Importance of Technology Integration

"𝐈𝐟 𝐈 𝐖𝐞𝐫𝐞 𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐒𝐀𝐏 𝐀𝐁𝐀𝐏 𝐢𝐧 2025 — 𝐇𝐞𝐫𝐞’𝐬 𝐖𝐡𝐚𝐭 𝐈’𝐝 𝐅𝐨𝐜𝐮𝐬 𝐎𝐧…" 𝑳𝒆𝒕 𝒎𝒆 𝒃𝒆 𝒃𝒓𝒖𝒕𝒂𝒍𝒍𝒚 𝒉𝒐𝒏𝒆𝒔𝒕: Learning just classical ABAP in 2025 is like learning MS Paint when the world is using Figma. You’ll still get things done… but you’ll always be behind. So if I were starting as a new SAP ABAP developer in 2025, this is exactly what I’d focus on — step by step. 1️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐞 𝐀𝐁𝐀𝐏 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 — 𝐁𝐮𝐭 𝐌𝐨𝐝𝐞𝐫𝐧 𝐀𝐁𝐀𝐏 𝐎𝐧𝐥𝐲 Focus on ABAP 7.4+ syntax — inline declarations, expressions, constructor operators. Understand how FOR, REDUCE, FILTER make code clean and functional. Don’t waste time on WRITE: and TOP-OF-PAGE. 𝐂𝐥𝐞𝐚𝐧 𝐜𝐨𝐝𝐞 > 𝐋𝐨𝐧𝐠 𝐜𝐨𝐝𝐞 2️⃣ 𝐉𝐮𝐦𝐩 𝐢𝐧𝐭𝐨 𝐑𝐀𝐏 (𝐑𝐞𝐬𝐭𝐟𝐮𝐥 𝐀𝐁𝐀𝐏 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥) RAP isn’t optional. It’s the future of SAP app development. Learn how to create: CDS-based data models Behavior definitions Fiori apps without writing a single line of UI5 Understand late numbering, draft-enabled apps, and managed vs unmanaged scenarios. 𝐑𝐀𝐏 𝐦𝐚𝐤𝐞𝐬 𝐲𝐨𝐮 𝐒/4𝐇𝐀𝐍𝐀 𝐚𝐧𝐝 𝐁𝐓𝐏 𝐫𝐞𝐚𝐝𝐲. 3️⃣ 𝐆𝐞𝐭 𝐂𝐨𝐦𝐟𝐨𝐫𝐭𝐚𝐛𝐥𝐞 𝐰𝐢𝐭𝐡 𝐂𝐃𝐒 𝐕𝐢𝐞𝐰𝐬 Learn how to: Expose data via CDS Use annotations for UI, analytics, OData Combine views using associations & joins Use VDM (Virtual Data Model) like a pro. 𝐂𝐃𝐒 𝐢𝐬 𝐭𝐡𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐑𝐀𝐏 𝐚𝐧𝐝 𝐀𝐁𝐀𝐏 𝐑𝐄𝐒𝐓 𝐀𝐏𝐈𝐬. 4️⃣ 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐀𝐏𝐈𝐬 𝐚𝐧𝐝 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 Learn OData V2 and V4 (you’ll need both) Understand how CPI, SAP Gateway, and API Management use your backend logic Practice building and consuming APIs — with Postman, CAP, or Integration Suite 𝐘𝐨𝐮𝐫 𝐀𝐁𝐀𝐏 𝐜𝐨𝐝𝐞 𝐢𝐬𝐧'𝐭 𝐣𝐮𝐬𝐭 𝐟𝐨𝐫 𝐒𝐀𝐏 𝐚𝐧𝐲𝐦𝐨𝐫𝐞 — 𝐢𝐭’𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐞𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦. 5️⃣ 𝐒𝐭𝐚𝐫𝐭 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐖𝐚𝐲 Use ABAP Unit, Test Seams, and Test Classes Get into TDD mindset: write test cases first Learn how automated testing fits into CI/CD pipelines 𝐀 𝐠𝐫𝐞𝐚𝐭 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐭𝐞𝐬𝐭𝐬. 𝐀𝐧 𝐞𝐩𝐢𝐜 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐬 𝐭𝐡𝐚𝐭 𝐭𝐞𝐬𝐭𝐢𝐧𝐠. 6️⃣ 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐒𝐀𝐏 𝐁𝐓𝐏 (𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦) Even if you’re ABAP-core, you must understand: SAP Build Apps for no-code UI SAP Event Mesh & Workflow for process integration Deployment in clean-core environments 𝐘𝐨𝐮𝐫 𝐟𝐮𝐭𝐮𝐫𝐞 𝐚𝐩𝐩𝐬 𝐰𝐢𝐥𝐥 𝐥𝐢𝐯𝐞 𝐨𝐧 𝐁𝐓𝐏, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐢𝐧𝐬𝐢𝐝𝐞 𝐒𝐀𝐏𝐆𝐔𝐈. If you're starting out or restarting your SAP journey in 2025, don't build muscle memory on outdated tools. Build for where SAP is going — not where it came from. And if you're already in the game? 𝐈𝐭'𝐬 𝐧𝐞𝐯𝐞𝐫 𝐭𝐨𝐨 𝐥𝐚𝐭𝐞 𝐭𝐨 𝐦𝐨𝐝𝐞𝐫𝐧𝐢𝐳𝐞 𝐲𝐨𝐮𝐫 𝐀𝐁𝐀𝐏 𝐦𝐢𝐧𝐝𝐬𝐞𝐭. ************ Be a part of my SAP Community : https://lnkd.in/gspu3-Fd

