User Experience and Data Privacy

𝟔𝟔% 𝐨𝐟 𝐀𝐈 𝐮𝐬𝐞𝐫𝐬 𝐬𝐚𝐲 𝐝𝐚𝐭𝐚 𝐩𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐬 𝐭𝐡𝐞𝐢𝐫 𝐭𝐨𝐩 𝐜𝐨𝐧𝐜𝐞𝐫𝐧. What does that tell us? Trust isn’t just a feature - it’s the foundation of AI’s future. When breaches happen, the cost isn’t measured in fines or headlines alone - it’s measured in lost trust. I recently spoke with a healthcare executive who shared a haunting story: after a data breach, patients stopped using their app - not because they didn’t need the service, but because they no longer felt safe. 𝐓𝐡𝐢𝐬 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐩𝐞𝐨𝐩𝐥𝐞’𝐬 𝐥𝐢𝐯𝐞𝐬 - 𝐭𝐫𝐮𝐬𝐭 𝐛𝐫𝐨𝐤𝐞𝐧, 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐬𝐡𝐚𝐭𝐭𝐞𝐫𝐞𝐝. Consider the October 2023 incident at 23andMe: unauthorized access exposed the genetic and personal information of 6.9 million users. Imagine seeing your most private data compromised. At Deloitte, we’ve helped organizations turn privacy challenges into opportunities by embedding trust into their AI strategies. For example, we recently partnered with a global financial institution to design a privacy-by-design framework that not only met regulatory requirements but also restored customer confidence. The result? A 15% increase in customer engagement within six months. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐫𝐞𝐛𝐮𝐢𝐥𝐝 𝐭𝐫𝐮𝐬𝐭 𝐰𝐡𝐞𝐧 𝐢𝐭’𝐬 𝐥𝐨𝐬𝐭? ✔️ 𝐓𝐮𝐫𝐧 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐧𝐭𝐨 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐦𝐞𝐧𝐭: Privacy isn’t just about compliance. It’s about empowering customers to own their data. When people feel in control, they trust more. ✔️ 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐏𝐫𝐨𝐭𝐞𝐜𝐭 𝐏𝐫𝐢𝐯𝐚𝐜𝐲: AI can do more than process data, it can safeguard it. Predictive privacy models can spot risks before they become problems, demonstrating your commitment to trust and innovation. ✔️ 𝐋𝐞𝐚𝐝 𝐰𝐢𝐭𝐡 𝐄𝐭𝐡𝐢𝐜𝐬, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Collaborate with peers, regulators, and even competitors to set new privacy standards. Customers notice when you lead the charge for their protection. ✔️ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐀𝐧𝐨𝐧𝐲𝐦𝐢𝐭𝐲: Techniques like differential privacy ensure sensitive data remains safe while enabling innovation. Your customers shouldn’t have to trade their privacy for progress. Trust is fragile, but it’s also resilient when leaders take responsibility. AI without trust isn’t just limited - it’s destined to fail. 𝐇𝐨𝐰 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐫𝐞𝐠𝐚𝐢𝐧 𝐭𝐫𝐮𝐬𝐭 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐬𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧? 𝐋𝐞𝐭’𝐬 𝐬𝐡𝐚𝐫𝐞 𝐚𝐧𝐝 𝐢𝐧𝐬𝐩𝐢𝐫𝐞 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫 👇 #AI #DataPrivacy #Leadership #CustomerTrust #Ethics

The most misused discovery question on the planet: "How does that impact you personally?" A best? Buyers roll their eyes. At worst? They lose trust and never call you back. 4 questions to use instead: 1. "How is that showing up in the business?" After you explore a challenge with a buyer, try asking this. Usually, they answer with metrics and other issues that have financial impact. A good thing. 2. "Who else is impacted by that, and how?" Asking how YOU are impacted (personally) feels like "too much." But when you ask how others are impacted, it's less intrusive. You're simply spot-checking the consequences across the business. Plus, this sets you up for multi-threading. 3. "What are some of the ripple effects this challenge is having on the business?" This is similar to "what's the impact?" But the varied wording is different. It's not what buyers expect. It feels more sophisticated. The intent comes across as analyzing the health of the business, rather than manipulating the individual. 4. "I've found that most challenges like the one you're sharing with me create OTHER challenges somewhere else in the business. Do you see that happening here?" Again. The intent comes across better. A "trusted advisor" asks questions like this. Because it feels like you're helping them analyze their business. Not extract personal pain you can lord over them later. Takeaway: Asking "How does this impact you personally?" can have its place. But it's not for a first call. You can only get away with asking that after you've built trust. If you've done that, then fire away. Until then, try these four alternative questions first.

