate statistical significance, the final interpretation requires human expertise - distinguishing meaningful pathways from batch effects, identifying sample-specific anomalies that automated metrics can't really sufficiently address. This manual review typically takes quite some time. It is too experience dependent and also too critical to skip - misinterpreted expression patterns can lead to incorrect biological conclusions or wasted validation experiments. Here is a suggestion on how we could transform this necessary manual step from a hidden black box into a trackable, reproducible process: •  Create a structured decision tree for expression pattern evaluation •  Build a template that captures key decision points and reasoning •  Implement mandatory visualization archiving for each decision •  Version control these manual decisions alongside the code •  Make the human element explicit in your pipeline documentation The goal is to make the human judgement transparent and trackable. Think of it as version controlling your expertise, not just your code. In scientific publications, this interpretation of results is tracked by default - it is written in the methods, results and discussion sections. What parts of your analytical workflows that do not lead to publications (as in academic work) could benefit from a similar kind of structured human intervention tracking system? How would you design a system that embraces rather than hides the human element? #Analytics #DataScience #Reproducibility #QualityControl #ProcessDesign

Where is the analytics function headed in the next 5-10 years? It's worth exploring how the "data-as-a-product" movement could profoundly transform what is mostly manual, hand-crafted work today. The optimistic view is we finally realize dramatic productivity gains for data teams while empowering business users with no-code superpowers. Let's delve deeper into this idea. Firstly, adopting a "product mindset" involves breaking down the work into well-defined workflows. Analytics workflows essentially aim to answer four crucial questions in the business: 1. "What happened?" 2. The "why" behind the "what." 3. The "what-if" scenarios, such as what happens if trends continue or if we make certain decisions (X or Y). 4. Lastly, "what's next?" With these primary goals in mind, we can categorize the workflows into roughly ten key actions or "verbs," as illustrated in the first image: - Measure and track (related to "what happened"). - Review, analyze, and infer (pertaining to "why"). - Forecast, simulate, and plan (associated with "what-if"). - Debate and decide (part of "what's next"). The second table outlines how we currently execute these actions compared to an envisioned future state where we "productionize" these workflows. By generalizing, abstracting, and leveraging software, we can augment and automate what is predominantly manual work today. Some key highlights include: For the "measure and track" workflow: - We transition from creating custom datasets for building dashboards to working directly with a business metrics layer through APIs. - Taking it a step further, we envision managing the entire set of dashboards as code, enabling system-wide governance and streamlined maintenance. For the "review, analyze, and infer" workflows: - We move from cumbersome drill-downs within dashboards or painstaking, repetitive SQL queries to designing metric trees that capture metric connections and common drill paths. - Software then operates on these trees rapidly accelerating insights and the learning loops within the organization. - Business reviews become easier to orchestrate, and more fruitful, as the organization aligns and operates on inter-connected metric trees. For the "forecast, simulate, and plan" workflows: - Instead of building custom one off models in spreadsheets, we shift towards democratized no-code operations on top of metric trees that inherently understand business equations. - The cost of “what-ifs” is lowered so much that segment-driven planning becomes table stakes versus a luxury operation for the select few. I'm presenting a vision of the future, but its realization depends on both cultural and tooling shifts that promote the right abstractions and work patterns. As the modern data stack continues to evolve, and enduring principles of business modeling re-emerge, it opens up new avenues to accelerate analytical workflows fueled by the "data-as-a-product" mindset.

Still wrangling endless CSVs in your lab workflow? There's a smarter way: unify all your data with xarray. Curious how a single data structure can simplify everything? Read on. After years of managing experimental and machine learning data across scattered files and formats, I realized the cognitive load of keeping everything aligned was overwhelming. I started exploring unified data structures to reduce this friction. For example, I once spent days writing index-matching code just to keep my training data, features, and model outputs in sync across multiple files. It was exhausting and error-prone—one small misalignment could break the whole pipeline. This experience pushed me to look for a better, unified approach. Traditional lab data management means scattered files, mismatched indices, and constant manual bookkeeping. It's error-prone and exhausting. Inspired by a recent talk at SciPy, I built a synthetic microRNA study example to show how xarray can unify raw measurements, computed features, and model outputs in a single, coordinate-aligned Dataset—no more index-matching headaches. With xarray, you can store all your experimental measurements, computed features, statistical estimates, and even train/test splits in one dataset. Every piece of data knows exactly where it belongs—no more index juggling. In my latest blog post, I walk through this synthetic example step by step. The result? Cleaner workflows, bulletproof data consistency, and cloud-native scalability. If you're ready to reduce friction in your experimental data lifecycle, check out my blog post for a practical guide. Would love to hear your thoughts or experiences! https://lnkd.in/eXqGJB57 How are you currently managing complex experimental or ML data? Have you tried a unified approach like xarray? #datascience #laboratoryinformatics #machinelearning #xarray #bioinformatics