The insurance industry has been promising revolutionary change since the early 2010s ⌛ Your smart home would know when a pipe was about to burst and shut off the water before you knew there was a problem. As you locked the front door, your insurance would seamlessly shift from home to motor, adjusting your premium in real time based on road conditions, your driving history, and the weather. Every conference presentation showed the same timeline: "3-5 years away." 2015 came and went. Then 2020. Now we're halfway through 2025, the "blue sky thinking" sessions have fizzled out, and the industry has learned to be more cautious with timelines. But the fundamental challenge remains: we're still not delivering the transformation the industry keeps promising. What's different this time? AI has reached the capability threshold needed to handle insurance's complex, unstructured data reality. 👉 5 insurance AI applications that I'm genuinely excited about: ↳ End-to-end claims automation - you crash at 3am, AI handles everything overnight, you wake up with repairs booked and money transferred ↳ Intelligent fraud detection - AI spots fake damage photos, synthetic identities, and coordinated fraud rings operating across multiple insurers ↳ AI broker assistants - AI agents that simultaneously negotiate with multiple insurers, optimising your renewal terms automatically ↳ Cross-carrier fraud networks - AI systems that share intelligence across the entire industry ↳ Zero-friction underwriting - AI pulls from hundreds of data sources to assess risk instantly without you filling out anything The reality today? Only 11% of UK insurers report successful AI outcomes. Over 50% of pilots stall because of data quality issues. The winners by 2030 won't be the companies with the most cutting-edge AI - they'll be the ones who make it work consistently. The gap between promise and reality is still enormous. But for the first time in years, I'm genuinely optimistic we might finally start to close it. Are you seeing real AI progress in your industry, or is it still mostly hype? 👇

Ready for takeoff: Generative AI in insurance Boards at every insurance company are talking about gen AI. But the discussion has changed from POCs to now rapidly executing ideas for responsible, secure, scalable, and commercially successful gen AI. The direction of travel !! Some insurers are already using gen AI in the back office for tasks like knowledge management. But since insurance is all about probability & statistics, we expect to see it soon across the entire enterprise. The next wave of deployment will include areas like risk scenario modelling & enhancing cognitive processes (alongside AI and RPA) where human intervention was previously necessary. Customer-facing uses are being created and we expect insurers to use gen AI to understand customer preferences and drive personalized products and services. First things first  For a successful gen AI-led transformation, insurers need a well-planned and well-communicated  change roadmap made by a cross-functional team, from an enterprise-wide point of view. At this stage, leaders would be well-advised to develop an ecosystem of partnerships to share gen AI expertise, since there is serious competition for capable talent. Tackling data demands  Data is the greatest challenge to getting gen AI right, since all generative large language models rely on high quality data and excellent prompt engineering for their success. Insurers will need to make sure that the way they train their gen AI models is transparent, fair, and accountable. This means knowing where their data comes from, where it’s housed, how secure it is, and whether their planned uses are ethical and responsible under todays’ data laws. To train gen AI models effectively, they will have to put old customer data into today’s context and use synthetic data to overcome gaps in their data that could lead to bias, as well as look for potential unfair correlations with external data sets that could deliver poor outcomes. Keeping compliant   The data challenge is where regulators are focusing their attention. Already there are laws in some US states (Colorado & California), and in Europe, that require insurers to, e.g., backtest some gen AI-delivered outcomes. And then there are industry agnostic laws governing gen AI, that capture insurers too, e.g. use of external consumer data. Expect regulation to get tighter and more specific. The regulation requirements need not be considered adversarial. Instead, they should be prepared to answer on data lineage, audibility, and governance structures.  As insurers begin to implement gen AI across their business, it is important to focus on fair & transparent outcomes, build a strong data foundation, and partner with expert vendors to help them achieve their goals.  ... But it isn’t all challenge and competition, insurers should feel positive that Gen AI can help them to better deliver for and delight their customers. Ben Podbielski Ramesh Sethi Maria Kokiasmenos Genpact

