Energy Company Data Management Case Study

𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝟰𝟬 𝗼𝗶𝗹 𝗮𝗻𝗱 𝗴𝗮𝘀 𝗳𝗮𝗰𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝟯 𝗰𝗼𝗻𝘁𝗶𝗻𝗲𝗻𝘁𝘀 𝘀𝗼𝘂𝗻𝗱𝘀 𝗶𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝘂𝗻𝘁𝗶𝗹 𝘆𝗼𝘂 𝘀𝗲𝗲 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗰𝗵𝗮𝗼𝘀 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝘁. When I first sat down with the operations team at a global energy company, we talked about scaling operations and the complexity that comes with it. "𝘞𝘦'𝘳𝘦 𝘴𝘱𝘦𝘯𝘥𝘪𝘯𝘨 𝘩𝘢𝘭𝘧 𝘰𝘶𝘳 𝘵𝘪𝘮𝘦 𝘤𝘩𝘢𝘴𝘪𝘯𝘨 𝘯𝘶𝘮𝘣𝘦𝘳𝘴 𝘵𝘩𝘢𝘵 𝘥𝘰𝘯'𝘵 𝘢𝘥𝘥 𝘶𝘱," he said. "𝘍𝘶𝘦𝘭 𝘶𝘴𝘢𝘨𝘦 𝘳𝘦𝘱𝘰𝘳𝘵𝘴 𝘧𝘳𝘰𝘮 𝘛𝘦𝘹𝘢𝘴 𝘴𝘩𝘰𝘸 𝘰𝘯𝘦 𝘵𝘩𝘪𝘯𝘨. 𝘌𝘲𝘶𝘪𝘱𝘮𝘦𝘯𝘵 𝘳𝘶𝘯𝘵𝘪𝘮𝘦 𝘭𝘰𝘨𝘴 𝘧𝘳𝘰𝘮 𝘕𝘪𝘨𝘦𝘳𝘪𝘢 𝘴𝘩𝘰𝘸 𝘢𝘯𝘰𝘵𝘩𝘦𝘳. 𝘗𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘰𝘶𝘵𝘱𝘶𝘵 𝘧𝘳𝘰𝘮 𝘐𝘯𝘥𝘰𝘯𝘦𝘴𝘪𝘢 𝘪𝘴 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦𝘭𝘺 𝘰𝘧𝘧." Every month, his team burned through hundreds of hours manually reconciling data across spreadsheets and outdated dashboards. Meanwhile, discrepancies were costing them millions in hidden inefficiencies. "𝘞𝘦 𝘬𝘯𝘰𝘸 𝘵𝘩𝘦𝘳𝘦 𝘢𝘳𝘦 𝘱𝘳𝘰𝘣𝘭𝘦𝘮𝘴," he continued. "𝘞𝘦 𝘫𝘶𝘴𝘵 𝘤𝘢𝘯'𝘵 𝘧𝘪𝘯𝘥 𝘵𝘩𝘦𝘮 𝘧𝘢𝘴𝘵 𝘦𝘯𝘰𝘶𝘨𝘩 𝘵𝘰 𝘥𝘰 𝘢𝘯𝘺𝘵𝘩𝘪𝘯𝘨 𝘢𝘣𝘰𝘶𝘵 𝘪𝘵." We built an anomaly detection system trained on their specific equipment logs, shift patterns, and cost drivers. Xenoss engineers discovered that their Nigerian facilities had completely different maintenance cycles than their Texas operations. Equipment that looked "broken" in one region was actually running normal seasonal patterns in another. Three months later: -> 50% less time spent on manual reconciliation -> Real-time detection of fuel usage anomalies -> Compliance issues caught before they became problems Now they catch discrepancies in real-time instead of discovering them during monthly reviews. Their ops managers can focus on optimization instead of playing data detective. What's one manual task in your operations you'd automate tomorrow if you had the right system?

