Workplace Trends

Data Integration Revolution: ETL, ELT, Reverse ETL, and the AI Paradigm Shift In recents years, we've witnessed a seismic shift in how we handle data integration. Let's break down this evolution and explore where AI is taking us: 1. ETL: The Reliable Workhorse      Extract, Transform, Load - the backbone of data integration for decades. Why it's still relevant: • Critical for complex transformations and data cleansing • Essential for compliance (GDPR, CCPA) - scrubbing sensitive data pre-warehouse • Often the go-to for legacy system integration 2. ELT: The Cloud-Era Innovator Extract, Load, Transform - born from the cloud revolution. Key advantages: • Preserves data granularity - transform only what you need, when you need it • Leverages cheap cloud storage and powerful cloud compute • Enables agile analytics - transform data on-the-fly for various use cases Personal experience: Migrating a financial services data pipeline from ETL to ELT cut processing time by 60% and opened up new analytics possibilities. 3. Reverse ETL: The Insights Activator The missing link in many data strategies. Why it's game-changing: • Operationalizes data insights - pushes warehouse data to front-line tools • Enables data democracy - right data, right place, right time • Closes the analytics loop - from raw data to actionable intelligence Use case: E-commerce company using Reverse ETL to sync customer segments from their data warehouse directly to their marketing platforms, supercharging personalization. 4. AI: The Force Multiplier AI isn't just enhancing these processes; it's redefining them: • Automated data discovery and mapping • Intelligent data quality management and anomaly detection • Self-optimizing data pipelines • Predictive maintenance and capacity planning Emerging trend: AI-driven data fabric architectures that dynamically integrate and manage data across complex environments. The Pragmatic Approach: In reality, most organizations need a mix of these approaches. The key is knowing when to use each: • ETL for sensitive data and complex transformations • ELT for large-scale, cloud-based analytics • Reverse ETL for activating insights in operational systems AI should be seen as an enabler across all these processes, not a replacement. Looking Ahead: The future of data integration lies in seamless, AI-driven orchestration of these techniques, creating a unified data fabric that adapts to business needs in real-time. How are you balancing these approaches in your data stack? What challenges are you facing in adopting AI-driven data integration?

