How to Turn Data Into Strategic Assets

Using Data to Drive Strategy: To lead with confidence and achieve sustainable growth, businesses must lean into data-driven decision-making. When harnessed correctly, data illuminates what’s working, uncovers untapped opportunities, and de-risks strategic choices. But using data to drive strategy isn’t about collecting every data point — it’s about asking the right questions and translating insights into action. Here’s how to make informed decisions using data as your strategic compass. 1. Start with Strategic Questions, Not Just Data: Too many teams gather data without a clear purpose. Flip the script. Begin with your business goals: What are we trying to achieve? What’s blocking growth? What do we need to understand to move forward? Align your data efforts around key decisions, not the other way around. 2. Define the Right KPIs: Key Performance Indicators (KPIs) should reflect both your objectives and your customer's journey. Well-defined KPIs serve as the dashboard for strategic navigation, ensuring you're not just busy but moving in the right direction. 3. Bring Together the Right Data Sources Strategic insights often live at the intersection of multiple data sets: Website analytics reveal user behavior. CRM data shows pipeline health and customer trends. Social listening exposes brand sentiment. Financial data validates profitability and ROI. Connecting these sources creates a full-funnel view that supports smarter, cross-functional decision-making. 4. Use Data to Pressure-Test Assumptions Even seasoned leaders can fall into the trap of confirmation bias. Let data challenge your assumptions. Think a campaign is performing? Dive into attribution metrics. Believe one channel drives more qualified leads? A/B test it. Feel your product positioning is clear? Review bounce rates and session times. Letting data “speak truth to power” leads to more objective, resilient strategies. 5. Visualize and Socialize Insights Data only becomes powerful when it drives alignment. Use dashboards, heatmaps, and story-driven visuals to communicate insights clearly and inspire action. Make data accessible across departments so strategy becomes a shared mission, not a siloed exercise. 6. Balance Data with Human Judgment Data informs. Leaders decide. While metrics provide clarity, real-world experience, context, and intuition still matter. Use data to sharpen instincts, not replace them. The best strategic decisions blend insight with empathy, analytics with agility. 7. Build a Culture of Curiosity Making data-driven decisions isn’t a one-time event — it’s a mindset. Encourage teams to ask questions, test hypotheses, and treat failure as learning. When curiosity is rewarded and insight is valued, strategy becomes dynamic and future-forward. Informed decisions aren't just more accurate — they’re more powerful. By embedding data into the fabric of your strategy, you empower your organization to move faster, think smarter, and grow with greater confidence.

