Data-Driven Strategy Formulation

RIP Tableau Tableau is a business intelligence tool owned by Salesforce. For years it was part of how we worked at Voi. In the beginning it felt powerful, but over time it turned into what many legacy SaaS tools become: expensive, clunky and slow. Every ad hoc request ended up in an analyst backlog. Local teams across our 100 plus cities were left waiting for insights, costs kept going up and speed disappeared. So we ripped it out, saved at least 500k EUR, potentially millions (from speed). The direct savings are hundreds of thousands of euros in licenses. The indirect savings are even bigger since analysts can now focus on high impact work instead of repetitive reporting. The biggest shift is speed. What once took weeks now happens in seconds. Here is how we made it possible: 1. We fixed the foundations. Years of work on data governance. Every metric has an owner, quality checks, semantics and definitions. Everyone in the company knows what a number means. With that in place, self serve became possible, which is essential when local teams in 100 plus cities need the right data at the right time. 2. We defined what we need, not what we paid for. A single source of truth, real time data streaming and self serve for non technical users. Analysts no longer spend their days on small one off requests. 3. We used LLMs as the bridge. Together with a design partner we built a UI that supports continuous business intelligence, and we created an AI data analyst that lives inside Slack and Sheets. LLMs translate natural language into SQL, query the warehouse and return insights or visuals in natural language again. This step is what unlocked true self serve at scale. But LLMs alone are not enough. In an enterprise setting you need strict guidelines and guardrails. Without governance you risk inconsistent answers, wrong definitions or even compliance issues. The combination of solid data governance with the power of LLMs is what makes this work. The results are clear: 1. Millions saved on SaaS and labor 2. One source of truth for all key metrics 3. Self serve for everyone in the company within clear constraints 4. Up to 100x faster time to insight and decision making LLMs made this shift possible. Strong governance made it safe. RIP Tableau. And it will not be the last legacy SaaS tool we replace.

Your employee learning systems won’t just train people anymore — they’ll train your AI Agents. Your corporate university will become an LLM fine-tuning hub. The implications are big: • 𝗬𝗼𝘂𝗿 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗮 𝗱𝗮𝘁𝗮𝘀𝗲𝘁: onboarding docs, sales playbooks, support transcripts, and internal wikis will shape your AI’s behavior and outputs. • 𝗬𝗼𝘂𝗿 𝘀𝘂𝗯𝗷𝗲𝗰𝘁-𝗺𝗮𝘁𝘁𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝘁𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝗔𝗜 𝗺𝗲𝗻𝘁𝗼𝗿𝘀: their expertise won’t just teach people anymore; it will calibrate intelligent systems across your org. • 𝗬𝗼𝘂𝗿 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗲𝗱𝗴𝗲: companies that invest in tuning will have AIs that sound like them, think like them, and scale them. It’s a cultural transformation. Your AI will only be as smart as what (and who) it learns from. 👉 How are you thinking about training both people and AIs in your company?

Business leaders who lay off data engineers to ramp up for AI probably look up and down before crossing the street. GenAI doesn’t make data irrelevant. It makes first-party contextual data more critical than ever. I teach executives a simple framework called the #AI College Model. Think of LLMs like a student headed off to university. Pretraining is the prerequisites courses that provide LLMs with the horizontal breadth required to build deeper capabilities. Like college prerequisites, pretraining only gives LLMs a 6-inch depth in each subject. Post-training is courses that support a college major, specialization, or advanced degree. It uses contextual first-party #data and reinforcement learning to develop vertical depth in a domain. AI researchers focus on pretraining, and that’s where all the hype is centered, but businesses care more about post-training. Value and capabilities that can be monetized get baked into the LLM during post-training. Reinforcement learning is much more expensive (computationally and/or human-labor) than high-quality contextual datasets. The more information a business has, the lower post-training costs and higher AI product margins get. BI data is formatted for people and LLMs who have already graduated from college. #DataEngineering turns BI data into information and curates new information sets that can be used for post-training #GenAI. It’s critical for executive leaders to rapidly upskill their data and AI literacy. Business and operating models increasingly rely on data and AI, so it’s ridiculous to think that the C-suite can be effective without understanding them.