Gone are the days when the only way to know something was wrong with your machinery was the ominous clunking sound it made, or the smoke signals it sent up as a distress signal. In the traditional world of maintenance, these were the equivalent of a machine's cry for help, often leading to a mad dash of troubleshooting and repair, usually at the most inconvenient times. Today, we're witnessing a seismic shift in how maintenance is approached, thanks to the advent of Industry 4.0 technologies. This new era is characterized by a move from the reactive "𝐈𝐟 𝐢𝐭 𝐚𝐢𝐧'𝐭 𝐛𝐫𝐨𝐤𝐞, 𝐝𝐨𝐧'𝐭 𝐟𝐢𝐱 𝐢𝐭"  philosophy to a proactive "𝐋𝐞𝐭'𝐬 𝐟𝐢𝐱 𝐢𝐭 𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐭 𝐛𝐫𝐞𝐚𝐤𝐬" mindset. This transformation is powered by a suite of digital tools that are changing the game for industries worldwide. 𝐓𝐡𝐫𝐞𝐞 𝐍𝐮𝐠𝐠𝐞𝐭𝐬 𝐨𝐟 𝐖𝐢𝐬𝐝𝐨𝐦 𝐟𝐨𝐫 𝐄𝐦𝐛𝐫𝐚𝐜𝐢𝐧𝐠 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞: 𝟏. 𝐌𝐚𝐤𝐞 𝐅𝐫𝐢𝐞𝐧𝐝𝐬 𝐰𝐢𝐭𝐡 𝐈𝐨𝐓 By outfitting your equipment with IoT sensors, you're essentially giving your machines a voice. These sensors can monitor everything from temperature fluctuations to vibration levels, providing a continuous stream of data that can be analyzed to predict potential issues before they escalate into major problems. It's like social networking for machines, where every post and status update helps you keep your operations running smoothly. 𝟐. 𝐓𝐫𝐮𝐬𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐂𝐫𝐲𝐬𝐭𝐚𝐥 𝐁𝐚𝐥𝐥 𝐨𝐟 𝐀𝐈 By feeding the data collected from IoT sensors into AI algorithms, you can uncover patterns and predict failures before they happen. AI acts as the wise sage that reads tea leaves in the form of data points, offering insights that can guide your maintenance decisions. It's like having a fortune teller on your payroll, but instead of predicting vague life events, it provides specific insights on when to service your equipment. 𝟑. 𝐒𝐭𝐞𝐩 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐰𝐢𝐭𝐡 𝐌𝐢𝐱𝐞𝐝 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 Using devices like the Microsoft HoloLens, technicians can see overlays of digital information on the physical machinery they're working on. This can include everything from step-by-step repair instructions to real-time data visualizations. It's like giving your maintenance team superhero goggles that provide them with x-ray vision and super intelligence, making them more efficient and reducing the risk of errors. ******************************************** • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Behind every opportunity is a relationship, and behind every relationship is a conversation. Networking is about building real connections that last and have the potential to help you find your next opportunity. Data shared by the University of Maryland’s Department of Economics indicates you won’t find 70% of available jobs on any site that posts open positions. Those positions are usually found on a company’s internal network, often by referral. In other words, relationships can make the difference between finding a job or not. That’s no surprise to me. Throughout my journey, from engineer to investor, relationships have been a constant driver of growth. Mentors, colleagues and peers have not only opened doors, but also challenged my thinking, sharpened my skills and inspired my vision. Here’s what I have learned: - Be curious: Ask questions that show you care about people’s stories. - Be intentional: Connect with purpose, not just for your own gain. - Be consistent: Follow up, follow through and add value where you can. Networking isn’t a one-time event. It requires maintaining ongoing relationships rooted in trust and genuine interest in other people’s lives. Whether you’re just starting out on your professional journey or deep into your field, relationships are what power careers.

I’m going to be brutally honest with you. Because I know this will help you in your career. When you are looking for a job, you have probably heard this a million times: Networking will help you land job offers. And, it's true. But, as someone who has received over 500 messages from job seekers, I need to say this: A lot of people are approaching networking the wrong way. Networking isn’t just about sending a message to a stranger and asking them for a job. It’s not just about sending a message to an influencer asking to “pick their brain.” It’s about building a RELATIONSHIP with someone: You do that by… -Finding people who are RELEVANT to your career interests  -Reading someone’s LinkedIn profile and asking questions about their specific background  -Commenting on their posts  -Thinking of ways you can support them 99 percent of people don’t do this. You can stand out in the job search by implementing these strategies.

Working paper from researchers at NUS, Rochester, and Tsinghua argues AI creates an "inflection point" for freelancers. Before hitting this point, AI significantly boosts freelancer earnings (web developers saw a +65% increase by using AI as a productivity tool). However, after crossing the inflection point, AI begins replacing workers (translators experienced a -30% drop in earnings). They argue that this shift appears to be one-way: once AI starts replacing workers in a field, that trend doesn't reverse with newer AI versions.

This might be a controversial hot take, but I don't think the idea of employee equity (in most cases) makes sense. After several experiences, including being an early employee at companies that have IPO'd through a SPAC and a founding member of a company that exited, my view on this has changed completely. I want to caveat, that I do believe, in many cases, equity packages make sense for senior executives and very early employees. However, for the most part, I believe equity compensation for junior and mid level employees in lieu of pay is a bad idea. A few reasons: 1) Misunderstanding Equity: Most junior employees have no idea what equity actually means. They believe that owning a piece of the company will make them rich upon an exit and that taking "more shares" in lieu of higher pay is a guaranteed payday. This is such a gamble and so misleading. Most companies don't exit. It's a huge risk that may never pay off. 2) Logistics of Options: Many junior employees don't understand the logistics of "options." While many of us know that being granted options doesn't mean owning them, this is typically elusive for younger, more junior employees. 3) Exercise Price, Spread, and Taxes: The reality of the exercise price, spread, and taxes makes it impossible for those who aren't wealthy to buy the options upon exit (within the typical time period). They are unaware that they will have to shell out a significant amount of money and feel the pressure of the clock ticking. 4) Stock Purchase Reality: When junior employees do make the purchase, they may not realize they are simply buying stock at a discounted price to what they believe the long-term value will be. This is equivalent to buying Meta stock at its IPO because you believe there is tremendous upside that isn't currently reflected in the price. This isn't to say that equity grants aren't great for senior executives and very early employees. It's to say that most people given equity simply don't have all the facts, and using it as a replacement for equivalent compensation no longer makes sense, especially in a cooler market where multiples are no longer 40x revenue. Happy to be challenged :)

