Integrating IoT Devices in Business

They were hemorrhaging money on digital tools their managers refused to use. The situation: A retail giant in the diamond industry with post-COVID digital sales tools sitting unused. Store managers resisting change. Market volatility crushing performance. Here's what every other company does: More training on features. Explaining benefits harder. Pushing adoption metrics. Here's what my client did instead: They ignored the technology completely. Instead, they trained 200+ managers on something nobody else was teaching; how to fall in love with change itself. For 8 months, we didn't focus on the digital tools once. We taught them Change Enthusiasm®, how to see disruption as opportunity, resistance as data, and overwhelm as information. We certified managers in emotional processing, not technical skills. The results were staggering: → 30% increase in digital adoption (without a single tech training session) →  2X ROI boost for those who embraced the mindset →  25% sales uplift in stores with certified managers →  96% of participants improved business outcomes Here's the breakthrough insight: People don't resist technology. They resist change. Fix the relationship with change, and adoption becomes automatic. While competitors were fighting symptoms, this company cured the disease. The secret wasn't better technology training, it was better humans. When managers learned to thrive through change, they stopped seeing digital tools as threats and started seeing them as allies. Most companies are solving the wrong problem. They're trying to make people adopt technology. We help people embrace transformation. The results speak for themselves. What would happen if you stopped training on tools and started training on change? ♻️ Share if you believe the future belongs to change-ready organizations 🔔 Follow for insights on making transformation inevitable, not optional

Are you keeping track of your company’s emissions in real-time? It might sound like a small step, but monitoring emissions continuously could be the shift we need for more sustainable industries. Imagine knowing every hour – or even every minute – exactly what’s going into the air, especially in fields like oil and gas, where methane leaks are a growing concern. The stakes are high, with increasing regulatory pressure worldwide and ambitious goals from global conferences like COP26. In this environment, knowing your emissions isn’t just good business; it’s essential. Continuous Emissions Monitoring (CEM) systems offer businesses real-time data about pollutants in the air, water, and even noise pollution. It’s no longer about random sampling or occasional checks; instead, CEM provides a steady, live feed of emissions data directly to the cloud, often powered by IoT. From methane to volatile organic compounds (VOCs) and beyond, companies can see their environmental impact unfold in real time, offering a unique opportunity to act fast on unexpected trends or leaks. For instance, imagine an oil company that can catch a small methane leak early because of real-time monitoring, preventing it from turning into a costly – and environmentally damaging – problem. By having a clear picture of emissions data as it happens, companies can save time, meet regulatory expectations, and ultimately reduce their environmental footprint. Switching to continuous monitoring may seem challenging, especially for large or remote facilities. However, newer IoT solutions have brought down costs and increased accessibility, allowing even larger companies to deploy CEM across wide areas or multiple locations. Instead of using traditional detection methods that are often expensive and labour-intensive, businesses can adopt a system that’s more adaptable to their needs and budget. With emissions monitoring, we’re not just tracking data – we’re getting insight that drives better decisions, enhances accountability, and ultimately pushes us closer to a cleaner, more sustainable future. Is your organization ready to embrace that kind of visibility?

𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 1: 𝐓𝐡𝐞 𝐃𝐚𝐰𝐧 𝐨𝐟 𝐒𝐦𝐚𝐫𝐭 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐭𝐡𝐞 𝐄𝐝𝐠𝐞 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐂𝐥𝐨𝐮𝐝 In 2024, the spotlight is on smart connectivity, a critical evolution that promises to redefine IoT by enhancing the synergy between device intelligence at the Edge and cloud capabilities. This transformative approach is set to impact organizations across industries by enabling more efficient, secure, and intelligent operations. 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬: 📌𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠: With the acceleration of Edge processing, organizations can leverage local data analysis for quicker, more autonomous decision-making. This reduces dependency on cloud processing, thereby minimizing latency and enhancing real-time responses. 📌𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Full-stack integration means that IoT devices will be more self-reliant, requiring less intervention and manual oversight. This leads to streamlined operations, lower operational costs, and reduced potential for human error. 📌𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: The emphasis on secure, resilient connectivity ensures that data is protected from endpoint to cloud. This is crucial for organizations dealing with sensitive information, helping them meet regulatory compliance standards like GDPR and HIPAA more effectively. 📌𝐂𝐨𝐬𝐭 𝐚𝐧𝐝 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Intelligent connectivity allows devices to select the most cost-effective and efficient network paths. This adaptability can lead to significant savings on data transmission costs and optimize network resource usage. 📢 𝐌𝐲 𝐓𝐡𝐨𝐮𝐠𝐡𝐭𝐬 The prediction of smart connectivity as a cornerstone for IoT in 2024 resonates with a growing trend toward distributed intelligence and the need for more agile, secure, and efficient operations. From an organizational perspective, this shift is not merely technological but strategic, offering a pathway to transform how businesses interact with digital infrastructure, manage data, and deliver services. 📌𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞: Organizations that embrace smart connectivity will gain a competitive edge through enhanced operational agility, improved customer experiences, and a stronger posture on security and compliance. 📌𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬: This new paradigm opens doors for innovative applications and services that leverage Edge intelligence, from advanced predictive maintenance to dynamic supply chain management and beyond. 📌𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐚𝐧𝐝 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: While the benefits are clear, organizations must also navigate the complexities of integrating this technology. This includes ensuring interoperability across diverse devices and platforms, managing the increased complexity of decentralized data processing, and addressing the security vulnerabilities that come with expanded IoT ecosystems.

This concept is the reason you can track your Uber ride in real time, detect credit card fraud within milliseconds, and get instant stock price updates.  At the heart of these modern distributed systems is stream processing—a framework built to handle continuous flows of data and process it as it arrives.     Stream processing is a method for analyzing and acting on real-time data streams. Instead of waiting for data to be stored in batches, it processes data as soon as it’s generated making distributed systems faster, more adaptive, and responsive.  Think of it as running analytics on data in motion rather than data at rest.  ► How Does It Work?  Imagine you’re building a system to detect unusual traffic spikes for a ride-sharing app:  1. Ingest Data: Events like user logins, driver locations, and ride requests continuously flow in.   2. Process Events: Real-time rules (e.g., surge pricing triggers) analyze incoming data.   3. React: Notifications or updates are sent instantly—before the data ever lands in storage.  Example Tools:   - Kafka Streams for distributed data pipelines.   - Apache Flink for stateful computations like aggregations or pattern detection.   - Google Cloud Dataflow for real-time streaming analytics on the cloud.  ► Key Applications of Stream Processing  - Fraud Detection: Credit card transactions flagged in milliseconds based on suspicious patterns.   - IoT Monitoring: Sensor data processed continuously for alerts on machinery failures.   - Real-Time Recommendations: E-commerce suggestions based on live customer actions.   - Financial Analytics: Algorithmic trading decisions based on real-time market conditions.   - Log Monitoring: IT systems detecting anomalies and failures as logs stream in.  ► Stream vs. Batch Processing: Why Choose Stream?   - Batch Processing: Processes data in chunks—useful for reporting and historical analysis.   - Stream Processing: Processes data continuously—critical for real-time actions and time-sensitive decisions.  Example:   - Batch: Generating monthly sales reports.   - Stream: Detecting fraud within seconds during an online payment.  ► The Tradeoffs of Real-Time Processing   - Consistency vs. Availability: Real-time systems often prioritize availability and low latency over strict consistency (CAP theorem).  - State Management Challenges: Systems like Flink offer tools for stateful processing, ensuring accurate results despite failures or delays.  - Scaling Complexity: Distributed systems must handle varying loads without sacrificing speed, requiring robust partitioning strategies.  As systems become more interconnected and data-driven, you can no longer afford to wait for insights. Stream processing powers everything from self-driving cars to predictive maintenance turning raw data into action in milliseconds.  It’s all about making smarter decisions in real-time.

