Data Analysis For Project Managers

Dear #DataEngineers, No matter how confident you are in your SQL queries or ETL pipelines, never assume data correctness without validation. ETL is more than just moving data—it’s about ensuring accuracy, completeness, and reliability. That’s why validation should be a mandatory step, making it ETLV (Extract, Transform, Load & Validate). Here are 20 essential data validation checks every data engineer should implement (not all pipeline require all of these, but should follow a checklist like this): 1. Record Count Match – Ensure the number of records in the source and target are the same. 2. Duplicate Check – Identify and remove unintended duplicate records. 3. Null Value Check – Ensure key fields are not missing values, even if counts match. 4. Mandatory Field Validation – Confirm required columns have valid entries. 5. Data Type Consistency – Prevent type mismatches across different systems. 6. Transformation Accuracy – Validate that applied transformations produce expected results. 7. Business Rule Compliance – Ensure data meets predefined business logic and constraints. 8. Aggregate Verification – Validate sum, average, and other computed metrics. 9. Data Truncation & Rounding – Ensure no data is lost due to incorrect truncation or rounding. 10. Encoding Consistency – Prevent issues caused by different character encodings. 11. Schema Drift Detection – Identify unexpected changes in column structure or data types. 12. Referential Integrity Checks – Ensure foreign keys match primary keys across tables. 13. Threshold-Based Anomaly Detection – Flag unexpected spikes or drops in data volume or values. 14. Latency & Freshness Validation – Confirm that data is arriving on time and isn’t stale. 15. Audit Trail & Lineage Tracking – Maintain logs to track data transformations for traceability. 16. Outlier & Distribution Analysis – Identify values that deviate from expected statistical patterns. 17. Historical Trend Comparison – Compare new data against past trends to catch anomalies. 18. Metadata Validation – Ensure timestamps, IDs, and source tags are correct and complete. 19. Error Logging & Handling – Capture and analyze failed records instead of silently dropping them. 20. Performance Validation – Ensure queries and transformations are optimized to prevent bottlenecks. Data validation isn’t just a step—it’s what makes your data trustworthy. What other checks do you use? Drop them in the comments! #ETL #DataEngineering #SQL #DataValidation #BigData #DataQuality #DataGovernance

Your dashboards can be 100% green. And still completely wrong. That’s the scary part about data quality problems: they spread quietly before anyone notices. A reliable pipeline doesn’t just move data. It verifies trust at every stage. The checks that matter most: • null & duplicate validation • primary key checks • referential integrity • schema evolution detection • freshness monitoring • range & outlier checks • distribution drift tracking And one lesson engineers learn late: Schema evolution is not “just metadata.” A tiny structural change can break: • joins • aggregations • ML features • dashboards • historical consistency If you want stronger systems: • validate schemas before deploys • monitor row-count anomalies • compare distributions over time • treat data contracts seriously • build observability into pipelines early Because pipelines usually fail long before they crash. The best engineers catch the signal before the incident. Here’s are some amazing frameworks to include in your data projects: → Great Expectations : Write tests for your data like you test code. → Deequ: Amazon's gift to data quality. Scales beautifully. → Monte Carlo : Observability for data pipelines. Sleep better. → dbt Labs tests: Test your transformations. Trust your models. Quality isn't a one-time project. It's a daily practice. Image Credits: Sumit Gupta What’s one silent data issue your team learned the hard way? #data #engineering

Think your dataset is clean? 🤔 The 3 types of outliers silently sabotaging your model say otherwise... | Most teams focus on model architecture while ignoring dataset hygiene. They discover too late that quality outliers, content anomalies, and annotation errors are destroying their model's reliability. Traditional data cleaning methods miss these critical issues. | By combining embedding spaces with Local Outlier Factor analysis, you can catch these issues early and systematically clean your datasets. 🔑 KEY LEARNINGS: → Dataset outliers directly impact model metrics by skewing confidence thresholds and reducing precision/recall → Dense embeddings from your model's penultimate layer provide rich feature representations for outlier detection → UMAP visualization reveals clusters of similar images—isolated points are your first outlier candidates ⚡ TRY THIS NOW: Start with a small subset of your data (~1000 images): extract their embeddings and apply LOF to get an outlier score for each image. The highest scoring samples are your priority investigation targets. 🔬 Ready to level up? My Coursera course on computer vision quality is free to audit and includes complete notebooks on embedding-based outlier detection. 💭 What's your biggest dataset quality challenge? Let me know in the comments! #deeplearning #data #computervision #objectdetection

