Data-Driven Leadership

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

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

Data or Gut Feelings. Whenever I’ve made strategic decisions while neglecting my gut feelings, I have felt a tinge of regret. Leaders are often urged to make data-driven decisions in this age of abundant data. Data is significant; it offers valuable insights by revealing past trends and providing predictive analytics, yet I believe it has limitations. Data alone will not always account for individual circumstances, unexpected challenges, or the essential human elements crucial to effective leadership. On the other hand, intuition - rooted in experience, judgment, and the ability to recognise patterns - can be incredibly powerful, especially in uncertain or quickly changing environments. Still, we must acknowledge that biases and narrow perspectives can sway intuition. Today’s leaders face the interesting challenge of blending analytical skills with intuitive wisdom rather than choosing one over the other. For example, while data may highlight an emerging market trend, intuition empowers leaders to assess whether the timing, cultural relevance, or team readiness aligns with taking action. A potent way to bridge this gap is by asking lots of critical questions during decision-making: Cultivating a habit of evaluating choices from numerical and descriptive angles ensures a more robust approach. The essence of future leadership lies in mastering the art of merging analytics with intuition. We can achieve this by fostering critical thinking to evaluate data accuracy, employing scenario planning, evaluating multiple alternatives to juxtapose gut feelings with measurable insights, and building diverse team thinking to challenge assumptions. Practical steps, such as conducting post-mortems to reflect on decision-making processes, help bring this balance to life. When data and intuition unite, leaders can make much more impactful decisions. So, I vote for a harmonious combination.

The legal operations function is a key part of driving transformation in an organization. The key to proving impact and identifying opportunities for improvement lies in mastering Key Performance Indicators (KPIs). These metrics go beyond mere tracking—they tell the story of how legal teams add value and innovate within their organizations. Some Key Areas to Focus Your Legal Ops KPIs:     •    Financial Management: Monitor spend under management, budget variance, and cost savings to demonstrate responsible fiscal stewardship and strategic impact.     •    Efficiency and Productivity: Track cycle times for contracts, task completion rates, and matter turnaround times to uncover process bottlenecks and improve output.     •    Technology Utilization: Assess adoption rates, system ROI, and user satisfaction to ensure investments in legal tech deliver measurable results.     •    Risk Management and Compliance: Measure compliance incident frequency, litigation exposure, and policy adherence rates to bolster organizational resilience. Legal operations teams that embrace these metrics position themselves as catalysts for smarter decision-making and tangible business outcomes. #legaltech #innovation #law #business #learning

As businesses integrate AI into their operations, the landscape of data governance and privacy laws is evolving rapidly. Governments worldwide are strengthening regulations, with frameworks like GDPR, CCPA, and India’s DPDP Act setting higher compliance standards. But as AI becomes more embedded in decision-making, new challenges arise: 🔍 Key Trends in Data Governance & Privacy Compliance ✔ Stricter AI Regulations: The EU AI Act mandates greater transparency, accountability, and ethical AI deployment. Businesses must document AI decision-making processes to ensure fairness. ✔ Beyond GDPR: Laws like China’s PIPL and Brazil’s LGPD signal a global shift toward tougher data protection measures. ✔ AI and Automated Decisions Scrutiny: Regulations are focusing on AI-driven decisions in areas like hiring, finance, and healthcare, demanding explainability and fairness. ✔ Consumer Control Over Data: The push for data sovereignty and stricter consent mechanisms means businesses must rethink their data collection strategies. 💡 How Businesses Must Adapt To remain compliant and build trust, companies must: 🔹 Implement Ethical AI Practices: Use privacy-enhancing techniques like differential privacy and federated learning to minimize risks. 🔹 Strengthen Data Governance: Establish clear data access controls, retention policies, and audit mechanisms to meet compliance standards. 🔹 Adopt Proactive Compliance Measures: Rather than reacting to regulations, businesses should embed privacy-by-design principles into their AI and data strategies. In this new era of ethical AI and data accountability, businesses that prioritize compliance, transparency, and responsible AI deployment will gain a competitive advantage. 𝑰𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒓𝒆𝒂𝒅𝒚 𝒇𝒐𝒓 𝒕𝒉𝒆 𝒏𝒆𝒙𝒕 𝒘𝒂𝒗𝒆 𝒐𝒇 𝑨𝑰 𝒂𝒏𝒅 𝒑𝒓𝒊𝒗𝒂𝒄𝒚 𝒓𝒆𝒈𝒖𝒍𝒂𝒕𝒊𝒐𝒏𝒔? 𝑾𝒉𝒂𝒕 𝒔𝒕𝒆𝒑𝒔 𝒂𝒓𝒆 𝒚𝒐𝒖 𝒕𝒂𝒌𝒊𝒏𝒈 𝒕𝒐 𝒔𝒕𝒂𝒚 𝒂𝒉𝒆𝒂𝒅? #DataPrivacy #EthicalAI #datadrivendecisionmaking #dataanalytics