Caution: Unpopular observation incoming. Are we thinking about climate tech all WRONG? I started my career in clean-tech in 2008 (solar, wind, then grid-scale storage). So many tech folks I met since then think climate tech is about breakthroughs. But one founder made me question everything. Let me take you back to 2012. I was analyzing battery technology startups (does anyone remember zinc-air?), watching charismatic founders pitch their "breakthrough innovations" to eager VCs. What did the Chinese do? Go for Li-Ion. In solar, the Germans bet on thin-film, but the Chinese INDUSTRIALIZED mono and poly-crystalline by 2008. The result? Billions were wasted on breakthrough promises. Fast forward to today, and the same pattern is repeating in clean building materials and carbon capture. The secret to scaling clean-tech innovation is systems engineering and radical industrialization, with improved—not breakthrough—chemical bonds. And that’s what I look for—I just don’t see it often. One such moment was when I met neustark way back when (which means "new powerful" in English). As we dove deeper into their approach, something struck me. The real trap isn’t backing the wrong technology. It’s being seduced by shiny promises of breakthroughs when the real opportunity is industrial execution. The German idiom: "The sparrow in your hand is better than the dove on your roof." Climate tech funding too often gets misallocated on huge promises, while the biggest impact comes from system integration. What policymakers should foster: - Scientific Hopium vs. Industrial Reality: Most VCs chase patents, but real winners build scalable systems with proven tech. - The Integration Game: Innovation isn’t just about new components—it’s about making them work together at scale. - Market Timing: We don’t always need future tech to solve today’s problems. The pieces are often already there. Neustark is a perfect example: - Partners with existing concrete recycling plants to integrate carbon storage. - Mineralizes captured CO₂ into recycled concrete aggregates, locking carbon in solid form. - Scales rapidly without new infrastructure—just plugging into existing systems. A system that could store millions of tons of CO₂ in the coming years. Not through hoping for groundbreaking chemistry, but through brilliant industrialization. I’m not against fundamental research—give us more of that. But why should VCs with no scientific background allocate capital into chemical breakthroughs? Change my mind. >>>>>> More on The System Integrator Model: https://lnkd.in/em9v73cy #CleanTech #VentureCapital #Sustainability #Innovation #ClimateAction P.S. Not an investor in Neustark, just genuinely impressed.

The Power of Integrations! Shensi Ding of Merge breaks down the importance of integrations for companies navigating today’s fragmented software landscape. • Integration Complexity: With companies often using 200+ software tools, seamless data syncing is essential—manual data transfers just can’t keep up. • Buyer Expectations: Today’s buyers demand products that effortlessly sync with existing tools, avoiding data silos and ensuring consistent information. • Building Partnerships: Merge has partnered with major platforms thanks to persistent networking and evangelizing the value of a unified API. • Becoming a Source of Truth: Like Salesforce, companies aspire to be essential hubs where others build around them, creating stickiness and a critical role in their tech stack.

Your customers can feel your internal chaos. When your systems are fragmented, the first people to notice are your customers. They receive inconsistent information, suffer from service delays, and get asked for the same details multiple times. In my experience, this is one of the fastest ways to lose trust and business. The truth is, poor data quality leads to unreliable insights and hesitant decisions internally, but it creates a terrible experience externally. And the stakes are high. 84% of customers will switch to a competitor after just one poor experience. When it costs 3.5 times more to acquire a new customer than to keep an existing one, you can't afford to get this wrong. This isn't an IT problem; it's a strategic business problem. Getting the architecture right with scalable systems and ensuring clean, consistent data isn't just about efficiency; it's about survival. We explore this connection between internal systems and customer loyalty in our new guide. When was the last time a customer had to tell you something that your systems should have already known? #CustomerExperience #DataQuality #CX