𝗕𝗲𝘆𝗼𝗻𝗱 𝗠𝗟 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: 𝗛𝗼𝘄 𝗧𝗿𝘂𝗲 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗖𝗿𝗲𝗮𝘁𝗲𝘀 𝗠𝗮𝗿𝗸𝗲𝘁 𝗗𝗼𝗺𝗶𝗻𝗮𝗻𝗰𝗲 Two years ago, I witnessed a pivotal moment. Two competitors in the same industry launched AI initiatives with nearly identical budgets. Today, one has transformed its market position while the other quietly disbanded its AI team. The difference wasn't talent, technology, or timing. It was the presence of true AI leadership. After guiding AI transformations across multiple sectors, I've observed a clear pattern: organizations conflate technical implementation with strategic leadership — a costly misconception in the algorithmic age. 𝗧𝗵𝗲 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗗𝗶𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 Most executives approach AI through a traditional technology lens: selecting vendors, implementing solutions, and measuring ROI. However, organizations creating asymmetric returns operate from a fundamentally different framework. When I joined a life sciences company's transformation, they had invested $15M in ML capabilities with minimal impact. Within 18 months of shifting to an AI leadership approach, those same technical assets drove a 28% market share increase in their core business line. 𝗧𝗵𝗲 𝗧𝗵𝗿𝗲𝗲 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 True AI dominance emerges at the intersection of three capabilities most organizations develop in isolation: 𝟭. 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Redesigning core business processes around algorithmic decision-making, not just augmenting existing workflows. One healthcare organization restructured its entire patient journey based on predictive insights, creating a competitive moat its technology-focused competitors couldn't replicate. 𝟮. 𝗗𝗮𝘁𝗮 𝗦𝗼𝗽𝗵𝗶𝘀𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Moving beyond data volume to data uniqueness. The market leaders I've worked with systematically identify and capture proprietary data assets that create algorithmic advantages that are impossible for competitors to match, regardless of their AI investment. 𝟯. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆: Implementing governance models built for algorithmic speed. One financial services firm reduced model deployment from months to days, allowing them to capture temporary market inefficiencies before competitors could respond. The organizations achieving market dominance are those with leadership capable of orchestrating these dimensions simultaneously. Have you observed this leadership gap in your industry? 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: 𝘛𝘩𝘦 𝘷𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴 𝘰𝘳 𝘳𝘦𝘭𝘢𝘵𝘦𝘥 𝘦𝘯𝘵𝘪𝘵𝘪𝘦𝘴. 𝘌𝘹𝘢𝘮𝘱𝘭𝘦𝘴 𝘥𝘳𝘢𝘸𝘯 𝘧𝘳𝘰𝘮 𝘮𝘺 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦 𝘩𝘢𝘷𝘦 𝘣𝘦𝘦𝘯 𝘢𝘯𝘰𝘯𝘺𝘮𝘪𝘻𝘦𝘥 𝘢𝘯𝘥 𝘨𝘦𝘯𝘦𝘳𝘢𝘭𝘪𝘻𝘦𝘥 𝘵𝘰 𝘱𝘳𝘰𝘵𝘦𝘤𝘵 𝘤𝘰𝘯𝘧𝘪𝘥𝘦𝘯𝘵𝘪𝘢𝘭 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯.

Let me share a personal story that changed my perspective on data's role in decision-making. Picture this: I'm on the New York subway platform, staring at the digital display. "Next train: 6 minutes." Useful? A bit. But I've already swiped my card and committed to this train line. All I can do is figure out how to best use the wait time. This is classic Business Intelligence (BI) - information that's useful but not action-oriented. Now, fast forward a few years. The MTA installs displays outside the stations. Seeing a 6-minute wait for the local train, I now have a choice. It's a 4-minute walk to the express station. Stay or go? This is Decision Intelligence (DI) - the power of right place, right time delivery. The same principle applies to our role as CDOs. We often pour resources into creating insights, reports, and metrics, but then neglect that crucial last mile - getting the right information to the right person at the right time. Here's how we can shift from BI to DI in our organizations: 1. Identify Key Decision Points Where in the business cycle are your stakeholders making critical decisions? That's where your data products need to be integrated and ready to use. 2. Focus on Actionable Insights Don't just report what happened. What's relevant to the decision-maker? Is your insight in the "good to know" category or the "option A is vastly better" category? 3. Optimize the Last Mile Think about how you're delivering insights. Are they embedded in the decision-making process or sitting in a separate report? This shift isn't just about technology - it's about positioning data as a profit enabler, not a support function - from data aware to data driven. This is how we move from being seen as a cost centre to becoming a strategic partner directly contributing to the core objectives of the business. *** 2500+ data executives are subscribed to the 'Leading with Data' newsletter. Every Friday morning, I'll email you 1 actionable tip to accelerate the business potential of your data & make it an organisational priority. Would you like to subscribe? Click on ‘View My Blog’ right below my name at the start of this post.