My nonprofit leaders, here is a reminder of how data can impact the hard-built trust with the community: ● You collect data and never share back what you learned. → people gave their time, insight, and stories — and you disappeared. ● You ask for feedback, but nothing visibly changes. → silence after a survey signals: “We heard you, temporarily.” ● You only report the “positive” data. → editing out discomfort makes people feel their real concerns don’t matter. ● You don’t explain why you are collecting certain data. → people feel they are being extracted, not invited into a process. ● You ask the same questions in 3 different data collection tools in the same year — and do nothing new. → it reads not purposeful. ● You frame questions in a way that limits real honesty. → biased language, narrow choices, and lack of nuance tell people what you want to hear — not what they need to say. ● You over-collect but under-analyze. → too much data without insight leads to survey fatigue and disengagement. ● You hoard the data instead of democratizing it. → when leadership controls the narrative, your community loses faith in transparency. ● You don’t acknowledge who is missing from your data. → if marginalized groups are underrepresented and unacknowledged, you reinforce exclusion. ● You use data to justify decisions already made. → trust me, people know when you’re just cherry-picking numbers. #nonprofit #nonprofitleadership #community

Imagine a recruiter cold-calling you: “Hi, my name is Sarah Johnson with Career Growth Partners. Are you happy with your current job?” You’d probably think, “Whoa, Sarah, we just met! Buy me dinner first!” and hang up. Why? The question feels abrupt and self-serving. It’s like someone skipping the small talk and jumping straight to, “So, what’s your deepest insecurity?” People are wired to protect their personal information, especially when there’s no trust or context—and when the question feels like it’s all about you instead of them. The way out? Start with a neutral, non-threatening invitation. Like this: “This is Sarah with Career Growth. We’ve never spoken, but I was on your LinkedIn and was wondering if I could ask you a couple of quick questions.” See the difference? Now Sarah’s not barging in with, “Tell me your hopes and dreams!” She’s casually inviting you to chat—like a human. This simple shift respects the person’s autonomy, lowers their defenses, and opens the door for a real conversation. It’s a small change, but it’s the difference between being perceived as pushy versus being pleasant.

A hairdresser and a marketer came into the bar. Hold on… Haircuts and marketing? 🤔 Here's the reality: Consumers are more aware than ever of how their data is used. User privacy is no longer a checkbox – It is a trust-building cornerstone for any online business. 88% of consumers say they won’t share personal information unless they trust a brand. Think about it: Every time a user visits your website, they’re making an active choice to trust you or not. They want to feel heard and respected. If you're not prioritizing their privacy preferences, you're risking their data AND loyalty. We’ve all been there – Asked for a quick trim and got VERY short hair instead. Using consumers’ data without consent is just like cutting the hair you shouldn’t cut. That horrible bad haircut ruined our mood for weeks. And a poor data privacy experience can drive customers straight to your competitors, leaving your shopping carts empty. How do you avoid this pitfall? - Listen to your users. Use consent and preference management tools such as Usercentrics to allow customers full control of their data. - Be transparent. Clearly communicate how you use their information and respect their choices. - Build trust: When users feel secure about their data, they’re more likely to engage with your brand. Make sure your website isn’t alienating users with poor data practices. Start by evaluating your current approach to data privacy by scanning your website for trackers. Remember, respecting consumer choices isn’t just an ethical practice. It’s essential for long-term success in e-commerce. Focus on creating a digital environment where consumers feel valued and secure. Trust me, it will pay off! 💰