Blending IO psychology with digital innovation flipped the results of our last tech rollout. Most teams never connect these dots—here's why it changes everything ↓ Tech implementations often fail not because of the technology, but due to human factors. The deployment to a large international pharma company was heading for disaster until we brought in IO psychologists. They helped us understand: - How different personality types interact with new systems - The impact of change on team dynamics - Ways to reduce resistance and boost adoption We tailored our approach based on these insights: - Customized training for different learning styles - Change champions selected based on influence networks - Communication strategies aligned with team cultures The results were staggering: - 94% adoption rate within 3 months - 40% increase in user satisfaction scores - 25% boost in productivity post-implementation Key takeaway: Technology and human behavior are deeply intertwined. By considering both, we unlocked synergies we never thought possible. Next time you're planning a tech rollout, remember: The most powerful integration isn't between systems, but between tech and human psychology. Embrace this approach to transform your digital initiatives. PS - and if you know this story, you also know how it set me on the path for my PhD in IO Psychology.

The Long Road to IoT Success The promise of the Internet of Things (IoT) is clear – connecting devices and assets to collect data, monitor conditions, and enable efficiencies, cost savings, and new revenue streams. Yet many enterprises struggle to move from IoT proof-of-concept to full deployment. Research shows the average IoT implementation takes 18-24 months to go live. Why does it take so long and how can organizations accelerate IoT initiatives? Based on my experience, I’ve identified some core challenges that lead to lengthy deployment timelines: Data Complexity: Most IoT deployments involve multiple sensors collecting vast amounts of data from disparate systems. Cleansing, normalizing and integrating this data is difficult and time-consuming. Lack of in-house skills and incomplete data management strategies often stall projects. Security: IoT introduces many new cybersecurity risks with so many connected devices and data flows. Building comprehensive protections across hardware, software, network layers takes time. Most organizations underestimate this critical component. Securing both IT and OT environments makes this more complex. Technology Immaturity: The IoT technology stack is complex with components like devices, connectivity, platforms, applications and analytics. There is still fragmentation and lack of standardization. Integrating pieces is challenging without proven blueprints. Organizational Silos: IoT requires collaboration across IT, OT, engineering, operations and business teams. Lack of alignment leads to false starts. Managing expectations and building partnerships across departments is essential. Beyond these technical and operational hurdles, organizational culture misalignment is a major, yet overlooked barrier to IoT success. If engineering, IT and business teams do not share a common vision and work collaboratively, IoT initiatives stall. A culture focused on siloed metrics and legacy processes rather than cross-functional problem solving will severely hamper any digital transformation effort. Most importantly, organizations need to ensure they are pursuing IoT not just for the sake of adopting a shiny new technology, but because they have defined a clear business model and path to value. Without a well-considered ROI and business justification, IoT deployments meander without real impact. A strong business sponsor must guide the initiative and keep it focused on measurable outcomes that enhance the organization’s objectives. To accelerate IoT deployments, leading organizations focus first on high-value use cases, centralized data platforms and developing talent. But they also cultivate partnerships between teams to align on goals. With the right strategies, business alignment and culture, IoT can start delivering ROI more quickly. But it takes realistic planning, collaboration and skill building to smooth the long road - especially in complex legacy environments.

Six ways to ensure your industrial transformation avoids the common pitfalls of technology led initiatives and successfully scales across the business... 1) Ensure your team has defined an operational architect role for supporting governance and decision making across IT-OT. 2) Ensure your operational architecture has prioritized personas with use cases - based on impact/effort and aligned with enterprise priorities. 3) Ensure your governance process has defined the entire life cycle for monument systems, best of breed systems, and homegrown systems. 4) Ensure solutions moving from vision to value have an identified solution owner that comes from the primary value chain function, with cross-functional credibility. 5) Ensure there is sufficient pull before scaling a solution, i.e. value chain leaders believe in the value and there are sufficient champions across the organization. 6) Ensure that scaling is done in alignment with the operating model. Solutions should either support/digitize existing ways of working (decision making and execution) or the operating model needs to evolve along with the scaling of solutions. Too many COOs fall into the trap of running their industrial transformation teams like an internal IT organization... and they end up disconnected from operations and stranded in pilot purgatory. Are there any other ways you have successfully scaled industrial transformation?