Design of Experiments only pays off when your data is trustworthy, connected, and ready to analyze.     Most teams don’t have a data problem. They have a context problem. Experiments cross people, sites, instruments and time, yet the data arrives fragmented. That invites errors, slows tech transfer and forces your scientists to clean data instead of learning from it.     What’s worked across complex pipelines is building a digital backbone that keeps process context attached to every sample and step. In practice, that looks like process-centric workflows, versioning of methods and materials, automatic sample IDs and lineage, QC checks against specs, and instant creation of analysis-ready data frames. When process changes, the data structure updates with it, so your DoE stays intact and computable.     One line from my notes for leaders: aim for FAIR by design. Data should be findable, accessible, interoperable and reusable as it’s collected, not after the fact. When teams can capture experiment context, aggregate instrument and manual inputs, join data across unit operations and run real-time visualization or ML, throughput rises and transfer friction drops. This approach has shown time-to-market reductions, screening throughput increases, and major cuts in data prep effort.     In regulated work, don’t forget the guardrails. Audit trails, electronic signatures for completed experiments, and role-based access keep governance tight while letting collaborators contribute. APIs and SQL access matter too, because DoE is strongest when it connects to your analytics stack and master data.     Try this: pick one high-variance process, map the workflow end-to-end, assign permanent IDs to samples, and enforce QC ranges at data entry. Then push the resulting data frame into your DoE analysis. You’ll see clearer signals and faster iteration. 

Most businesses are data-rich, insights-poor, and decision-starved. Why? A lack of communication, clarity, and connection between the operational workflows and supply chain of information. Here’s how most discussions go: 👨💼 Can you pull the 10 top-cost drivers? 👩💻 Yes, here are the top 10 sorted by cost. 👨💼 Thanks. ~2 months later~ 👨💼 Can you pull the 10 top-cost drivers? 👩💻 Yes, here are the top 10 sorted by cost. 👨💼 Why haven't these numbers changed? 🤷♀️ It’s not enough to ask for analytics if there are no operations to affect or drive change based on what the data show. Even if the numbers had improved… - Would the business have improved with a different strategy? - What are the lessons learned? How can you solve this in your organization? 1. Start with the end goal in mind. What are the top 3-5 organizational objectives, and how do they tie to profitability? Identify the best use cases across each department related to the enterprise objectives and profitability and determine what would drive the most significant impact in the shortest time. 2. Develop informed strategies. What can you do operationally to drive revenue, avoid cost, or decrease expenses with the prioritized use cases? Develop strategies and hypotheses about the expected outcomes and determine how to achieve them operationally. 3. Run everything like a science experiment. Remember the scientific method? Observations -> hypotheses -> experimentation - > test -> conclusions -> repeat It’s okay to be wrong in your assumptions. It's not okay to stagnate or dig your heels in because the data don't show the information you expected to see. 4. Listen, collaborate, and use each other's strengths. The business has the relevant context and understand why and how decisions were made. Data teams understand how to architect and develop the right solutions to support operations. Lean on one another, give each team agency to make decisions, and be honest in your feedback. Let's give the conversation another go: 👨💼 Can you pull the 10 top-cost drivers? 👩💻 I can, but wasn't our strategy this year to find revenue opportunities? 👨💼 Yes, but I'd like to know about the cost. 👩💻 Alright, what operations do we have to support our findings if we see significant cost-cutting opportunities? 👨💼 We hired a new director of ops I will connect you with. They're focused on cutting costs in a few strategic areas. 👩💻 Great, thank you. ~2 months later~ 👨💼 How is the cost-cutting work coming along? 👩💻 Everything has progressed well! Once you connected me with the new director, we found 3 key areas to focus on and developed new dashboards to support operations. We've seen a 20% reduction in expenses, which has the potential to grow to 30% by the end of the year. 👨💼 Great, thank you. 👩💻 Happy to help. When the business, data, and tech move together, that's when true ROI and value are delivered. #EGDataGuy