Implementing IoT solutions for monitoring and managing energy consumption requires an integrated vision combining technology, data analysis, security, and sustainability to achieve significant efficiency and cost savings. Internet of Things (IoT) IoT refers to a network of physical devices that communicate via the Internet. These include sensors, smart meters, thermostats, and HVAC systems, all of which work together to collect and share real-time energy consumption data. Energy Consumption Monitoring Using smart sensors and meters allows real-time tracking of energy use, enabling the identification of inefficiencies and the implementation of immediate corrective measures to reduce unnecessary energy expenditure. Energy Management Automation systems in IoT can control lighting, heating, and cooling based on environmental data and occupancy. This optimization reduces energy waste without compromising comfort and operational needs. Data Analysis Advanced data analysis techniques, including big data and machine learning, help identify trends and consumption patterns. These insights drive long-term energy-saving strategies and continuous improvement in energy performance. Integration with Existing Systems Ensuring compatibility and seamless integration of new IoT devices with existing systems is crucial. Interoperability allows for smooth data exchange and functionality, enhancing overall system efficiency. Data Security Protecting the data collected by IoT devices is essential. Implement robust security measures, including encryption and access control, to safeguard sensitive energy data and ensure only authorized personnel have access. Economic and Environmental Benefits Efficient energy management leads to substantial operational cost savings, and reducing energy consumption supports corporate sustainability goals by lowering the organization’s carbon footprint. Implementation and Maintenance The implementation process includes planning, device installation, system integration, and staff training. Ongoing maintenance and regular updates ensure the IoT systems remain efficient and effective over time. Regulations and Standards Compliance with local and international energy management and IoT standards is vital. Certifications ensure the quality and security of the IoT solutions, meeting regulatory requirements and industry best practices. Staff Training Training staff on the use and maintenance of IoT systems is essential. Building an energy-conscious culture within the organization promotes efficient energy use and maximizes the benefits of IoT solutions. #IoT #EnergyManagement #BusinessEfficiency Ring the bell to get notifications 🔔

The Website Consent Problem: Too Many Tools, Too Little Harmony Websites rely on various third-party tools like analytics platforms, ad managers, and tag managers. While these tools are essential for functionality, each has unique privacy settings. The real challenge is ensuring they work together to honor user consent. When integration fails, consent flows break, leading to compliance risks and loss of trust. Websites often use over 20 different types of tools. Key categories of website tools: 1. Analytics tools Google Analytics and Adobe Analytics track user behavior and performance. They rely on settings like Google Consent Mode to operate compliantly. Without proper integration, they may collect data before consent. 2. Ad management platforms Prebid.js and Google Ad Manager manage ad delivery. They need frameworks like IAB TCF strings to serve personalized ads only with user consent. Misconfigurations can lead to tracking and legal risks. 3. Tag management systems (TMS) Google Tag Manager and Tealium control when other tools are deployed. The CMP (Consent Management Platform) must load first to capture consent preferences. Without proper setup, tools may fire prematurely. 4. Heatmaps and session recording tools Hotjar and FullStory track user interactions to improve experience. These tools collect sensitive data and should operate only with explicit consent. Poor configurations can result in privacy issues. Why honoring consent is a challenge? - Fragmented ecosystem Most tools operate in silos, making it hard to create a unified consent flow. Without integration, tools don’t respect shared consent signals. - Regulatory complexity Privacy laws vary across regions, requiring different approaches for compliance (e.g., opt-in vs. opt-out). Configuring tools to meet global regulations adds complexity. - Lack of real-time monitoring Consent flows change as tools are updated or replaced. Without regular monitoring, settings can become outdated, leading to unauthorized data collection. - Misaligned priorities Revenue goals often take precedence over compliance. This results in shortcuts like firing tracking scripts before consent is obtained, risking penalties and user trust. What should Privacy Teams do? 1. Audit your website List all third-party tools and document their data flows. 2. Understand privacy settings Review each tool’s privacy settings and integration with the CMP. 3. Fix tag management systems Ensure the CMP loads first to capture user consent before other tags fire. 4. Verify CMP integration Confirm the CMP communicates consent signals to all tools for consistency. 5. Automate, automate, automate Manual consent flow monitoring is time-consuming and prone to errors. Work with tech teams to automate consent checks or use vendors specializing in consent monitoring automation. This will help in catching issues early on. #Privacy pros, How are you auditing your website’s tools and #consent flows?