📱 Just spoke with a CTO who spent 6 months debugging connectivity issues instead of building their product. They're not alone. In the last month, I've talked with dozens of IoT leaders whose growth is being strangled by unreliable connectivity. Here's what I'm seeing in today's connected device landscape: 1. The hidden cost of connectivity problems • Devices failing in the field = emergency engineering resources • Support tickets pile up while your team troubleshoots network issues • Product launches delayed by months due to connectivity challenges 2. Why it's getting worse, not better • Global supply chain pressures mean devices are being deployed in new regions • Carrier network changes happen with little warning • Engineering teams are stretched thinner than ever 3. The real impact on your business • Every outage costs an average of $5,600 per hour in lost revenue • 40% of customers won't return after a poor connectivity experience • Engineering teams spend up to 30% of their time on connectivity troubleshooting I've spent 10+ years in the IoT connectivity space, and I've never seen teams more frustrated by the basics just not working. That's why we created our free "Outage-Proof Connectivity Assessment" - a 15-minute evaluation that identifies your specific connectivity vulnerabilities and provides actionable recommendations. No sales pitch. Just practical advice from engineers who've solved these problems for thousands of companies across 190+ countries. Is your team spending more time debugging connectivity than building your core product? DM me for access to the assessment. I'll personally review your results.

$500k in spoiled vaccines vs. $50k in preventive tech. The difference? Not just technology—it’s proactive ownership. Some companies: - Depend on manual checks - React after the damage is done - Accept losses as "the cost of business" But the smarter ones? They’re preventing loss before it happens—by embedding real-time monitoring into their cold chain logistics. Here’s how leading providers are doing it with Azure: 1️⃣ IoT sensors are installed in transport containers to monitor temperature and humidity, feeding data directly into Azure IoT Hub. This integration allows logistics companies to access real-time data in their systems without disrupting operations. 2️⃣ Data flows seamlessly into Azure IoT Hub, where pre-configured modules handle the heavy lifting. The configuration syncs easily with ERP and tracking software, so companies avoid a complete tech rebuild while gaining real-time visibility. 3️⃣ Instead of piecing together data from multiple sources, Azure Data Lake acts as a secure, scalable repository. It integrates effortlessly with existing storage, reducing workflow complexity and giving logistics teams a single source of truth. 4️⃣ Then, Azure Databricks processes this data live, with built-in anomaly detection directly aligned with the current machine learning framework. This avoids the need for new workflows, keeping the system efficient and user-friendly. 5️⃣ If a temperature anomaly occurs, Azure Managed Endpoints immediately trigger alerts. Dashboards and mobile apps send notifications through the company’s existing alert systems, ensuring immediate action is taken. The bottom line? If healthcare companies want to reduce risk truly, proactive monitoring with real-time Azure insights is the answer. In a field where every minute matters, this setup safeguards patient health and reputations. Now, how would real-time monitoring fit into your logistics strategy? Share your thoughts below! 👇 #Healthcare #IoT #Azure #Simform #Logistics ==== PS.  Visit my profile, @Hiren, & subscribe to my weekly newsletter: - Get product engineering insights. - Discover proven development strategies. - Catch up on the latest Azure & Gen AI trends.

They're scared, not stubborn. I recently spoke to a frustrated CEO. "My people just won't adapt to our AI tools. We've spent millions, and they're resisting everything." After twenty minutes of conversation, I asked her a simple question: "When was the last time you personally used any of these tools?" Her silence told me everything I needed to know. The greatest leadership failure happening right now isn't poor technology selection. It's the disconnect between imposing AI change from above while remaining comfortably distant from the disruption yourself. Your team isn't resisting AI because they're stubborn. They're scared. And they're watching to see if you're willing to go first, to be vulnerable, to learn something new, to stumble a bit in front of others. Research emphasizes that organizations where executives actively use and demonstrate AI tools experience significantly higher adoption rates compared to those where leadership remains hands-off. This isn't delegation territory. This is leading by example. If you're frustrated by AI resistance in your organization, try this: Schedule three hours next week to sit with your team and use the tools together. Don't observe them using it. Use it yourself. Ask questions. Show your learning process. Be comfortable with not knowing every answer. The AI revolution happening in your company needs leaders who aren't just sponsoring change but experiencing it alongside their people. When it comes to technological transformation, proximity creates possibility. AI is 10% Technology - 90% People. What's one way you've successfully led your team through technological change by demonstrating rather than directing?