Using Data To Improve Efficiency

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀𝗻'𝘁 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗵𝗲𝗰𝗸 －it's a continuous contract enforced across the various data layers to avoid breakage. Think about it. Planes don’t just fall out of the sky when they land. Crashes happen when people miss the little signals that get brushed off or ignored. Same thing with data. Bad data doesn’t shout; it just drifts quietly—until your decisions hit the ground. When you bake quality checks into every layer and, actually use observability tools, You end up with data pipelines that hold up. Even when things get messy. That’s how you get data people can trust. Why does this matters? Bad data costs money → Failed ML models, wrong decisions. Good monitoring catches 90% of issues automatically. → Raw Materials (Ingestion)  • Inspect at the dock before accepting delivery.  • Check schemas match expectations. Validate formats are correct.  • Monitor stream lag and file completeness. Catch bad data early.  • Cost of fixing? Minimal here, expensive later.  • Spot problems as close to the source as you can. → Storage (Raw Layer)  • Verify inventory matches what you ordered.  • Confirm row counts and volumes look normal.  • Detect anomalies: sudden spikes signal upstream issues.  • Track metadata: schema changes, data freshness, partition balance.  • Raw data is your backup plan when things go sideways. → Processing (Transformation)  • Quality control during assembly is critical.  • Validate business rules during transformations. Test derived calculations.  • Check for data loss in joins. Monitor deduplication effectiveness.  • Statistical profiling reveals outliers and distribution shifts.  • Most data disasters start right here. → Packaging (Cleansed Data)  • Final inspection before shipping to warehouse.  • Ensure master data consistency across all sources.  • Validate privacy rules: PII masked, anonymization works.  • Verify referential integrity and temporal logic.  • Clean doesn’t always mean correct. Keep checking. → Distribution (Published Data)  • Quality assurance for customer-facing products.  • Check SLAs: freshness, availability, schema contracts met.  • Monitor aggregation accuracy in data marts.  • ML models: detect feature drift, prediction degradation.  • Dashboards: validate calculations match source data.  • Once data is published, you’re on the hook. → Cross-Cutting Layers (Force Multipliers)  • Metadata: rules, lineage, ownership, quality scores  • Monitoring: freshness, volume, anomalies, downtime  • Orchestration: dependencies, retries, SLAs  • Logs: failures, patterns, early warning signs Honestly, logs are gold. Don’t sleep on them. What's your job? Design checkpoints, not firefight data incidents. Quality is built in, not inspected in. Pipelines just 𝗺𝗼𝘃𝗲 data. Quality 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝘀 your decisions. Image Credits: Piotr Czarnas 𝘌𝘷𝘦𝘳𝘺 𝘭𝘢𝘺𝘦𝘳 𝘯𝘦𝘦𝘥𝘴 𝘪𝘯𝘴𝘱𝘦𝘤𝘵𝘪𝘰𝘯.  𝘚𝘬𝘪𝘱 𝘰𝘯𝘦, 𝘳𝘪𝘴𝘬 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 𝘥𝘰𝘸𝘯𝘴𝘵𝘳𝘦𝘢𝘮.

Machine Learning models are critical not only for many customer-facing products like recommendation algorithms but also very important for unlocking value in backend tasks to enable efficient operations and save business costs. This blog, written by the machine learning engineering team at Netflix, shares the team's approaches to automatically leverage machine learning to remediate failed jobs without human intervention. -- Problem: At Netflix, millions of workflow jobs run daily in their big data platform. Although failed jobs represent a small portion, they still incur significant costs given the large base. The team currently has a rule-based approach to categorize error messages. However, for memory configuration errors, engineers still need to remediate the jobs manually due to their intrinsic complexity. -- Solution: The team uses the existing rule-based classifier as the first pass to classify errors, and then develops a new Machine Learning service as the second pass to provide recommendations for memory configuration errors. This Machine Learning system has two components: one is a prediction model that jointly estimates the probability of retry success and the retry cost, and the other is an optimizer that recommends a Spark configuration to minimize the linear combination of retry failure probability and cost. -- Result: Such a solution demonstrated big success, with more than 56% of all memory configuration errors being remediated and successfully retried without human intervention. This also decreased the compute cost by about 50% because the new configurations make the retry successful or disable unnecessary retries. Whenever there is a place with inefficiencies, it's helpful to think about a better solution. Machine learning is the way to introduce intelligence into such solutions, and this blog serves as a nice reference for those interested in leveraging machine learning to improve their operational efficiency. #machinelearning #datascience #operation #efficiency #optimization #costsaving #snacksweeklyondatascience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gRcaifKR 