How the UAE Digitally Transformed Governance — at Unprecedented Scale In 2024, the UAE didn’t just adopt digital transformation it institutionalized it through a federated, data-driven model that redefined public service delivery across access, efficiency, trust, and integration. What Was Achieved – 2024 Key Results 1. 173.7 million government transactions completed digitally 2. 1,419 government services fully digitized across all sectors 3. 57 million+ unique users engaged through federal platforms 4. 91% user satisfaction rate with digital government services This means: For every citizen and resident, there were approximately 17 digital government transactions completed this year alone. What Enabled This Transformation? The UAE’s success was powered by deep-tech infrastructure, cloud platforms, and smart policy design delivering both scale and precision: • 2.6B digital transactions • 99.7% AI response accuracy • 20M digital documents issued • 12M verified exchanges • 1,500+ integrated government systems This was not mere digitization it was a full-scale re-architecture of government. Strategic Focus Areas 1. Unified Digital Government 1,500+ systems integrated across ministries for seamless, efficient service delivery. 2. AI-Powered Services 99.7% smart assistant accuracy reduced wait times and improved user experience. 3. Trusted Infrastructure 20M documents issued and 12M verified exchanges ensured secure, compliant digital interactions. 4. Widespread Accessibility 57M+ users citizens, residents, and investors accessed services anytime, anywhere. 5. Data-Driven Optimization Real-time analytics and feedback drove a 91% satisfaction rate, setting a global benchmark. Visionary Leadership Behind the Model The transformation is driven by the far-sighted leadership of: • HH Sheikh Mohamed bin Zayed Al Nahyan, President of the UAE • HH Sheikh Mohammed Bin Rashid Al Maktoum, Vice President, Prime Minister of the UAE & Ruler of Dubai They didn’t just ask “how can we digitize government?” They asked, “how can we lead the world in digital governance?” By fusing vision with execution, the UAE positioned itself not as a digital follower — but as a global model for AI-enabled, citizen-first, government innovation. Why This Matters Globally • 2.6B digital transactions put the UAE on par with top digital nations like Singapore and South Korea. • 99.7% AI accuracy positions it as a global leader in smart governance. • 1,500+ system integrations created one of the world’s most unified public service ecosystems. • 173.7M annual transactions = nearly 475,000 digital interactions every day. This is not enhancement. This is national digital infrastructure at work. The UAE is not preparing for the future. The UAE is building it system by system, document by document, decision by decision. In the UAE, governance isn’t evolving it’s accelerating toward the future at the speed of trust and technology.

🔍 I've been thinking deeply about what makes data-powered governance truly effective. After some observation and some experience, I've identified three critical ingredients – what I humbly call the "Three D's". 📊 Data Exchange Platforms: The foundation that enables innovation through open data sharing and collaborative models. Estonia's X-Road has revolutionized public services by creating a secure data exchange layer connecting government databases. Citizens can access nearly all government services online, with 99% of public services available digitally. Singapore's Smart Nation Sensor Platform integrates data from sensors and IoT devices across the city to optimize everything from traffic flow to energy consumption. 📜 Data Policies: The essential guardrails that establish trust. The European Union's GDPR has set a global standard for data protection, enhancing citizen trust while creating a framework for responsible innovation. Closer home, the DPDP will start to set benchmarks for data-centric guardrails for a massive, diverse, and data-rich country like India. 🧩 Decision-Support Systems: The mechanisms that transform data into action. South Korea's COVID-19 response leveraged their Epidemic Investigation Support System to enable rapid contact tracing while maintaining transparency with citizens. Also, New Zealand's Integrated Data Infrastructure connects data across government agencies to inform policy decisions with robust economic analysis, resulting in more targeted and effective social programs. 💡 When these 3D's are combined deftly by the public-sector, citizen-centric governance becomes the cornerstone for any government. For the scale India operates at, it's a very good opportunity to show the way for the Global South. 🤔 I think we're at that inflection point with the recent announcement of AI Kosha and the DPDP, and they can help safely incubate innovative solutions that will optimize the delivery of government schemes, thereby ensuring timely, targeted assistance for citizens. Thoughts? #DigitalTransformation #PublicSector #Innovation #DataStrategy