🔥 Stop asking these 5 questions. They’re silently killing your team’s trust. I’ve seen it happen more times than I can count. One leader walks into a meeting. Asks one question. And the whole room shuts down. People stop breathing. They look down. They give the “safe” answer. And just like that: → Trust? Gone. → Momentum? Dead. → Innovation? Don’t even bother. It wasn’t the tone. It wasn’t the setting. It was the 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯. Some questions don’t build insight. They build fear. And fear kills progress. Here are 5 questions smart leaders stop asking (and what they ask instead): 𝟭/ “𝗪𝗵𝘆 𝗱𝗶𝗱𝗻’𝘁 𝘆𝗼𝘂…?” 👎 Signals blame   ↳ People shut down to protect themselves ✅ Ask instead: “What got in our way here?” 💡 Shifts focus from fault to feedback 𝟮/ “𝗪𝗵𝗼’𝘀 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗳𝗼𝗿 𝘁𝗵𝗶𝘀 𝗺𝗶𝘀𝘁𝗮𝗸𝗲?” 👎 Sounds like: “Who’s getting fired?”   ↳ No one learns, everyone hides ✅ Ask instead: “What can we learn from this?” 💡 Creates safety and makes mistakes useful 𝟯/ “𝗗𝗼𝗻’𝘁 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝘄𝗲 𝘀𝗵𝗼𝘂𝗹𝗱…?” 👎 This is just your opinion in disguise ↳ Blocks real dialogue from the start ✅ Ask instead: “What’s your take on this?” 💡 Invites ownership and builds trust 𝟰/ “𝗪𝗵𝘆 𝗰𝗮𝗻’𝘁 𝘆𝗼𝘂 𝗯𝗲 𝗺𝗼𝗿𝗲 𝗹𝗶𝗸𝗲 [𝗽𝗲𝗿𝘀𝗼𝗻]?” 👎 Comparison kills motivation   ↳ It never inspires, it only isolates ✅ Ask instead: “What support do you need?” 💡 Shifts from judgment to growth 𝟱/ “𝗛𝗮𝘃𝗲𝗻’𝘁 𝘆𝗼𝘂 𝗳𝗶𝗻𝗶𝘀𝗵𝗲𝗱 𝘁𝗵𝗮𝘁 𝘆𝗲𝘁?” 👎 Signals impatience and disconnect   ↳ Implies laziness before understanding reality ✅ Ask instead: “What obstacles are you facing?” 💡 Be a partner, not a critic 🧨 The Hard Truth: You don’t lose trust with big betrayals. You lose it with small questions that feel like traps. If your people aren’t speaking up… It’s not because they don’t have ideas. It’s because they don’t feel safe. Great leaders ask questions that pull people in. Not push them away. So check your questions. Because your words aren’t just words. They shape the culture your team lives in. ❓Which of these questions do you catch yourself using? — ♻️ Repost to help others ask better questions. ➕ Follow Nadeem for more leadership truth.

AI: Game-Changer or Privacy Intruder? When we think about AI, most people envision cutting-edge tools driving business profits or predicting global economic trends. But here’s the twist: AI isn’t just about macro-level insights—it’s deeply personal. Consider this: every time you post on social media, use an app, or carry your smartphone, you’re generating data. This can lead to some incredible benefits: ✅ Hyper-personalized recommendations that feel like magic. ✅ Efficient solutions tailored to your daily needs. ✅ Insights into behavior patterns that improve decision-making. But there’s a flip side we can’t ignore. Sandra Matz, author of Mindmasters and a Columbia professor, reminds us that this constant data collection can feel like someone looking over your shoulder 24/7. Amazing? Sometimes. But it’s also a profound intrusion into privacy. Here’s how we can reframe our approach to AI and data sharing: 🧠Be selective. Pause before granting permissions on apps—are they really necessary? 🧠Stay informed. Know how your data is being collected and used. 🧠Prioritize boundaries. Treat your data like the valuable resource it is. By being more mindful about the data we share, we can harness the incredible benefits of AI without sacrificing our privacy.

The numbers are staggering: 78% of companies track user data across platforms. But here’s the real issue: Most users don’t know how much of their behavior is being monitored. Most companies treat “consent” as a checkbox, not a commitment. And in a digital-first economy, trust is the most valuable currency. Case in point: A recent global study revealed that while data collection has surged, consumer trust in corporations has declined sharply. The tension is clear:  → Businesses need data to personalize experiences.  → Users want control, transparency, and ethical handling. The leaders who will win in this new era are those who move from:  “How much data can we get?” to “How can we earn lasting trust?” Privacy-first frameworks are emerging: Transparent opt-ins, not hidden clauses. User data vaults controlled by the individual. AI systems that process data without storing sensitive identifiers. The lesson is simple: Companies that build trust-first, track-second will outlast those who treat data like a commodity. So here’s my question for you: Would you rather buy from a company that personalizes aggressively, or one that promises minimal data tracking with full transparency? P.S. Dropping impactful insights that matter in my weekly newsletter every Saturday, 10 AM EST. Don't miss it. Subscribe right here! https://lnkd.in/gcqfGeK4