Many companies get SELF-SERVICE ANALYTICS WRONG. I see most data teams framing the goal of self-service analytics as convenient, individual access to reports and "insights". In practice, they mean access to dashboards. I believe many data teams are missing the big picture. The most common scenario I see --- The data team partners with a business team to develop a set of dashboards to support decision-making. The dashboard is "self-service" and answers the initial questions. Until it isn't. What about new/ follow-up questions? It's time to REDEFINE self-service analytics. Here's how to get SELF-SERVICE ANALYTICS right: 1️⃣ Self-service is not the goal. IMPACT is the end GOAL. Define clear targets and end goals beyond just enabling self-service. For instance, do you want to enable your business users to understand WHAT's happening by looking at a few dashboards or do you aim to enable them to understand WHY things are changing? 2️⃣ Ensure DATA QUALITY and CONSISTENCY If everyone has access to the wrong data, this will have a huge cost on decision quality. Define and maintain a single source of truth. Get the fundamentals right first. 3️⃣ Ensure a minimum level of DATA LITERACY. You should judge properly who has access and train them accordingly. Otherwise, your self-service initiative won't lead to a greater impact. 4️⃣ Implement a clear GOVERNANCE MODEL There is a fine line between data democratization and data anarchy. You need clear data owners, who are responsible for avoiding the latter. When your business colleague finds a discrepancy in the data - there should be one clear owner for this matter. 5️⃣ Develop an ADAPTABLE solution, not too rigid, that enables users to answer follow-up questions The requirements and needs of business users will evolve over time. Your self-service solutions have to keep up. Dashboards are not enough, especially for more operational analytics. Complement your dashboards with a more flexible solution. Self-service analytics is already widespread and is here to stay. To what extent it provides greater value and impact depends on the factors mentioned. ---- DATA AND BUSINESS FOLKS: What has been your experience with self-service analytics? 👇Let me know in the comments 👇 #data #analytics #selfservice #AI #businessanalytics

Too many teams accept data chaos as normal. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North take a different path - eliminating silos, reducing wasted effort, and unlocking real business value. Here’s the playbook they’ve used to break down silos and build a scalable data strategy: 1️⃣ Empower domain teams - but with a strong foundation. A central data group ensures governance while teams take ownership of their data. 2️⃣ Create a clear governance structure. When ownership, documentation, and accountability are defined, teams stop duplicating work. 3️⃣ Standardize data practices. Naming conventions, documentation, and validation eliminate confusion and prevent teams from second-guessing reports. 4️⃣ Build a unified discovery layer. A single “Google for your data” ensures teams can find, understand, and use the right datasets instantly. 5️⃣ Automate governance. Policies aren’t just guidelines - they’re enforced in real-time, reducing manual effort and ensuring compliance at scale. 6️⃣ Integrate tools and workflows. When governance, discovery, and collaboration work together, data flows instead of getting stuck in silos. We’ve seen this shift transform how teams work with data - eliminating friction, increasing trust, and making data truly operational. So if your team still spends more time searching for data than analyzing it, what’s stopping you from changing that?

CDOs are experts in removing data silos. In view of data strategy, they are building a silo? Some days ago I asked you about the ownership of the data strategy. 55% of people voted for the CDO. I propose, it is time to rethink the traditional approach. A single CDO, no matter how talented, can't fully grasp the complexities of an entire organization's data needs. A Team-Based Approach for owning the Data Strategy empowers a diverse group of data leaders (which might be product leaders as well) to collaborate, innovate, and drive real data business impact. Benefits of a Team-Based Approach: * A collective responsibility ensures a holistic view of data challenges and opportunities. * A team brings a wide range of expertise, leading to more creative and effective solutions. * Agile Decision-Making: Faster response times to evolving data needs. * A team can adapt to growing data demands and organizational changes. (the team-based approach requires a CDO with strong communication and moderation skills!) Let's break down ALL data silos and build a future-proof data strategy together! What are your thoughts on this? #datastrategy #dataleadership #datateams #dataculture #digitaltransformation 🫶🏼 Share this one with your colleagues and peers who are aiming for authenticity as well. ✨ Follow Dr. Markus Schmidberger and Turtle Transformation Limited for more authentic and data leadership content. #authenticleadership #growthmindset #leadership #data