At the end of the day, it all comes back to DATA. If everyone is using the same foundation models, the only real competitive edge left is your own enterprise data. Your proprietary data is what turns a general-purpose model into YOUR model. A model that reflects your customers, your industry, your unique challenges and opportunities, your proprietary knowledge and experience. The organizations that win today won’t be those who plug into the latest LLM. They’ll be the ones who: (1) curate high-quality, domain-specific data, (2) invest in data infrastructure and governance and (3) continually fine-tune and adapt models with their own insights. The fundamentals haven’t changed: the value is in the data. Those who recognize this and treat data as their most strategic asset will be the ones who lead in the age of AI. #data #artificialintelligence #generativeAI #strategy #LLMs TL;DR Don't chase GenAI hype without addressing your core data reality first. Trusted data (aka cleaned, governed, AI-ready data) is your real differentiator.

ℹ Gartner research just published: How to Create a Business-Driven Data and Analytics Strategy. Data, analytics and AI initiatives must create concrete and measurable impact on business outcomes. This research helps data and analytics leaders to create a resilient, business-focused strategy to enable tangible business outcomes. 🔵 Position D&A as a business function focused on delivering value to shareholders and customers by creating a dynamic D&A strategy based on your organization’s business scorecard. 🔵 Evaluate the impact of internal, societal/market and technology drivers to ensure your D&A strategy is resilient. 🔵 Identify how strategic D&A actions will directly address prioritized, measurable business outcomes so that the purpose and impact of your strategy is understood across the organization. 🔵 Analyze your D&A capabilities and deficits using Gartner’s IT Score for Data and Analytics to ensure that your strategic roadmap and D&A operating model can realistically deliver on what you have promised. I'm pleased we have produced this research, led by the esteemed Saul Judah, coauthored with myself (David Pidsley), guidance of Alan D. Duncan and my mentor Andrew White. Gartner clients subscribing to our #Data and #Analytics & #AI practices can login now and read it: https://lnkd.in/e4zmvwbV

My nonprofits in the community - are you planning a donor survey in the next two months? Here are some examples of how you can ensure that the data does not sit silently in your work folders but actually lets it help you take meaningful actions. Example 1: Say your survey question is: "How likely are you to continue donating to our organization in the next year?" ● Data says: If 60% of donors say they are "very likely" to continue donating, but 30% are "somewhat likely" and 10% are "unlikely," this indicates a potential drop-off in donor retention. ● Turning that data into action: Focus retention efforts on the "somewhat likely" group. Create a targeted campaign that re-engages these donors by highlighting recent successes, impact stories, or new initiatives they might care about. Additionally, reach out to the "unlikely" group to understand their concerns and see if any issues can be addressed. Example 2: Say your survey question is: "Which of the following areas do you believe your donation has the most impact?" ● Data says: 50% of respondents say their donation has the most impact on "Education Programs," while only 10% say "Healthcare Initiatives." ● Turning that data into action: Understand the why and promote the success and need for your "Healthcare Initiatives" more prominently, aiming to increase donor awareness and support in this underfunded area. Example 3: Say your survey question is: "What is your primary reason for donating to our organization?" ● Data says: If the top reason to engage is "Alignment with my values" (40%) followed by "Transparency in how funds are used" (35%). ● Turning that data into action: Emphasize your organization's values and transparency in all communications. Regularly update donors on how their funds are being used with clear, detailed reports, and align your messaging with the core values that resonate with your donor base. Example 4: Say your survey question is: "How satisfied are you with the level of communication you receive from our organization?" ● Data says: If 70% of donors are "satisfied", 20% are "neutral," and 10% are "dissatisfied," there's room for improvement in communication. ● Turning that data into action: Understand the "neutral" and "dissatisfied" groups to pinpoint where communication may be lacking. This could involve increasing the frequency of updates, personalizing communications, or providing more opportunities for donor feedback and engagement. Sit with the data you collect. Read the numbers. Read the stories. Read the hopes, barriers, and interests of those humans in your data. The best possibility of a survey is to make the humans in that data feel included and belong by listening and acting on their perspectives. Co-create change with your community in those surveys. #nonprofits #nonprofitleadership #community #inclusion