9 Applications of Artificial Intelligence (AI) in semiconductor manufacturing 1. Yield Prediction and Enhancement AI models analyze massive amounts of process and test data to predict wafer yield and identify patterns causing defects. This helps in real-time corrective action and higher fab efficiency. 📈 Example: Machine learning models trained on historical wafer maps to predict low-yield lots. 2. Defect Detection and Classification AI-powered computer vision detects micro-defects in wafers and masks far more accurately and faster than manual inspection or traditional rule-based methods. 🧠 Example: Deep learning models used in automated optical inspection (AOI) to detect defects down to the nanometer level. 3. Predictive Maintenance of Fab Equipment Machine learning algorithms anticipate equipment failure based on sensor data (vibration, temperature, pressure) to schedule timely maintenance and reduce unplanned downtime. ⚙️ Example: AI detects anomalies in photolithography tools to prevent catastrophic failures. 4. Process Control and Optimization AI tunes thousands of process parameters (e.g., temperature, etch time) in real-time to ensure consistency and reduce process variability. 🛠️ Example: Reinforcement learning used to dynamically adjust plasma etch parameters. 5. Material and Recipe Optimization AI helps in discovering and validating new materials or process recipes faster by simulating outcomes based on previous data and physical models. 🧪 Example: Accelerated discovery of new high-k dielectrics using AI-assisted simulations. 6. Wafer Map Pattern Recognition AI clusters and recognizes patterns in wafer test maps to correlate with root causes in earlier process steps or design issues. 🧩 Example: Using CNNs (Convolutional Neural Networks) to classify wafer failure signatures. 7. Supply Chain and Inventory Optimization AI improves forecasting of raw material requirements, optimizes fab throughput, and minimizes bottlenecks and delays. 🚛 Example: AI predicting demand surges and adjusting chemical and gas inventory levels. 8. Automated Defect Review (ADR) and Decision-Making AI systems automate the decision-making in defect review systems, reducing the load on human analysts. 🕵️ Example: AI classifies defect types and decides whether they’re killer or nuisance defects. 9. Design-for-Manufacturability (DfM) Feedback AI assists in bridging design and manufacturing by predicting which layouts are more prone to yield issues, feeding that back to designers. 💻 Example: AI used in EDA tools to highlight layout hotspots during IC design. Add more in the comments. For all semiconductor and AI related content, follow TechoVedas -------------------- If you are looking to invest in semiconductors and need expert consulting, drop us a DM.