A big 4 consulting firm implemented ERP which caused a major business disruption for a client   Top level partners in top notch suits made best promises in a beautifully crafted presentation.   The client got excited. Deal signed.   I joined the party at the SIT phase. Which could not complete.   The integration between ERP, WMS and boundary systems was failing.   SIT lasted for 3 more months. Go live data had to be pushed. But the integration still did not work.   The burn rate was insane.   Eventually the leadership decided to make it live and forced half-baked solution.   Yay! Everyone was happy until they realized what a sh*t show they were in.   Right after go live the planners and supply chain operations realized that the stock on hand in ERP, WMS, their custom system and on the shelf were all different.   They had to call the warehouse to make sure they had the right quantity of equipment.   The result: ·      All the departments started overordering to cover up for their projects ·      2 million dollar sales were lost. No stock. Couldn’t deliver ·      Inventory across the supply chain grew by $20M ·      It took 18 months to stabilize the system   Why did that happen? 1.      The vendor recently bought the WMS solution and did not yet build native integration. 2.      Poor integration between the systems. Transactions were stuck due to errors. 3.      Terrible user experience. Warehouse workers could not perform their role in a system and circumvented the restrictions. 4.      Lack of training and end user support   Want to avoid this costly mistake? Here’s what you should consider. ·      ERP can look great on a slide deck but may not necessarily fit your business ·      ERP can fit your business but not your boundary systems ·      An implementation partner can have a big brand name, but one integration architect can screw the whole thing ·      Be careful with newly purchased solutions by the vendor. They might not be integrated into their ERP yet.   In summary:   Don’t trust ERP fairy tales.   Do your due diligence.   #TheERPGuy

The insurance industry is undergoing a technology renaissance. And it’s more than just digitization. To show how far we’ve come, I’m sharing an infographic that traces the evolution of insurance and insurtech over the past 100 years. (Credits: Amir Kabir) But what’s happening now? Here’s a closer look at the current technology landscape in insurance (2024–25): 1️⃣𝐀𝐈 & 𝐌𝐋 → AI is now embedded across the insurance value chain. → Underwriting is becoming more dynamic, with AI models analyzing far more data than traditional actuarial methods. → In claims, CV assesses vehicle damage from photos, while NLP extracts insights from adjuster notes and legal documents. According to Allied Market Research AI in insurance was valued at $4.59 billion in 2022 and is projected to reach $79.86 billion by 2032, growing at a CAGR of 33.06%. 2️⃣𝐈𝐨𝐓 & 𝐓𝐞𝐥𝐞𝐦𝐚𝐭𝐢𝐜𝐬 → Connected devices are allowing insurers to move from reactive to proactive risk management. → In auto insurance, premiums now reflect actual driving behavior. → In home insurance, smart sensors help detect risks early like water leaks or fire hazards. → In health, wearables support wellness programs and enable dynamic underwriting. The global IoT insurance market is projected to grow from $49.4 billion in 2024 to $76.73 billion by 2029. 3️⃣𝐁𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧 & 𝐒𝐦𝐚𝐫𝐭 𝐂𝐨𝐧𝐭𝐫𝐚𝐜𝐭𝐬 → Smart contracts automatically trigger payments when conditions are met, such as in parametric travel or weather insurance. → Blockchain-based systems create tamper-proof records, reducing duplicate or exaggerated claims. → Shared blockchain data pools enable secure, cross-insurer collaboration. PwC estimates that blockchain can reduce fraud-related losses by up to 30 percent in insurance. 4️⃣𝐋𝐨𝐰-𝐂𝐨𝐝𝐞 & 𝐍𝐨-𝐂𝐨𝐝𝐞 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 → Insurers are inclined towards business teams to create workflows & applications without deep IT involvement. → These platforms help launch new digital products faster → Streamline customer onboarding and service → Improve internal efficiency through automation This is helping address longstanding IT backlogs while improving agility. 5️⃣ 𝐇𝐲𝐛𝐫𝐢𝐝 𝐂𝐥𝐨𝐮𝐝 & 𝐀𝐏𝐈 𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦𝐬 → Cloud solutions integrate more easily with third-party services, from eKYC to health data. → Open APIs allow insurers to embed offerings into fintech, travel, or retail ecosystems, expanding distribution. → The move to modular, cloud-native platforms is now foundational to scaling innovation. ----------------------------------------- What does this mean going forward? Insurers that adopt these technologies thoughtfully are better positioned to: 📌 Anticipate and prevent risk 📌 Personalize offerings 📌 Streamline operations 📌 Build trust through better service and transparency Which of these technologies do you think will shape the next decade of insurance? Do share your thoughts in the comment section below. #insurance #insurtech #ai