Last week, I posted about data strategies’ tendency to focus on the data itself, overlooking the (data-driven) decisioning process itself. All it not lost. First, it is appropriate that the majority of the focus remains on the supply of high-quality #data relative to the perceived demand for it through the lenses of specific use cases. But there is an opportunity to complement this by addressing the decisioning process itself. 7 initiatives you can consider: 1) Create a structured decision-making framework that integrates data into the strategic decision-making process. This is a reusable framework that can be used to explain in a variety of scenarios how decisions can be made. Intuition is not immediately a bad thing, but the framework raises awareness about its limitations, and the role of data to overcome them. 2) Equip leaders with the skills to interpret and use data effectively in strategic contexts. This can include offering training programs focusing on data literacy, decision-making biases, hypothesis development, and data #analytics techniques tailored for strategic planning. A light version could be an on-demand training. 3) Improve your #MI systems and dashboards to provide real-time, relevant, and easily interpretable data for strategic decision-makers. If data is to play a supporting role to intuition in a number of important scenarios, then at least that data should be available and reliable. 4) Encourage a #dataculture, including in the top executive tier. This is the most important and all-encompassing recommendation, but at the same time the least tactical and tangible. Promote the use of data in strategic discussions, celebrate data-driven successes, and create forums for sharing best practices. 5) Integrate #datascientists within strategic planning teams. Explore options to assign them to work directly with executives on strategic initiatives, providing data analysis, modeling, and interpretation services as part of the decision-making process. 6) Make decisioning a formal pillar of your #datastrategy alongside common existing ones like data architecture, data quality, and metadata management. Develop initiatives and goals focused on improving decision-making processes, including training, tools, and metrics. 7) Conduct strategic data reviews to evaluate how effectively data was used. Avoid being overly critical of the decision-makers; the goal is to refine the process, not question the decisions themselves. Consider what data could have been sought at the time to validate or challenge the decision. Both data and intuition have roles to play in strategic decision-making. No leap in data or #AI will change that. The goal is to balance the two, which requires investment in the decision-making process to complement the existing focus on the data itself. Full POV ➡️ https://lnkd.in/e3F-R6V7

I've come to understand that the real magic happens when you can transform raw data into actionable insights. Now this logic probably won't work in your relationships, but ... you'll most likely find more success at work. 😆 Achieving this requires more than just intuition; it demands a rigorous, strategic approach to data analysis, especially critical during those pivotal monthly and quarterly reviews, and some great debate conversational skills-you'll see why. What revenue leaders need—and what new marketing leaders must learn—is the importance of grounding their strategies in solid, data-driven evidence. *Read THAT AGAIN. To navigate those conversations, one must rely on reports(data), meticulously tailored to various segmentations such as persona and use cases. This is how one navigates from the ASK -> ACTION. The sales funnel is your beacon in navigating the complex journey of #demandgeneration. It offers a detailed view into the genesis of revenue, tracking Closed Won (CW) opportunities by pipeline source (PS), and dissecting metrics such as Average Annual Recurring Revenue (ARR) and sales cycle lengths. This analysis extends to the creation and conversion rates of qualified opportunities, providing a clear picture of your marketing effectiveness. The #attribution analysis is essential for understanding the impact of our marketing efforts. By categorizing qualified opportunities and high-intent submissions through self-reported attribution (SRA), we can pinpoint the most effective channels and "touchpoints," guiding our investment strategies. This one pains me sometimes; investment insights. We examine everything from total marketing spend to Customer Acquisition Cost (CAC) and the payback periods, ensuring every dollar is accounted for and aimed towards maximizing ROI. For new marketing leaders, here's my advice: Live in the Data. Use these reports as lenses through which to view the entire marketing landscape. Each campaign, whether it be a podcast series or paid media, should be meticulously tracked and analyzed. This not only provides a roadmap for navigating through the complexities of marketing strategies but also acts as a powerful mentorship tool, enabling your team to quickly identify and capitalize on opportunities for improvement. In essence, the arsenal of reports and analytical tools we've developed are more than a collection of data points. It's a strategic asset that enables us to continuously refine our approach, ensuring our marketing efforts are not just efficient but strikingly effective. By embracing a data-first mentality, we navigate the competitive digital landscape with confidence, driving growth and success through informed, evidence-based strategies. This is the new paradigm for marketing leadership, one where data and action converge to create tangible results. #digitalmarketing #dataanalytics #growthmarketing #marketinginsights