Last week, I heard from a super impressive customer who has cracked the code on how to give salespeople something they’ve always wanted: more selling time. Here’s how he transformed their process. This customer runs the full B2B sales motion at an awesome printing business based in the U.S. For years, his team divided their time across six key areas: 1. Task prioritization 2. Meeting prep 3. Customer responses 4. Prospecting 5. Closing deals 6. Sales strategy Like every sales leader I know, he wants his team to spend most of their time on #5 and #6 — closing deals and sales strategy. But together, those only made up about 30% of their week. (Hearing this gave me flashbacks to my time in sales…and all that admin tasks 😱) Now, his team uses AI across the sales process to compress the amount of time spent on #1-4: 1. Task prioritization → AI scores leads and organizes daily tasks 2. Meeting prep → AI surfaces insights from calls and contact records before meetings 3. Customer responses → Breeze Customer Agent instantly answers customer questions 4. Prospecting → Breeze Prospecting Agent automatically researches accounts and books meetings The result? Higher quantity of AI-powered work: More prospecting. More pipeline.  Higher quality of human-led work: More thoughtful conversations. Sharper strategy. This COO's story made my week. It's a reminder of just how big a shift we're going through – and why it’s such an exciting time to be in go-to-market right now.

If you're wondering, "Is a Machine Learning Certification worth it in 2025?" here are some honest thoughts 👇 Short answer - Yes, ML certifications are valuable. They can lead to real career growth, better salaries, and help you stand out in an increasingly crowded talent pool. 𝗪𝗵𝘆 𝗜𝘁’𝘀 𝗪𝗼𝗿𝘁𝗵 𝗜𝘁 1. Career Growth: Over 60% of certified professionals report getting promoted, and around 1 in 3 see salary increases, often above 20%. Certifications help you pivot into ML roles faster and take on more technical responsibilities. 2. Stand Out in a Crowded Field: Hiring managers are flooded with resumes, and if you have a certification from Google Cloud, AWS, or Microsoft they assume that you’re applying it in cloud-native, production-ready ways. 3. Industry Recognition: Top-tier certs like: ✅ Google Cloud Professional ML Engineer ✅ Amazon Web Services (AWS) Certified ML- Specialty ✅ Microsoft Azure AI Engineer Associate ✅ Databricks Certified ML Professional …are recognized by employers and often show up as "preferred qualifications" in job listings. 4. Employer Value: Typically, certified employees are seen as more productive, innovative, and independent. Companies say they trust certified hires to build models that actually work in production, I have always seen it as a requirement in big techs atleast. 5. Rising Demand: AI/ML jobs are expected to grow 40% between 2023-2027, and the fastest-growing demand is for engineers who understand ML and how to ship it, exactly what most cloud certs focus on. 𝗪𝗵𝗼 𝗦𝗵𝗼𝘂𝗹𝗱 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗜𝘁 → Career Switchers: If you'r trying to move from product, business, or academic backgrounds into AI? A cert gives you structure and credibility to break into the field. → Tech Pros (Early to Mid Career) If you're already a SWE or data engineer? A cloud ML cert can help you transition into ML roles or MLOps roles and get noticed for internal promotions. → Hands-On Learners: Certs with project-based components, like deploying models on GCP’s Vertex AI or AWS SageMaker are especially valuable. Employers love to see that! 𝗪𝗵𝗮𝘁 𝘁𝗼 𝗞𝗲𝗲𝗽 𝗶𝗻 𝗠𝗶𝗻𝗱 → Cert != Experience: A cert alone won’t get you the job. Pair it with real projects: open source work, GitHub repos, Kaggle comps, or cloud ML demos. → Certification vs. Certificate: A certification involves a proctored exam and industry recognition (like AWS, GCP). A certificate might just mean you completed a few videos. So, it's not the same weight. So, Be Selective! Skip generic "ML Bootcamp" or $10 Udemy-style courses unless they include real-world, resume-worthy projects. Rather, focus on programs that teach tools actually used in production. My 2 cents 🫰 An ML certification in 2025 is absolutely worth it, IF you choose the right one and back it up with hands-on experience. It's a good asset that signals your skill, curiosity, and job-readiness :)