Engineering designed it one way. Manufacturing built it another. Field service maintains something entirely different. And nobody knows until a customer finds out the hard way. This is the baseline gap problem. In practice, 40 to 80% of BOMs arrive at an EMS for manufacturing with problems before anyone even gets to the as-built stage. The discrepancies only compound from there. Manually reconciling these baselines across thousands of items, serial numbers, and modification histories is not realistic. It is not even attempted at most organizations. 𝗧𝗵𝗲 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲: AI continuously compares the as-designed, as-built, and as-maintained baselines, surfacing discrepancies that humans cannot detect at scale. A component was redesigned in the as-designed baseline to address a safety issue, but it did not fit; a fix was applied in manufacturing, which was not reflected in the as-designed baseline. While in the field, the as-maintained baseline has not been updated for the systems that received the solution for the safety issue. AI identifies these gaps systematically, across every product, every serial number, every site. Machine learning pipelines for multi-level BOM anomaly detection are already being developed in research. But anomaly detection without baseline structures is just noise. 𝗪𝗵𝗮𝘁 𝗖𝗠𝟮 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀: Because all three baselines share the same as-planned/as-released structure, AI can compare them directly. Same format, same change traceability. As-built records are provided with evidence of conformance, waivers, and deviations. The as-maintained baseline provides visibility of planned changes and retrievable modification history. 𝗧𝗵𝗲 𝗵𝘂𝗺𝗮𝗻 𝗿𝗼𝗹𝗲: AI surfaces the discrepancies. Humans determine the disposition. A gap between the as-designed and as-built baseline might be an accepted deviation, a pending change not yet effectuated, or an actual nonconformance. That judgment requires understanding the history, the intent, and the risk. AI cannot determine whether a field modification that was never formalized is a documentation gap or a safety concern. A configuration manager can. 𝗧𝗵𝗲 𝗖𝗠𝟮 𝗿𝗼𝗹𝗲: CM2 ensures that each baseline has a defined source of truth. Every baseline derives its content from change notices and their impact matrices. As-built records trace to work authorizations. As-maintained baselines include a modification history that is retrievable at any time. Because all three share the as-planned/as-released structure, AI can pinpoint not just that a discrepancy exists, but exactly where in the lifecycle the baselines diverged. Without governed baselines, AI compares opinions. With CM2, AI compares records built on the same structure. Do you actually know where your as-designed, as-built, and as-maintained baselines diverge? Or are you waiting for the field to tell you? #ConfigurationManagement #CM2 #PLM #AI #CM #Baseline #Records

OT Asset Management under NIST 1800-23 >> NIST 1800-23: Energy Sector Asset Management (ESAM) delivers a blueprint for visibility, control, and resilience across electric utilities, oil & gas, and other critical infrastructure sectors. >>> This project addresses the following characteristics of asset management: > Asset Discovery: establishment of a full baseline of physical and logical locations of assets > Asset Identification: capture of asset attributes, such as manufacturer, model, OS, IP addresses, MAC addresses, protocols, patch-level information, and firmware versions > Asset Visibility: continuous identification of newly connected or disconnected devices and IP and serial connections to other devices > Asset Disposition: the level of criticality (high, medium, or low) of a particular asset, its relation to other assets within the OT network, and its communication with other devices > Alerting Capabilities: detection of a deviation from the expected operation of assets >>> A standardized architecture allows organizations to replicate deployments across sites while tailoring to local needs, ensuring both scalability and security. > At each remote site, control systems generate raw ICS data and protocol traffic (Modbus, DNP3, EtherNet/IP), which is collected by local data servers. > These servers act as the secure bridge, encapsulating serial traffic and transmitting structured data through VPN tunnels back to the enterprise. > Once in the enterprise environment, asset management tools aggregate inputs from multiple sites, giving analysts a single source of truth. > Events and asset health indicators are displayed on centralized dashboards, enabling timely detection of anomalies, vulnerabilities, or misconfigurations. > Importantly, remote management is limited only to the data servers, ensuring that core control systems remain shielded from unnecessary exposure. >>> Here’s a 10-point summary of the ESAM reference design asset management system: > Data Collection – Gathers raw packet captures and structured data from OT networks. > Remote Configuration – Allows secure management and policy-driven data ingestion. > Data Aggregation – Centralizes collected data for further processing. > Monitoring – Continuously observes network activity for anomalies. > Discovery – Detects new devices when new IP/MAC addresses appear. > Data Analysis – Normalizes multi-site traffic into one view and establishes baselines of normal behavior. > Device Recognition – Identifies devices via MAC addresses or deep packet inspection (model/serial). > Device Classification – Assigns criticality levels automatically or manually. > Data Visualization – Displays collected and analyzed information in a centralized dashboard. > Alerting & Reporting – Notifies analysts of abnormal events and generates reports, including patch availability. #icssecurity #OTsecurity