As a data engineer, I was more interested in building pipelines and solving tech puzzles, not setting up policies and processes. Little did I realize that data governance was the backbone of the very systems I relied on. Fast forward to today, and my perspective has completely shifted. Working on an entire data platform taught me that data governance is more than rules and restrictions; it’s the glue that holds everything together. Think of it as being the GPS for your organization’s data—it helps you navigate, keeps your data secure, and ensures everyone reaches their destination smoothly. I started seeing data governance as essential when I faced real-world problems: ▪️ Reports built on inaccurate data. ▪️ Duplicate or missing records causing business losses. ▪️ Sensitive information being exposed due to improper controls. It became clear that governance wasn’t an optional add-on; it was the foundation for ensuring trust in the data. So, What is Data Governance? It’s like onboarding a new employee. Just as every new hire is introduced to the company’s policies and trained for their role, every piece of data needs rules and a structure to follow. This ensures: ▪️ The data is high-quality and trustworthy. ▪️ It’s accessible only to the right people. ▪️ It’s traceable, so you know where it came from and how it’s been used. Here’s how I like to explain the main aspects of data governance: 1. Metadata Management Imagine a treasure map where the “X” marks the data you need. Metadata is that map. It tells you what the data represents, its origin, and how to use it effectively. Without it, you’re just guessing in the dark. 2. Data Access Control Think of a vault in a bank. Not everyone gets the same key. Permissions are granted based on roles, ensuring sensitive data stays protected while authorized users get what they need. 3. Data Lineage Ever traced a package you ordered online? Data lineage works the same way. It tracks where the data came from, where it’s going, and what’s been done to it. This visibility ensures accuracy and helps fix issues faster. 4. Data Access Audit This is your security camera. It logs who accessed what and when, providing a trail that keeps the system secure and compliant. 5. Data Discovery Finally, imagine a search engine for your organization’s data. It helps you find the exact dataset you need, fostering innovation and smarter decisions. So, next time you think of governance as just red tape, remember: It’s the invisible infrastructure making everything else work smoothly. The cleaner and safer your data, the more power it holds. What’s your take on data governance? Have you faced any challenges or successes with it? ❣️Love it...spread it ♻️

How To Kickstart Your Data Governance Plan Last week, I had the opportunity to discuss AI and data governance with a select group of leaders and entrepreneurs. Here is an excerpt from that discussion. Data governance is key to a successful data-powered organization. Here are three steps to get started: 1. Address the IT-Business Disconnect - IT as Custodians, Business as Experts: IT manages data, but business teams know its impact on operations. - Empower Business Users: Provide self-service data tools to reduce reliance on IT. - Define Data Flows: Let departments/functions define their own data needs for better efficiency. 2. Show the Value of Data Governance - Not Just an IT Concern: Data governance benefits the entire organization. - Show ROI: Demonstrate value for different teams: - Sales & Marketing: Better data quality boosts campaigns and sales. - Procurement: Governed data optimizes purchasing, reducing costs. - Legal & Compliance: Clear policies prevent non-compliance. - Finance: Well-governed data improves reporting. 3. Implement Technology Wisely - Use Modern Tools: Enhance data discovery with tech, but ensure human oversight. - Human-Driven Processes: Some processes need human input—automation isn’t enough. - Support System: Use tech to support, not replace, human decision-making. Key Takeaway Data governance creates value by bridging IT and business, communicating benefits, and using tech with human oversight to drive efficiency and reduce risks. How are you bringing IT and business closer in your data governance journey?

How we built a data-driven powerhouse at a leading financial Institution A journey worth sharing... Our starting point? A talented risk team relying heavily on individual expertise rather than systematic approaches. Sound familiar? Here's how we turned things around: The Challenge: Scattered documentation, unclear metrics and limited analytics capabilities holding back a potentially world-class risk organization. Despite having top-tier talent, the lack of structured processes was a ticking time bomb. The Solution: We implemented a comprehensive Operational Excellence (OPEX) framework that transformed how the organization approached risk: ✓ Mapped every critical process using SIPOC analysis ✓ Introduced robust metrics management for predictive insights ✓ Deployed FMEA for granular risk evaluation ✓ Created structured improvement cycles with 120-day delivery windows We didn't just drop tools and run. Our approach combined practical training in Lean principles, design thinking and creative problem-solving with continuous coaching. This wasn't about theoretical frameworks - it was about real, measurable change. The Result? A complete metamorphosis from gut-feel decisions to data-driven excellence. The organization now stands as a benchmark in risk management, equipped with both the tools and mindset for continuous evolution. Lessons Learned: 1. Small wins build unstoppable momentum 2. Combine process excellence with human expertise 3. Focus on sustainable change through skill-building Would you like to learn more about how we approach similar transformations? DM me for a detailed conversation. #Riskmanagement #Operationalexcellence #Innovation #Processimprovemet