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

Groupe KILOUTOU, a billion-euro international equipment rental group has fundamentally transformed its #datastrategy by moving from a traditional centralized Business Intelligence model to a domain-driven "Data Product" architecture built on Snowflake. This evolution reflects how strategic M&A activity, international expansion and customer demands for sustainability reporting can drive profound organizational change in #datamanagement. The company's journey began in 2016 when its BI capabilities were admittedly basic, with siloed departmental data trapped in a "spaghetti plate" architecture. The 2018 adoption of Snowflake marked an inflection point. A critical finance report that previously required hours on Oracle could suddenly be generated in under ten minutes. However, the initial excitement led to unsustainable data dumping that created what the team candidly called a "big mess." This painful experience crystallized a fundamental insight: powerful technology alone is insufficient without robust governance aligned to business organization. The breakthrough came in June 2024 with a complete organizational restructuring. Kiloutou dismantled its geography-based data team structure and organized teams by business domains like Finance, Sales, HR. Each domain operates under a dedicated Data Owner responsible for #dataquality, governance and utility. This domain-driven approach treats data as a product with clear labeling, usage instructions and freshness indicators. The company has defined data products organized into domains and sub-domains. Data exchange between domains operates through formal Data Contracts that define structure, quality standards and usage terms. The business impact has been substantial for M&A integration. When Kiloutou acquires a company, ingesting its data into Snowflake enables immediate assessment against group-level KPIs and accelerated integration even while full IT system consolidation continues over three years. The platform has also enabled new data-driven services. With billions of telemetry records from connected equipment flowing through #Snowflake, #Kiloutou now offers sustainability metrics including fuel consumption analysis, carbon footprint tracking and optimization recommendations. The company built a custom Data Portal using #Snowpark and Streamlit that provides an integrated catalog of approximately 1,000 information points, a GUI-based query builder and a natural language interface powered by Snowflake Cortex AI where users can ask questions in plain language (https://lnkd.in/e5-Ue96s). The strategic vision extends toward full self-service capabilities by 2026. Kiloutou's transformation demonstrates how aligning data organization with business structure, treating data as a managed product and leveraging modern platform capabilities can turn data from a bottleneck into a genuine competitive advantage.

"We found the missing barrels.” The VP looked up — confused. I turned my screen toward him Every tank drop Every ticket image Every timestamp — perfectly aligned. Then I pointed to what wasn’t there: the payments. “These loadouts were taken from the tanks. Documented. But never paid.” Within minutes, he picked up the phone. One call. One email. By the end of the week, 5% of total cashflow had been recovered — instantly verified by the data. Because the data wasn’t just data anymore — it was evidence. And it only surfaced because reconciliation was already happening autonomously, every day, across every asset — quietly matching patterns humans would’ve missed. Cashflow doesn’t sleep anymore — it reconciles itself. What used to take entire accounting teams and late nights now runs continuously, 24/7. For years, real-time field data has been one of the least utilized assets in oil & gas. McKinsey & Company estimates less than 1% of operational data in energy is analyzed in real time. Deloitte found 60%+ of field data remains trapped in legacy systems. Not because COOs didn’t want visibility— but because the technology to correlate patterns across time, tickets, and payments didn’t exist. ⚙️ Real Example — Autonomous Reconciliation in Action At 10:09 AM, the oil level dropped from 204.58 → 117.38 bbl, a drawdown of about 87 barrels. The truck was connected to multiple equalized tanks, bringing the total to around 185 barrels — a standard truckload over ~30 minutes. Here’s how the system validates it autonomously: 1️⃣ Time Validation: Confirms drop within 30–45 minutes → consistent with a real loadout. 2️⃣ Volume Match: Correlates withdrawal to standard truck capacity. 3️⃣ OCR Validation: Matches scanned ticket with tank location, tank level drop, driver, purchaser, timestamp, etc. 4️⃣ Payment Correlation: Verifies that payment reflects the same ~185 bbl withdrawn. Any mismatch is flagged instantly — before the books ever close. What once required hours of manual entry and cross-checking now happens continuously, autonomously, and auditably. With 80% of reconciliation work automated, teams focus where they create real value — reviewing exceptions, not chasing tickets. That’s operational leverage in action. Whether its a billion-dollar operator or a 10-well producer, this turns raw data into cashflow assurance — a single, trusted source of truth between operations, accounting, and buyers. Because when tank patterns, truck tickets, and payments align, you don’t just have data — you have proof. 💬 Question for Operators: Where do you see the biggest cashflow blind spots today — in field loadouts, ticket matching, or payment reconciliation? 👇 Drop your perspective below. 📈 Follow for weekly COO cashflow playbooks + real examples of operational AI in motion #OilAndGas #leadership #business #OperationalExcellence