The CEO You Need in 2025 Won't Be on Traditional Hiring Playbooks. Leadership has evolved yet companies still rely on outdated executive search methods. 🔹 Static resumes 🔹 Gut-feel hiring 🔹 Narrow industry filters And that’s why they miss out on transformative leaders. I've spent 12+ years connecting FMCG giants with transformative leaders, and one truth stands out: traditional executive search is broken. Let me share what actually works in 2025. The Modern Executive Search Playbook (backed by our 94% retention rate): 1️⃣ Data-Driven DNA Gone are the gut-feel hires. Our AI-powered analytics have revealed fascinating patterns: 73% of successful leaders showed early innovation signals in previous roles Cultural fit predictions are now 89% accurate using our behavioral mapping Leadership trajectory modeling spots high-potential candidates 3 years before competitors 2️⃣ Diversity as a Performance Multiplier Recent McKinsey data shows diverse leadership teams outperform by 36%. Our approach? Assessment protocols that raised diverse placements by 47% YoY AI-driven bias elimination that expanded our qualified candidate pool immensly 3️⃣ Global Talent Arbitrage Reality check: Your next game-changing leader might be in Singapore while you're searching in London. Our cross-border placements increased in 2024 Cultural intelligence mapping shows 92% success rate in international transitions 4️⃣ Future-Proof Leadership Assessment Traditional metrics miss tomorrow's stars. Our predictive models track: Adaptability Quotient (AQ) - now more crucial than IQ Innovation Capacity Score™ - predicting market disruption potential Strategic Agility Index - measuring pivot capabilities in crisis 5️⃣ Continuous Evolution Last year taught us: Remote leadership capabilities are 3x more important than pre-2023 Sustainability expertise has become a top 5 requirement AI literacy is non-negotiable for 89% of C-suite roles 🎯 The truth: The talent industry has shifted more in the last 18 months than in the previous decade. Companies still using 2020 playbooks are missing out on transformative leaders. #ExecutiveSearch #Leadership #Hiring #AI #Recruitment #TalentAcquisition #FMCG

Crafting a Data and Analytics Strategy That Really Resonates For many organizations, articulating the tangible value of a data strategy can be a significant challenge. It's common to default to a technology-centric approach, leading to skepticism about solving a "problem" with a "hammer". 🔵 Strategy First, Technology Second Gaining buy-in for your data and analytics vision before diving into the technical details of the operating model. This prevents stakeholders from questioning the need for proposed technology solutions. Communication is key, and it must be segmented based on your audience – whether you're educating or informing (sideways; business partners), persuading (upwards; sponsors), or instructing (downwards; D&A teams). Each approach demands different content, length, and emphasis in your presentations. 🔵 Concise, Outcome-Led Vision Your vision statement should be remarkably concise, ideally 20-40 words, deliverable as an "elevator pitch". It should clearly state how your data and analytics team contributes to the top three organizational goals, identifies the specific stakeholders you aim to help, and outlines three mechanisms for delivering value. This also includes explicitly stating what you won't focus on, ensuring clarity and preventing dilution of effort. 🔵 Align with Business Transformations and Culture To ensure relevance, your strategy must connect with ongoing major business transformations within the organization. Furthermore, addressing cultural barriers to data-driven decision-making is paramount. I suggest framing the culture as "outcome-led" / "value-driven" and "decision-centric" rather than merely "data-driven". 🔵 Broaden The Appeal and Resonate, Wider Incorporate contemporary drivers and trends (e.g. how DA& teams are responding to Generative and Agentic AI), categorizing them as technology, internal, or market/societal factors, to demonstrate your strategy's forward-looking nature. 🔵 Defining Value and Measurable Impact Prioritize your primary stakeholders (ideally three), and for each, define the top three goals your team will help them achieve. For each goal, identify three measurable metrics, creating a "metrics tree" that clearly tracks your contribution to their success. Gartner defines three core value propositions for data and analytics: 1️⃣ Utility: Providing enterprise reporting as a service for common questions. Central team, allocated budget, data warehouse, etc. 2️⃣ Enabler: Facilitating business outcomes through self-service analytics, coaching, and projects based on business cases. 3️⃣ Innovation: Driving new initiatives like AI for decision making and prescriptive analytics. Each value prop requires a different delivery model, from service desks for utility to portfolio management for innovation, and these should be aligned. Collaborating with leaders like CIO, CISO, CAIO is also crucial for innovation efforts. Develop a D&A strategy that demonstrates tangible business value.