Data-Driven Workflow Automation

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

From raw sensor readings to intelligent automation - this 15-step pipeline shows how IoT data evolves into real-time insights and actions. I've seen teams miss steps here, and it always costs them. ➞ Data Capture: Sensors collect raw environmental and machine data such as motion, pressure, and temperature. ➞ Device Connectivity: Devices securely transmit this data through reliable IoT networks. ➞ Edge Filtering: Redundant and noisy data is filtered at the edge to reduce latency and bandwidth use. ➞ Data Aggregation: Sensor streams are merged and structured for consistent downstream processing. ➞ Gateway Management: IoT gateways securely handle data routing, device validation, and communication. ➞ Stream Processing: Tools like Kafka or MQTT process real-time data for instant insights. ➞ Cloud Storage: Clean data is stored in data lakes or databases for long-term access and analytics. ➞ Data Transformation: Standardizes, cleans, and enriches data for AI or predictive modeling. ➞ Visualization Layer: Dashboards and BI tools reveal real-time patterns and performance trends. ➞ Security & Compliance: Implements encryption, authentication, and regulatory compliance to protect sensitive data. ➞ Predictive Modeling: AI models forecast trends and automate decisions before issues occur. ➞ Edge AI Execution: Lightweight models run directly on devices for low-latency, offline intelligence. ➞ Automated Workflows: System triggers automate alerts, adjustments, and responses in real time. ➞ Self-Healing Systems: AIoT frameworks detect, diagnose, and fix problems with minimal human intervention. ➞ Continuous Optimization: Feedback loops improve performance, reliability, and efficiency over time. Building an AI-powered IoT system? Save this roadmap and use it to design smarter, data-driven pipelines. 🔁 Repost if you're building for the real world, not just connected demos. ➕ Follow Nick Tudor for more insights on AI + IoT that actually ship.

Good categorization of the application types. Please don't call everything an AGENT. 1) Workflow Automation (No AI): “A sequence of predefined steps that can run automatically.” Examples New CRM lead → add to mailing list → notify sales Form submitted → create invoice → send confirmation email Daily ETL → clean data → update dashboard When to Use It The process rarely changes Decisions can be made with simple rules The task is repetitive and predictable 2. Automated AI Workflow: “A sequence of predefined, automated steps that utilize AI to achieve a certain outcome.” Examples User email → LLM categorizes issue → route to support team Customer note → LLM categorizes → LLM summarizes → save to CRM CRM record list → LLM drafts emails → store as Outlook drafts Uploaded document → LLM extracts fields → populate database Website form entry → ML model scores lead → notify sales Sensor measurement → ML model predicts quality → send alert When to Use It You need interpretation, classification, or generation inside a predictable workflow Inputs vary, but the process doesn’t The order of steps matters and must be controlled You want clear human-in-the-loop checkpoints This is the most common architecture for real business applications today. 3.AI Agent: “An AI system that decides autonomously which steps to take to reach the goal.” Examples Research agent → searches the web → reads pages → extracts insights → compiles a report Data cleanup agent → inspects dataset → identifies issues → chooses transformations Customer service agent → reads ticket → decides whether to answer, escalate, or request clarification and then performs the action. Systems agent → monitors logs → diagnoses issues → initiates remediation steps autonomously When to Use It The system must choose between multiple possible actions The order of steps cannot be known upfront The task involves open-ended reasoning or exploration The workflow needs to adapt dynamically to new information Multiple tools or data sources might be needed depending on the case 4. Agentic Workflow Automation: “An AI agent embedded into an automated workflow.” Examples Claims processing → workflow collects documents → agent checks for missing info & decides what to request → workflow completes filing Content creation pipeline → workflow handles first draft → agent rewrites sections or improves structure → workflow checks output → workflow publishes When to Use It Most of the workflow is stable, but one part needs dynamic reasoning You want autonomy in a contained, well-defined environment You need agent-like flexibility without giving up control of the overall process