🚨 𝐇𝐨𝐰 𝐭𝐨 𝐔𝐧𝐥𝐨𝐜𝐤 𝐕𝐚𝐥𝐮𝐞 𝐟𝐫𝐨𝐦 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐃𝐚𝐭𝐚 — 𝐛𝐲 DEUNA 👇 As commerce evolves into an intelligent, agent-driven ecosystem, payments data is emerging as a critical strategic asset — but one that remains vastly underutilized. This post outlines the key limitations holding teams back — and how leading merchants are transforming data into a competitive advantage. — 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐃𝐚𝐭𝐚 Today’s commerce stack generates rich and interconnected data from multiple systems: → Payments Data: transaction metadata, approval rates, methods, issuer response → Orders Data: cart value, SKUs, timestamps, discounts → Identity Data: device fingerprinting, customer behavior, fraud signals → Other Sources: CRM, logistics, loyalty, geography, and more — 𝟑 𝐒𝐲𝐬𝐭𝐞𝐦𝐢𝐜 𝐁𝐥𝐨𝐜𝐤𝐞𝐫𝐬 𝐓𝐨 𝐕𝐚𝐥𝐮𝐞 𝐑𝐞𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 1️⃣ Fragmented & Inconsistent Data → 80% of data teams’ time is spent cleaning instead of analyzing → Siloed PSPs, fraud tools, and BI platforms prevent unified decision-making 2️⃣ Context-less AI doesn't deliver  → 90% of AI projects fail to operationalize → Without business context, generic AI outputs remain disconnected from strategic goals 3️⃣ Constant Firefighting, Minimal Strategy → Operational teams are stuck responding to incidents instead of driving outcomes — 𝐇𝐨𝐰 𝐋𝐞𝐚𝐝𝐢𝐧𝐠 𝐓𝐞𝐚𝐦𝐬 𝐔𝐧𝐥𝐨𝐜𝐤 𝐕𝐚𝐥𝐮𝐞 The process to transform raw payments data into actionable intelligence: 1️⃣ Data Ingestion → Consolidating data streams from PSPs, checkout systems, CRMs, fraud tools, and commerce platforms into a central layer 2️⃣ Variable Definition & Structuring → Translating raw fields into business metrics (e.g., GMV, approval rate, ARPU) 3️⃣ Real-Time Monitoring & Alerts → Establishing live observability for key indicators (e.g., spikes in declines, fraud signals, latency issues) 4️⃣ Cleaning, Deduplication & Standardization → Removing duplicates, normalizing formats (e.g., date, currency, identifiers) 5️⃣ Industry-Specific Contextualization → Applying domain knowledge (e.g., travel refunds, retail loyalty...) to reclassify and enrich data 6️⃣ Addition of Exogenous Variables → Integrating external factors such as FX volatility, holidays, weather, or marketing campaigns to reveal hidden correlations and refine models. 7️⃣ AI Implementation → Using ML and intelligent agents to detect anomalies, identify high-impact opportunities, and automate routing, retries... in real time. — 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧: 𝟑 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐃𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐓𝐡𝐞 𝐍𝐞𝐰 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 1️⃣ Unified Data Foundation 2️⃣ Context-Aware Opportunity Detection 3️⃣ Real-Time Execution on Intelligence → Automate retry logic, fraud triggers, routing adjustments, and more — Source: DEUNA ► Subscribe to 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐁𝐫𝐞𝐰𝐬: https://lnkd.in/g5cDhnjC ► Connecting the dots in payments... | Marcel van Oost

I’ve been a CEO for 30+ years. When I’ve had RevOps that felt like a strategic asset, it’s because they were able to deliver actionable, proactive insights to senior leadership. Here’s what they did: 1. Eliminated friction. There’s so much complexity in GTM that it’s easy for processes to become inefficient. Great RevOps teams excel at uncovering the internal inefficiencies that depress win rates and slow down sales cycles. When they show me where we're losing speed—and how—I understand the set of actions available to me as I recalibrate for success. 2. Provided insights on funnel. Outstanding RevOps can walk you through the whole lead→ deal funnel process. Everything from strategies being used to process breakdowns to insight into modest performers. As a CEO, you're not always on sales calls, so you need someone who can pull out the most critical details and present them clearly. Good, strategic RevOps delivers insights around both of these topics regularly. They have their finger on the pulse. They gather the data and package it. Then they give executives the "story" of the data, so we can take necessary action. That's what actionable optimization insights look like. It's not just a series of unrelated Excel sheets. It's the right data points brought together to create a clear picture. And that data HAS to be meaningful and lead to informed decision-making. Otherwise, it's just more noise.