How To Organize Data For Better Analysis

Data is the new lab bench in biotech, but most companies have a broken bench. Let me explain why this 123 approach is changing everything: Most biotech data goes unanalyzed—trapped in siloed systems, proprietary formats, and disconnected workflows. The fundamental problem? Traditional architectures treat each experiment as isolated rather than part of an interconnected knowledge web. This creates a massive cognitive burden for scientists who spend more time wrangling data than making discoveries. The solution isn't just better databases—it's creating what I call a "memory layer" for scientific knowledge. This layer has 3 critical components: 1) Structure first, analysis second Most labs try to analyze raw data directly without proper structure. Effective systems focus on building semantic models that define relationships between experimental components before analysis begins. This seemingly simple shift helps our customers dramatically reduce analysis time and enable previously impossible cross-experimental insights. 2) Graphs, not tables Biological systems are interconnected networks, yet we force data into rigid tables. Modern graph databases mirror how science actually works—through relationships, connections, and patterns. This approach allows scientists to discover "hidden bridges" between seemingly unrelated experiments. 3) Compound intelligence The true power emerges when these structured, graph-based systems learn over time. Each experiment enriches the model rather than sitting as a static data point. This creates compounding value where the 100th experiment is far more valuable than the first because it connects to everything before it. One genomics startup we worked with implemented this approach and saw remarkable acceleration: • They identified targets in weeks rather than months • Their experimental iterations became significantly faster • Scientists uncovered novel insights from existing data What's fascinating is that this approach makes scientists more effective while creating defensible IP in the data model itself. The biotech companies gaining the most investor traction aren't just producing molecules—they're building knowledge systems that get more valuable with every experiment. This is why forward-thinking VCs now evaluate data architecture as thoroughly as science. As we enter this new era, companies that build proper memory layers will outperform those still treating data as an afterthought. Wet lab scientists: Want to see how this memory layer approach could transform your research? DM me for a demo or subscribe to my newsletter: https://lnkd.in/gsyuTb_5

Stop losing your analysis files. The difference between chaos and clarity in computational biology is one habit: How you name your files/folders 🧵 1/ Ever dug through a maze of folders named “final,” “final2,” “final_really”—only to wonder which one held the real results? That’s chaos. 2/ The cure? Name folders with dates. Every run. Every project. Every result. It’s the simplest way to never lose track of your work again. 3/ Try this in Linux/macOS: mkdir $(date +%F) It creates a folder like 2025-03-26 in YYYY-MM-DD format. Clean. Automatic. Foolproof. 4/ Why this format works: It sorts naturally in file explorers. It avoids confusion (is 03-05 March 5th or May 3rd?). 5/ Example: You’re running RNA-seq. Structure it like this: project/ raw_data/ results/2025-03-26/ scripts/2025-03-26_differential_analysis.Rmd No guessing. No overwrites. Just clarity. 6/ If you analyze data daily, automate it: #!/bin/bash mkdir -p results/$(date +%F) Every run gets its own results folder. No exceptions. 7/ More naming habits that save your sanity: Use underscores, never spaces. Keep names short but clear (2025-03-26_qc_reads.txt). Avoid symbols like !@#* that break scripts. 8/ Want to go deeper? Read this classic guide: https://lnkd.in/efYfB-xz It’s a must-read for any computational biologist. 9/ Key takeaways: Date your directories. Automate consistency. Respect your future self. Your science is too important to get lost in a folder called “final_final2.” I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter chatomics to learn bioinformatics https://lnkd.in/erw83Svn

Still struggling with where to start when you are given a project? I have got you! Below is a step-by-step breakdown of key tasks to complete on a data analytics project. 1. Define The Project Objectives and Deliverables 🔹Identify the key questions or goals Why? A clear goal directs what data you need and how you will analyze it.   2. Understand the Structure of your Tables 🔹Examine each table's schema: columns, data types, relationships, and keys Why? This is helpful before any meaningful combination or analysis. Note: Most of the time, your project's data is located in different tables.   3. Prepare and Clean the Data 🔹Handle missing values 🔹Remove duplicates 🔹Fix formatting issues 🔹Ensure consistent units/currency/date formats Why? Data cleaning is often the most time-consuming part, but it is essential for ensuring accuracy and reliability in your analysis. 4. Combine/Merge the Tables 🔹Use keys or common fields to combine tables Why? It creates a complete dataset by bringing together relevant information from all the tables. It improves data quality and ensures that the analysis is comprehensive. 6. Data Enrichment (Optional) 🔹Create new variables or derive new metrics 🔹Create a date table using the date column from your table Why? It provides additional context and improves the power of your analysis by revealing deeper insights. 5. Conduct Exploratory Data Analysis (EDA) 🔹Run summary statistics 🔹Explore patterns, trends, and anomalies in your dataset Why? EDA helps you uncover patterns, spot errors, and decide which variables matter for analysis. 7. Perform Analysis 🔹Compare trends across time, regions, or segments 🔹Apply analytical techniques to answer initially defined questions 🔹Build KPIs Why? Here, you extract actionable insights from your prepared dataset and test hypotheses, directly addressing your project’s objectives. 8. Visualize Results 🔹Create different charts 🔹Use any visualization tool Why? It helps stakeholders understand results more easily through clear visuals. 9. Interpret and Report your Results 🔹Tell the story behind the data to communicate findings through reports or presentations tailored to your audience 🔹Explain what the analysis reveals, what it means, and why it matters 🔹Use concise reports, presentations, or dashboards Why? It converts technical output into business-relevant insights. This helps stakeholders make informed decisions based on your analysis. 10. Make Data-Driven Recommendations 🔹Validate your findings by checking for errors, testing assumptions, and possibly seeking feedback from others 🔹Suggest actions to be taken Why? Validation ensures the credibility and robustness of your conclusions before they are used in decision-making. 11. Monitor & Iterate 🔹Evaluate the impact of implemented changes 🔹Re-analyze periodically 🔹Update data pipelines or dashboards as needed Why? It ensures your analysis stays useful and responsive to changes. PS: What step can you add?