Today Glide becomes a whole new beast with the beta release of ⚡ Workflows–eerily powerful automations, perfectly integrated with Glide, continuing in our tradition of elegant tools with understated power. All of our customers pair Glide with a third-party automation tool like Zapier or Make, which are great for connecting your app to a wide array of existing services, but awkward for data-intensive automations that our customers want to achieve for a few reasons: 1. Shared data and compute: previously, customers implemented the same logic in Glide & the automation tool, drastically increasing maintenance cost. Glide Workflows have direct access to the same tables and computations as your apps, so your interfaces and automations remain in lockstep. 2. Zapier and Make are optimized for processing single events, connecting tool A to tool B. Glide Workflows are designed for operations on tables and batch data; for example, it's easy to loop over all Orders, then all Items per Order, and then finally complete a summary step. Looping is absent, primitive, or convoluted in these other tools. 3. No-code computations as steps. Glide Workflows has access to Glide's set of powerful computational primitives, making it simple to run AI, call APIs, manipulate numbers and text, without using any formulas or code. Chain these computations with actions to build simple but powerful workflows. 4. One subscription. Businesses want to consolidate their vendors. Agencies want simpler billing for clients. No-code solutions are often cobbled together with many tools, but we want building in Glide to be simpler than that. Business customers get access to scheduled triggers today, and webhook, email, and integration triggers are coming soon. Looking forward to your feedback!

This week I automated the process of identifying which clients are wrapping up a training and do not have anything else scheduled with us afterward. This week I built a small Power Automate flow that solves a problem we kept bumping into, but never took the time to automate. We store all of our client trainings in a single SharePoint list. Past, present, and future sessions all live together. The data was there, but the insight was not. The question we wanted to answer was simple: → Which clients are finishing a training this month and do not have anything else scheduled with us afterward? Manually, that meant filtering dates, scanning company names, cross checking future sessions, and then writing a follow up email. It worked, but it never happened as consistently as it should. So I automated it. Here is what the flow does: 1. First, it runs automatically on the first of every month. 2. It pulls all trainings that occur during the current month from SharePoint. 3. From there, it evaluates each company on that list and checks whether they have any trainings scheduled after the current month. If they do, the flow ignores them. 4. If they do not, the automation captures the company name and the name of their most recent training session and formats the results into a clean bulleted list. 5. Finally, it sends an email to our Director of Client Services with that list included in the body. Each bullet shows the company name and their latest training so follow up conversations are grounded in context. The email also includes a link to our full training library so she can easily dig deeper if needed. The outcome is simple but powerful. ★ Leadership gets a proactive view of clients who may need follow up. ★ Client services can prioritize outreach without pulling reports. No one has to remember to run a manual check every month. This is a good example of how automation does not need to be flashy to be valuable. Sometimes the best flows just make sure the right information reaches the right person at the right time, every time. If you are sitting on good data but still relying on reminders and manual checks, this is usually a sign there is an automation opportunity waiting. Let’s start building!

{{Modern enterprises don’t just collect data — they operationalize it.}} This AWS + Snowflake ETL architecture is designed for scalable, secure, and business-ready data pipelines across industries like financial services, e-commerce, healthcare, and SaaS. It supports batch and near-real-time ingestion, ensures data quality, and powers business intelligence & AI/ML initiatives. >>Where We Use This Architecture Financial Services → Fraud detection, credit risk scoring, regulatory compliance reporting. E-Commerce → Real-time customer behavior analytics, personalization, inventory optimization. Healthcare → Patient data integration, operational efficiency dashboards, predictive care analytics. SaaS Products → Usage analytics, product performance metrics, customer churn prediction. >> Architecture Walkthrough > Data Sources Relational: RDS (Postgres), operational DBs Streaming: Kafka, Kinesis APIs: External & 3rd-party data feeds >>Ingestion Layer AWS DMS → Continuous replication from databases AWS Glue (Ingest) → Scheduled batch ETL jobs Kinesis → Real-time data streaming from applications > Landing & Raw Zone (S3) Data stored in Landing (raw) and Bronze layers for full history & auditability >Processing Layer Databricks (PySpark) & EMR Spark for large-scale transformations Great Expectations for automated data quality checks > Orchestration & Automation Airflow (MWAA) for dependency-based scheduling AWS Step Functions & Lambda for event-driven workflows >Data Warehouse (Snowflake) Staging → Core → Business Marts modeled with dbt for version control & testing >Consumption Layer Power BI, Looker, Ad-hoc SQL for self-service analytics & decision-making > Monitoring & DevOps CloudWatch for real-time pipeline health monitoring GitHub Actions + Terraform for CI/CD & infrastructure as code ^Business Impact^ >Faster Time-to-Insight → From 12 hours down to 1 hour for complex ETL runs > Better Data Quality → 95%+ pass rate on automated data checks > Scalability → Handles 100M+ rows/day without performance degradation > Audit & Compliance → Full lineage and historical tracking for regulations like GDPR, HIPAA, PCI-DSS #DataEngineering #Snowflake #AWS #Databricks #ETL #DataPipeline #Airflow #dbt #CloudArchitecture #DataQuality #BigData #AnalyticsEngineering #MachineLearning #C2C #C2H #UsITrecruiters