Misaligned analytics is like running a relay where every runner lines up in the lane next to the previous runner, never making a hand-off, just running alongside them, wondering "when will we reach our goal?" That’s what happens when sales, marketing, and support aren’t working from the same playbook. → Data silos form. → Confusion spikes. → Precious time and trust are lost. For Marketing Ops pros, fixing analytics misalignment isn’t just a nice-to-have. It’s mission-critical. When KPIs don’t line up, no amount of dashboards or AI can save the business from bad decisions. Misaligned analytics waste effort and erode confidence. And confidence is the oxygen for cross-functional execution. And the fix is transformational Common Pitfalls: Redundant reporting, disconnected datasets, and “data theater.” Alignment Blueprint: Shared KPIs, integrated tools, and agreed-upon definitions. The MOps Advantage: Operators live at the intersection of every dataset, which uniquely positions them to lead this charge. Real-World Payoff: Smoother collaboration, cleaner decisions, stronger customer experiences. Ops pros know that clarity = alignment. When everyone’s looking at the same truth, strategy stops being a debate and starts being action. Tell me what one challenge you’ve faced in aligning analytics across teams? Let’s swap stories that can save each other months of frustration.

Financial dashboards tell you what happened. They rarely tell you what is likely to happen next. That’s the gap this CEO KPI framework is meant to address. What it surfaces is simple but often missed: the conditions that shape future revenue and margins change well before the numbers themselves do. Issues in execution discipline, leadership alignment, customer experience, or innovation momentum don’t hit the P&L immediately — but they materially influence where it goes next. That’s why these KPIs matter as a system, not as isolated metrics. Some reflect outcomes — like financial performance. Others act as early signals — like strategic execution, customer success, leadership effectiveness, or innovation momentum. ↘️ Together, they show whether the organisation is: • strengthening beneath the surface • quietly accumulating strain • or becoming dependent on short-term effort and individual heroics ↘️ When leadership attention is skewed toward only a few metrics, familiar patterns appear: • Strong numbers with growing internal friction • Busy teams but slowing execution • “Sudden” surprises that were forming quietly • Decisions that turn reactive instead of deliberate ↘️ Organisations that track this full system consistently tend to behave differently: • Execution issues surface earlier • Decision quality improves at the leadership level • Dependency on individual heroics reduces • Confidence with boards and investors builds over time This is why CEO KPIs are not just performance measures. They are attention-allocation mechanisms. What leaders choose to track — and discuss regularly — shapes how the organisation thinks, prioritises, and ultimately grows. If you’re involved in leadership reviews, strategy discussions, or board conversations, this framework offers a clearer way to look beyond quarterly numbers and understand organisational trajectory. ♻️ If this helps you see performance differently, save it. ♻️ If it helps reframe a leadership or boardroom conversation, share it. #CEO #Leadership #KPIs #BusinessStrategy #OrganisationalHealth #ValueCreation #Boardroom #FounderLed #PromoterLed #GauravMalik