One of the hardest parts of data engineering is getting a pipeline into production. The next hardest part is proving every day that it is still telling the truth. Most on-call pages are not about jobs failing. They are about dashboards lying. - A spike that should not exist. - A drop nobody can explain. - Business teams asking, "Is this real or a bug?" If you cannot answer that quickly, you do not have observability. You just have logs that are not useful to anyone. Here is how I think about pipeline observability in six layers. Each layer comes from the graphic you see, but translated into how we operate in real systems. Layer 1: Data checks to confirm the drop is real Before you debug anything, prove the alert is not a false positive. - Compare to the same weekday, same time window, not just “yesterday vs today”. - Check for late data, upstream throttling, backfills, or missing partitions. - Look at job runtimes and lag. Did a job run late, or not at all? Goal: you want to stop waking people up at 2 AM because your API was 4 hours late, not because the business collapsed. Layer 2: Lineage and drivers for the metric Once the drop is real, you ask, “Which layer broke?” - Decompose the KPI into 2 to 4 driver checks. Example: Health = Freshness + Completeness + Correctness. - Use lineage to trace which inputs, joins, and filters feed each driver. - Add cheap driver metrics: row counts, null ratios, uniqueness, expected ranges. Goal: isolate which part of the pipeline is sick instead of staring at the final dashboard. Layer 3: Trend monitoring for each driver Do not just look at the current value. Look at the story over time. - Plot each driver for the same time range. - Find the first timestamp where one driver diverged. - Correlate that moment with deploys, config changes, or upstream experiments. Goal: find the “start of failure” moment, not just the loudest symptom. Layer 4: Slice-level anomaly detection Now you zoom in. Averages hide the crime. - Break the failing driver into slices: source system, region, event type, partition, SDK version, and so on. - Look for the segment where the drop is sharpest. - Keep slicing until you find the smallest segment that behaves differently. Goal: localize the blast radius. You want “US web events since version 3.2.1” instead of “events are broken”. Layer 5: Controlled comparisons between healthy and failing segments Debugging becomes much easier when you compare two concrete worlds. - Pick a healthy slice that stayed normal and a failing slice that dropped. - List everything that is the same: ingest path, schedule, infra. - Then list what is different: schema version, SDK version, region, feature flags. Goal: turn a vague incident into a controlled experiment. The differences between the two slices are your clue list. Continued in comments:

Tracking a simplicial complex over time to detect anomalies requires modeling the evolution of higher-order interactions—beyond nodes and edges. The Hodge Laplacian serves as the cornerstone for lifting graphs into topological domains. The authors begin by introducing the essential elements of simplicial data: incidence matrices, Up/Down Laplacians, the Weisfeiler–Lehman isomorphism, and the notion of graph lifting. The anomaly detection task is formulated as analyzing temporal snapshots of a latent generative model that represents either true simplicial data or simplices derived from graphs. The proposed approach involves: 1.    Computing the combinatorial Hodge Laplacian and determining its maximum rank. 2.    Extracting the dominant singular values (via SVD) from the Hodge Laplacian for each snapshot to form the spectral representation. 3.    Applying a sliding-window mechanism to detect anomalies using both short-term and long-term temporal contexts. The temporal context refines feature vectors through weighted averaging, which is then used to define the anomaly scoring function. The paper concludes with performance analyses and experiments on the UCI online message and MIT Reality Mining datasets, demonstrating significant precision–recall improvements compared to traditional Graph Neural Networks. References: https://lnkd.in/giHzvu3G https://lnkd.in/gjkmd4Gi #SimplicialComplex #SimplicialAnomalyDetection #HodgeLaplacian #Graph