While data producers—like data engineers—have benefitted significantly from applying engineering principles, data consumers have been largely left behind. Data teams have been building infrastructure, creating sophisticated pipelines and data models to make data reliable and accessible. But for the majority of users, even with these technical advancements, data consumption still remains a fragmented and inefficient experience. We need to apply a different set of engineering principles designed specifically for data consumers. Here are some ideas: 1) No-Code Abstractions The first principle for data consumers is the need for no-code abstractions. If we're still exposing unnamed datasets and reams of sql code with custom logic, it's impossible for business users to consistently derive value from their data. Metrics and metric relationships must become the new units of abstraction for business users. By utilizing metric trees, we can structure data into abstractions that align with how the business works, and how the business users naturally conceptualize, think and act. 2) Out-of-the-Box Calculations and Algorithms But simply having structured data isn't enough. We also need out-of-the-box calculations and algorithms to support common questions and workflows for end users. Data consumers often have to resort to manual computations or spend excessive time trying to answer repetitive business questions. To address this, we must provide ready-made calculations and algorithms that are tailored to common business needs like revenue tracking, retention analysis, or customer segmentation. 3) Clear Definition of Common Workflows Even with no-code abstractions and built-in calculations, the process won't work unless we clearly define common workflows that users follow. Business reviews, metric root-cause analysis, variance analysis against budgets, pacing metrics—these are all common workflows that data consumers engage in. By defining the scope of these workflows, and tools built with these in mind, we will make it significantly easier for users to quickly jump into their work without struggling to piece together these workflows from scratch every time. 4) Workflow Automation Finally, to truly unleash the power of data for consumers, we need to integrate workflow automation into the process. The goal should be for the software to do the work proactively. Imagine systems that automatically flag anomalies, surface insights, and highlight areas where actions are needed. In summary, if we are to truly empower data consumers across an organization, we must rethink how we design data systems and workflows. By focusing on no-code abstractions, providing out-of-the-box calculations, defining common workflows, and automating these processes, we will transform data consumption into a streamlined, actionable and delightful experience that drives better business decisions and outcomes.

Automating Stupidity: Why Data Must Dictate Your Process, Not Your Technology An Operations Director at a financial firm asked for a Tech organization to automate a slow process. He thought software would solve his problems. Is your automation making a bad process faster? You must fix the workflow first. They looked at his actual workflow. It was a nightmare of redundant steps and disconnected files. Buying a tool would only make the mess move faster. In the article, you will learn: ➡️ How to question why a process exists. Do this before buying software. ➡️ To use data to stop spreadsheet chaos. ➡️ To use metrics to drive continuous improvement. ➡️ How to find ways to use data science to personalize how customers interact with you. Organizations cannot reach operational excellence by layering shiny technology over chaotic workflows. They must do the unglamorous work of cultural alignment and process fixing first. Let the data show you the truth about your workflows. Build systems to support a better way of working. At Digital Transformation Strategist, we help global firms and small businesses with these demanding tasks. We bring a clear method to turn data into better processes. Let's fix your workflows first. Then we buy your software.