Why Can't EHRs Be More Like HubSpot? The Healthcare Data Portability Problem I've been exploring HubSpot CRM recently to integrate some of our falcon health data, and it's been a revelation. Coming into this, I had limited direct experience with CRM platforms - I briefly used Salesforce at my first startup but hadn't touched similar systems since. Naturally, I approached HubSpot with some worries. My worries proved completely unnecessary. What impressed me most wasn't the sales functionality - yeah, I am not good at sales, but the remarkable data portability. The data import process was intuitive with an excellent validation interface. But the real game-changer? One-click API key generation. With that key, I could navigate and manipulate complex data relationships with surprising ease. For those outside healthcare, this might seem unremarkable. But if you've worked in healthcare IT, you understand why I'm amazed. A few weeks ago, a respected healthcare executive asked for my thoughts on selecting EHR vendors. I emphasized just one crucial factor: choose the EHR with the least lock-in effect. No lock-in effect - is it too much to ask? When will we reach a point where changing EHR systems becomes as straightforward as switching workflow tools? Many companies I've worked for regularly change their productivity platforms. Yes, these transitions have challenges, but it wasn't a million-dollar project. What's telling is that EHR-to-EHR transitions are so extraordinarily difficult that they've become the subject of academic research. A recent systemic review paper [2] outlines the "remarkably expensive, laborious, personnel devouring, and time consuming" nature of these transitions, requiring meticulous planning across ten critical domains from financial considerations to data migration and patient safety. Recent regulatory developments like the 21st Century Cures Act and information blocking rules aim to improve this situation, but progress remains slow. As healthcare organizations rush to embrace AI and other emerging technologies, I worry we're not paying enough attention to the looming lock-in problems these integrations might create. Will today's AI investments make future system transitions even more difficult? Without prioritizing data portability from the start, we risk building even higher walls around our healthcare data silos, precisely when we need more fluidity, not less. [1] https://lnkd.in/e_7XMQjg [2] https://lnkd.in/e9kD_hDK

𝐁𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐌𝐨𝐧𝐞𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐧 𝐅𝐢𝐧𝐭𝐞𝐜𝐡 In the fast-evolving fintech landscape, data monetization has become a crucial engine for growth. Harnessing data insights allows fintech companies to create personalized experiences, optimize financial products, and drive profitability. But with great power comes great responsibility - specifically, the responsibility to protect consumer privacy. Globally, privacy laws like GDPR, CCPA, DPDPA and others are setting new standards for data handling. Fintech companies must navigate this complex regulatory environment while exploring data monetization opportunities. As we stand at the cusp of 2025, the conversation around how we manage, monetize, and protect data in fintech is not just about compliance or innovation; it's about redefining trust in the digital age. In an era where data breaches are headline news, consumer trust is fragile. Balancing data use with robust privacy measures isn't just good practice; it's essential for maintaining customer loyalty and brand reputation. 𝐻𝑜𝑤 𝑐𝑎𝑛 𝑓𝑖𝑛𝑡𝑒𝑐ℎ 𝑛𝑎𝑣𝑖𝑔𝑎𝑡𝑒 𝑡ℎ𝑖𝑠 𝑑𝑒𝑙𝑖𝑐𝑎𝑡𝑒 𝑏𝑎𝑙𝑎𝑛𝑐𝑒? 𝟭. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗶𝘀 𝗞𝗲𝘆: Clearly communicate how data is collected, used, and protected. When users understand how their data benefits them, they are more likely to engage. 𝟮. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮-𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: Monetize insights, not individual identities. Aggregating and anonymizing data can provide value while protecting privacy. 𝟯. 𝗨𝘀𝗲𝗿 𝗘𝗺𝗽𝗼𝘄𝗲𝗿𝗺𝗲𝗻𝘁: Give users control over their data. Options to manage consent and access their data foster trust and demonstrate respect for their privacy. 𝟰. 𝗣𝗿𝗶𝘃𝗮𝗰𝘆-𝗙𝗶𝗿𝘀𝘁 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀: Leverage advanced encryption, secure data-sharing methods, and privacy-enhancing technologies to build a robust data protection framework. 𝟱. 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Beyond compliance, investing in cybersecurity infrastructure is crucial. This includes not just technology but also training for employees and establishing a culture of security awareness. The future of fintech will be defined by those who can master this balance. It's about creating value from data while ensuring that privacy isn't just an afterthought but a core value proposition. As we move forward, the integration of advanced privacy technologies, ethical frameworks, and a commitment to transparency will not only protect but also empower users, setting new benchmarks for what it means to be a leader in fintech.   How do you see the future of data privacy shaping the fintech landscape? 𝘐𝘮𝘢𝘨𝘦 𝘚𝘰𝘶𝘳𝘤𝘦 : 𝘋𝘈𝘓𝘓-𝘌 #Fintech #DataPrivacy #DataMonetization #Trust #Innovation #Privacy #Leader #ConsumerCentricity #Innovation #Ethical