Gartner has a new case study on unlocking value from data 🚀 🌐 A leading oil and lubricants company faced challenges with a complex data landscape. 📊 This included multiple data sources, inconsistent reporting, and poor data quality. 📈 To address these issues, they implemented a modern data and analytics strategy. 🎯 The Solution 🌊 Harmonized Data Architecture: Established a unified logical data model aligned with a global data lake 🌎 Data Quality and Governance: Introduced a global data quality management strategy across operations 🔄 Data Value Chain: Enabled seamless data lineage for automated normalization 📊 The Results 🕒 Efficiency: Reduced human effort by over 90% 📈 Data Quality: Improved through local governance based on global KPIs. 🚀 Scalability: Enhanced ability to introduce new technologies quickly. 🌟 By harmonizing data architecture and focusing on quality, organizations can unlock value delivery and achieve a data-driven culture. 💪 This approach improves decision-making, operational efficiency, and competitiveness. Their work continues as the core team paves the way for further maturing its data lake, global data model, and data management and #analytics capabilities. Gartner clients who subscribe to our AI and Emerging Technologies topic in Digital Supply Chain Value Realization should check out the 🔗 link in the comments to read the full: ℹ️ Case Study: Data & Analytics Intelligence to Unlock Value Delivery From my colleagues Christian Titze and Leonard Ammerer. Great work on this case study, gentlemen.

Starting in data governance can feel overwhelming. There’s pressure to “get it right,” but very little practical guidance on where to begin. That’s why I created this: The 7-step roadmap to implement (or improve) your data governance program. 👇 Here’s the breakdown: 1. Perform the initial assessment Understand where your organization stands, what's driving your DG program, and where to focus limited resources. Use a maturity model or even a simple self-assessment. 2. Get buy-in Programs fail without support. Build your business case, highlight tangible/intangible benefits (there are plenty of each), define risks, and secure at least one sponsor. 3. Set up the data governance program Time to get tactical: ✔ Define the scope ✔ Establish guiding principles ✔ Map out your data domains (start with 1-3) ✔ Build the governance organizational framework ✔ Set up a DG body and assign stewards 4. Develop metrics & KPIs If you can’t measure it, you can’t manage it. Choose a few impact and progress metrics to start. 5. Create policies, standards & processes This is where the rubber meets the road. Align these with your governance scope and business needs. 6. Set up and deploy tools Sure.. tools matter, but later. Start with essentials: business glossary, data dictionary, and a data catalog. 7. Manage change This is ongoing and it needs to start on day one. Even positive changes bring resistance. You’ll need communication, training, and champions. Do you want to learn all the above and MORE? 👉 That’s why I built an online course that walks you through every one of these steps — in detail, with templates, examples, and a clear action plan. Check it out here: https://lnkd.in/gwBcYQg5 If: ✅ You need a clear plan to implement or improve DG ✅ You’re tired of scattered or overly theoretical advice ✅ You want practical, real-world guidance Then this course is for you. It even comes with TEMPLATES. It doesn’t get easier than this. — Follow me here for more practical tips, cheat sheets and frameworks for data professionals. - George Firican

Electricity management is increasingly an analytics problem where AI needs to step in. Decarbonization, variable demand, regenerative energy, and complex infrastructure make it impossible to rely on static rules or occasional reporting. Value comes from analyzing operational data continuously and turning it into decisions. The usual analytics setup does not scale. Work is often done in silos, with data pulled into notebooks, results shared as static reports, and little reuse across projects. Domain experts are separated from the analysis, cycles are slow, and each new use case starts largely from scratch. A collaborative model is a catalyst enabling AI to change the economics. At Mitsubishi Electric, data scientists work directly with domain experts on shared workflows. Analytics is used to identify concrete issues and opportunities. In railways, analysis showed where braking generates surplus energy and how it could be reused. In thermal energy management, a full year of building data was analyzed in 20 business days to optimize heating and cooling. Platform efficiency matters. By running the full AI lifecycle in Dataiku, Mitsubishi Electric reduced their time to produce new projects by about 60 percent. That translates into delivering value roughly 2.5 times faster, which means more use cases delivered and quicker operational impact. This is what AI Success looks like in energy and industrial systems. Read the full story on our website: https://lnkd.in/evhhuQNF