From Theory to Practice: My Journey in Data Quality Management in Oil & Gas Over the past few weeks, I had the incredible opportunity to work on a real-world Data Quality Management (DQM) project as part of my Petroleum Data Management studies at the University of Aberdeen where i represented bp . This project pushed me beyond theoretical concepts into practical implementation, addressing the real challenges of data governance, compliance, and security in the energy sector. The Problem I was tasked with identifying and resolving critical data quality issues in four key datasets commonly used in the oil and gas industry: 1. Upstream Data: Oil & gas production records subject to regulatory compliance (OGA, NUPRC). 2. Downstream Data: Refinery storage & sales figures requiring inventory accuracy & forecasting optimization. 3. Corporate Data (CRM): Customer records governed by GDPR, emphasizing data privacy & security. 4. Financial Data: Sales ledger control, ensuring financial integrity & fraud detection. Key Challenges I Tackled: 🔹 Missing values, duplicates, and incorrect segmentation affecting data consistency. 🔹 Security risks in handling confidential business & customer data. 🔹 Data compliance issues (GDPR, ISO 27001, and OGA reporting standards). 🔹 Outliers & inconsistencies impacting analytics & decision-making. How I Solved These Challenges 1. Implemented data cleaning & standardization techniques to improve accuracy. 2. Used encryption, pseudonymization, and secure file transfer methods to enhance security. 3. Developed automation scripts in Python & Excel to detect and correct anomalies. 4. Applied data visualization techniques (heatmaps, bar charts, and dashboards) for insights. 🌍 Why This Project Matters? In the oil and gas industry, poor data quality leads to regulatory penalties, operational inefficiencies, and financial losses. By applying Data Quality Management (DQM) best practices, I helped ensure that data remains accurate, secure, and compliant, ultimately enabling better decision-making and industry innovation. I’m excited to take these new skills and industry insights forward into my career, working towards data-driven optimization in the energy sector. Let’s connect! If you’re in data management, compliance, or oil & gas analytics, I’d love to exchange ideas and insights. How does your industry handle data quality challenges? Let’s discuss! 👇 #DataManagement #DataQuality #OilAndGas #PetroleumData #Compliance #BigData #EnergySector #Python #Excel #MachineLearning #DigitalTransformation #GDPR #DataSecurity #PetroleumEngineering

Tiger powers the companies that power the world. Take Evergen (acquired by Intellihub Group): their mission is to decarbonize the energy system by building resilient, renewable-first infrastructure. At first, they used MongoDB Atlas for their time-series data, mainly because it was the default for their non-time-series workloads. But it quickly became cost-prohibitive and technically restrictive. "Our team created a schema on top of MongoDB by storing data in daily buckets. We tried to replicate what TimescaleDB does behind the scenes… it sort of worked, but it also didn’t." – Evergen Lead Software Engineer That’s when they made the switch: Evergen replaced MongoDB with TimescaleDB on Tiger Cloud. Now their engineers (and even non-technical teams) query time-series data easily with SQL: "Everyone knows SQL at some level, even non-technical people. We can give someone in data science or customer support access to TimescaleDB, and they’ll figure it out. That wasn’t possible with MongoDB." Evergen cut costs, simplified operations, and unlocked their data. Read the full case study linked below.

We recently helped a major utility company slash weeks of analysis time down to mere hours. This was a utility in a region with rapid load and generation growth. Things like new industrial load, data centers, and a flood of renewable projects. They had transmission capacity, but not in many of the areas where much of this extra load was planned. They needed a way to easily show "hosting capacity" on their transmission network. Doing this sort of analysis manually thought was painful and resource intensive. A study could take weeks to complete from start to finish. They needed a few things: 1) A standardized method for large-scale hosting capacity analysis at the transmission level. 2) A computationally intensive tool capable of running large-scale power flow studies efficiently. 3) The ability to provide fast, accurate, and accessible data for planning and decision-making. So we partnered with them to co-design a tool to solve this problem. We had already tackled similar challenges before, so we knew where to start, but they were still blown away by the outcome. Studies that could have taken weeks are done in hours, and running multiple scenarios is as easy as just clicking a few buttons. This is the kind of work I get excited about. Combining deep power systems knowledge, thoughtful UX design, software engineering, and devops... all with a focus on human-centered tools. When you build the right tool, you can unlock so much potential. Proud of the Simple Thread team for this one. ⚡️ ️ Read the full case study here: https://lnkd.in/esxXmFyN ⚡️ ️ Read the T&D World Article here: https://lnkd.in/eCSkEcpV #CustomSoftware #EnergyTech #GridModernization #UtilityInnovation #PowerSystems #CaseStudy #SimpleThread