Isolation Forests, while possibly not the most recent anomaly detection technique, represent still an intuitive and effective one, also fairly well performant in hyperdimensional datasets. Proposed in the homonymous paper by Liu et al. 2008, in it, the authors start by noticing how most anomaly detection systems start by modeling the "normal" observations, and only then proceed on identifying anomalous ones. Instead, they suggest to invert the task, by directly modeling the anomalous observations. Like its most famous close relative, Random Forest, also in an Isolation one an ensemble of (Isolation) Trees gets built in parallel. The fundamental idea, as per Algorithm 2 here attached, is to recursively divide the training samples by randomly picking one of their features q and a cutoff point p in it, hence leaving all observations either in the derived right or left child node. This recursive repartition is stopped when a child node has only one observation in it (or all the observations in it are identical if not all observations are distinct). If we allow the tree to grow fully, assuming all n observations are distinct, eventually all of them will be isolated, implying a total of n leaves and n-1 internal nodes. However, the authors not only suggest to use a subsample of size psi = 256 to build each tree, but also to consider a max_height based on it given by log_2(psi) = 8 to build only-partial trees. The intuition for this is that anomalous observations (our interest) will get isolated much earlier. Id est, these observations will present a short Path Length h(x), consisting in "the number of edges x traverses an iTree from the root node until the traversal is terminated at an external node". Based on h(x), how to build a clear anomaly score s(x,n)? First, let E(h(x)) be the average path length observed for a given observation x across the isolation trees. Then, considering that Isolation Trees are structurally equivalent to Binary Search Trees (BST), and that an external node termination in an Isolation Tree is equivalent to an unsuccessful search in a BST, they go on noticing that "the estimation of average h(x) for external node terminations is the same as the unsuccessful search in a BST". This quantity is defined as c(n), and it is based on the harmonic number which can in turn can be estimated as ln(n) + Euler-Mascheroni constant. The final anomaly score is defined as s(x, n) = 2^(-E(h(x))/c(n)). If E(h(x)) ~ c(n) => s(x,n) ~ 0.5 or E(h(x)) -> n - 1 => s(x,n) -> 0, it means that this observation x is hardly anomalous, since it either has a path length respecting the estimate for its average (first case) or converging to the longest possible one (second case). Conversely, if E(h(x)) -> 0 => s(x,n) -> 1, red flags for anomalous instance need to be raised. Link to the original paper in the comment: in the figure, from it, Algorithm 2 describing the construction of an Isolation Tree. 

Are your alarms already too late? Most teams know the feeling. A system runs quietly until one day it does not, then the work turns urgent fast. The problem often starts much earlier. Traditional BAS alarms wait for a hard threshold, so the signal appears only after the issue has already grown into a disruption. Condition-aware operations move that timeline forward. With AI applied to BAS trend data, teams can watch for small changes in runtime, cycling, efficiency, or performance against historical patterns. These shifts may be too subtle to trigger an alarm, but they can still be the first measurable signs of trouble. Take a chilled water pump. In a reactive model, the response begins after failure, reduced performance, or a comfort complaint. In a condition-aware model, a gradual rise in energy draw relative to output can be flagged earlier as a developing risk. As the pattern continues, the system can raise confidence over time, giving the team evidence to plan around instead of a single event to chase. That changes the work. Parts can be ordered before the window closes. Service can be scheduled when the building can absorb it. Teams spend less time guessing when to act and more time deciding based on a clear trend. Practical Next Steps: • Review weak signals - Look for assets where trend data shows small repeat changes before alarms ever fire. • Prioritize planned windows - Use early anomaly detection to shift service into scheduled maintenance time. • Build evidence over time - Treat emerging patterns as decision support, not just one-off alerts. • Focus on business impact - Fewer emergency callouts can mean less disruption across tenants and operations. The real value of condition-aware operations is giving teams more time to act before small issues become expensive emergencies. #BuildingAutomation, #FacilityManagement, #PredictiveMaintenance, #OperationalExcellence, #AI, #RefreshWithRyza, #NodeWork