👷♂️ Picture this: a robot tiling the floor space of four tennis courts — 1,000 m² — in just one day. These robots are not prototypes. They already exist. And they’re a perfect example of where AI-driven automation shines: Repetitive. Physically punishing. Unforgiving of error. But here’s what really matters: Every square meter a robot lays is also reshaping the skills mix on site. We gain speed, precision, and fewer injuries… but we risk sidelining the tradespeople whose craft built our cities. The untold story? The winners won’t be the robots alone — but the humans who move up the value chain: > Robot maintenance & calibration > Quality assurance & oversight > AI-driven project management If we don’t invest in these upskilling pathways now, automation becomes a zero-sum game. If we do, it becomes a win-win — safer sites, faster delivery, and jobs that value human creativity and judgment over back-breaking repetition. 💡 My take: Automation shouldn’t erase workers. It should elevate them. The question is whether governments, companies, and unions act fast enough to make that real. 👉 Have you seen an effective reskilling program in construction (or any hands-on industry) that could be a model here? #AI #Automation #FutureOfWork #Construction #Upskilling

This is nothing new if you've been paying attention, but LinkedIn data now confirms that hiring across tech-related fields is down about 20% from August 2018 in what they're calling a "white collar recession". But during that time, we've also had the ups and downs of "The Great Resignation" where people left their jobs in droves to jump into tech fields. We've had the "break into tech" folks (many of whom have not worked in tech beyond a 6-month contract themselves) selling dreams of easy pathways into high-paying remote jobs in tech if you just sign up for this course or that bootcamp. So the field has been flooded with more applicants competing for an ever-decreasing number of jobs. Rejection after rejection (if you're lucky enough to even hear back) is leaving folks questioning their skills and their value when it's really not about them, it's about the market. And there's also an interesting data point (that I think many recruiters can validate) that even with huge numbers of applicants, hiring processes are taking longer - often due to poor systems and hiring teams that may be more selective than ever in an environment where they have to fight to get every role approved. So what can you do if you're a jobseeker? 1. Pay attention to who is hiring. LinkedIn notes that healthcare has defied this trend as one of the few industries that has increased. Dig into jobs reports and identify the fields that are seeing increases and focus your energy there. You have a better chance with healthcare, education, and social services, or with the business/tech side of "blue collar" industries like construction, manufacturing, and transportation than you do for tech/SaaS companies. You have a better chance with on site/hybrid over remote. 2. Be realistic about your skills and qualification. When the field is competitive, companies are not hiring someone with 70% of the skills when they have a large pool of folks who has 100% of the skills. There's a decent chance a lot of folks are spinning their wheels and being hit with a lot of rejection unnecessarily. Assess your resume and application through the eyes of a recruiter or hiring manager who doesn't know your aptitude for learning, your interest, your passions...they only know what they see on the resume - employers, industries, job titles, results. And if you're on the hiring side: 1. High applicant volume should not be a surprise at this point. You can and should have systems and people in place to handle it. If you don't invest in it now. That poor candidate experience will hurt you at some point, and cost you the best talent. 2. Bring empathy with you at every step. You're talking to people who have faced rejection after rejection, who have negative bank accounts, who are worried about survival. Be thoughtful about your communication. Get back to people. Look past some nerves or a few extra follow up emails. Realize that this is all coming from anxiety and desperation.