→ 𝐓𝐡𝐞 𝐈𝐧𝐯𝐢𝐬𝐢𝐛𝐥𝐞 𝐅𝐨𝐫𝐜𝐞 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐘𝐨𝐮𝐫 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲: 𝐃𝐞𝐯𝐎𝐩𝐬 𝐇𝐚𝐯𝐞 𝐲𝐨𝐮 𝐞𝐯𝐞𝐫 𝐰𝐨𝐧𝐝𝐞𝐫𝐞𝐝 𝐰𝐡𝐲 𝐬𝐨𝐦𝐞 𝐭𝐞𝐚𝐦𝐬 𝐚𝐥𝐰𝐚𝐲𝐬 𝐝𝐞𝐥𝐢𝐯𝐞𝐫 𝐟𝐚𝐬𝐭𝐞𝐫 𝐰𝐡𝐢𝐥𝐞 𝐨𝐭𝐡𝐞𝐫𝐬 𝐥𝐚𝐠? 𝐓𝐡𝐞 𝐬𝐞𝐜𝐫𝐞𝐭 𝐨𝐟𝐭𝐞𝐧 𝐥𝐢𝐞𝐬 𝐢𝐧 𝐡𝐨𝐰 𝐃𝐞𝐯𝐎𝐩𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐬 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲, 𝐞𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐥𝐲 𝐢𝐧 𝐝𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬. 𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰: • Automated Data Pipelines Continuous ingestion, processing, and validation happen without manual effort, speeding up data readiness. • Real-time Monitoring Instant insights into data quality and flow mean issues are caught and fixed immediately. • Data Version Control Tracking dataset versions alongside code ensures your data stays reliable and reproducible. • CI/CD for Data Models Automated deployment of machine learning models accelerates innovation and updates. • Infrastructure as Code Provisioning scalable environments quickly and reliably removes bottlenecks. • Security & Compliance Automation Policies are enforced automatically - audits become smoother and faster. • Collaborative DataOps Culture When teams work together seamlessly, data delivery becomes faster and more efficient. • Cloud-Native Data Tools Cloud services give you on-demand scaling and rapid workflow deployment. • Automated Data Testing Early detection of data issues prevents costly delays downstream. • Observability & Analytics Feedback Performance insights guide continuous improvement in pipelines. • AI-Driven Data Orchestration AI optimizes complex workflows, saving time and reducing errors. • Edge Data Processing Real-time, low-latency processing at the edge transforms customer experiences. DevOps is not just a methodology - it’s a supercharger for your project timelines. It turns complex, slow workflows into streamlined engines of innovation. follow Carlos Shoji for more insights

I'm thrilled to share my latest project where I leveraged CrewAI to automate end-to-end CSV analysis. By orchestrating specialized AI agents, I built a workflow that takes raw data and transforms it into actionable insights and a polished Markdown report. Here’s how it works: ✨ Agents in Action: 1️⃣ Dataset Context Specialist: Understands the dataset structure, types, and purpose. 2️⃣ Data Cleaning Specialist: Identifies missing values, outliers, and ensures data quality. 3️⃣ Visualization Expert: Creates compelling visualizations like histograms, scatter plots, and heatmaps. 4️⃣ Report Specialist: Compiles all findings into a detailed, publication-ready Markdown report. 💡 What’s Unique? Fully automated pipeline from dataset to insights. Visualizations dynamically generated using Python’s matplotlib and seaborn. Outputs a structured Markdown report with graphs embedded for easy sharing. 🌟 Why It Matters? This approach saves countless hours of manual effort, providing data teams with a scalable and efficient solution for analyzing datasets across industries. Here is the medium blog : https://lnkd.in/g6-MJ-ux here is the code: https://lnkd.in/gG74KV6B Curious to learn more or collaborate? Let’s connect and discuss how AI can transform your data workflows. 🔗 #AI #DataScience #Automation #DataVisualization #CrewAI #MachineLearning