Understanding the Business Value of Data

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.

Over the past 9 months, I've travelled globally to meet with friends, professionals, longstanding customers, and partners. My primary goal was to understand the current market situation and everyone's "need of the hour." I'm happy to have gained deep insights into Digital Transformation needs. While the term may seem broad, our discussions pinpointed very specific requirements, bringing clarity to both short-term and long-term strategic planning. Many conversations were marked by clarity, allowing us to connect key dots—KPIs—to form a meaningful final image. Discussions raised critical questions: - Should we focus solely on immediate needs, or also plan for the future? - Should we replicate existing technological solutions, or allow room for detailed analysis? - How do we measure the value of our efforts, from every internal & external perspective? One rewarding aspect of my travels has been the candid discussions about business-specific needs for digital transformation. In today's rapidly evolving landscape, transformation requires a deep understanding of how to effectively utilize both tangible and intellectual resources. With 26 years of experience, I’m proud of our dedication to comprehending and supporting current and future transformational needs worldwide. Last year, I initiated a handbook filled with case studies to document our journey. We've now compiled over 300 case studies, each illustrating our tailored approach and providing clear insights. Data is more than a valuable asset—it is the lifeblood of every organization, essential for maintaining operational efficiency and growth. It’s not merely the new oil; it is the oxygen of every organization, crucial for making informed decisions and validating every step of our journey. Technology choice is tailored, and guided by specific business needs & KPIs. We often hear "data is the new oil," a valuable resource to be mined and monetized. Yet, after engaging with leaders globally, I've found a more fitting comparison: Data is like oxygen. Why Compare Data to Oxygen? Vital for Survival: Just like oxygen is essential for breathing, data is crucial for business efficiency and growth. Ensures Purity and Health: Clean data, like clean air, is fundamental for reliable insights and robust operations. Circulates Unseen, Impacts Profoundly: Data quietly influences every decision, similar to how oxygen affects our bodies. Supports Adaptation and Response: Data enables businesses to quickly adapt to market changes and customer needs. Sustains Life Over Time: As oxygen is vital over time, data drives long-term sustainability through continuous improvement and innovation. This analogy isn't hypothetical—it's the reality of how we operate. Visible, well-utilized data underpins our survival, drives our efficiency, and propels our growth, just as oxygen does for humans. Data permeates our strategies, enhancing our ability to not only respond to but also anticipate market shifts.

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

The unprecedented proliferation of data stands as a testament to human ingenuity and technological advancement. Every digital interaction, every transaction, and every online footprint contributes to this ever-growing ocean of data. The value embedded within this data is immense, capable of transforming industries, optimizing operations, and unlocking new avenues for growth. However, the true potential of data lies not just in its accumulation but in our ability to convert it into meaningful information and, subsequently, actionable insights. The challenge, therefore, is not in collecting more data but in understanding and interacting with it effectively. For companies looking to harness this potential, the key lies in asking the right questions. Here are three pieces of advice to guide your journey in leveraging data effectively: 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟏: 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐆𝐨𝐚𝐥-𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 • Tactic 1: Define specific, measurable objectives for each data analysis project. For instance, rather than a broad goal like "increase sales," aim for "identify factors that can increase sales in the 18-25 age group by 10% in the next quarter." • Tactic 2: Regularly review and adjust these objectives based on changing business needs and market trends to ensure your data queries remain relevant and targeted. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟐: 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐂𝐫𝐨𝐬𝐬-𝐃𝐞𝐩𝐚𝐫𝐭𝐦𝐞𝐧𝐭𝐚𝐥 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 • Tactic 1: Conduct regular interdepartmental meetings where different teams can present their data findings and insights. This practice encourages a holistic view of data and generates multifaceted questions. • Tactic 2: Implement a shared analytics platform where data from various departments can be accessed and analyzed collectively, facilitating a more comprehensive understanding of the business. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝟑: 𝐀𝐩𝐩𝐥𝐲 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 • Tactic 1: Utilize machine learning models to analyze current and historical data to predict future trends and behaviors. For example, use customer purchase history to forecast future buying patterns. • Tactic 2: Regularly update and refine your predictive models with new data, and use these models to generate specific, forward-looking questions that can guide business strategy. By adopting these strategies and tactics, companies can move beyond the surface level of data interpretation and dive into deeper, more meaningful analytics. It's about transforming data from a static resource into a dynamic tool for future growth and innovation. ******************************************** • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Too many people think a data strategy is the plan for how to "get good at data." But I think that's a trap... The most common failure mode for data leaders is building infrastructure that doesn't drive enough business results. Instead data strategy should be the plan to "get good at getting results from data." Thinking about your data strategy like this leads to a much more people and project based strategy vs. infrastructure driven strategy for your team. What does this look like? Instead of the strategy to: "Launch the new data warehouse." The strategy would be: "Get the merchandising team the data they need to execute on their personalization strategy." Building a new data warehouse isn't success. Being great at using the data warehouse is success. Every organization is different, which is why every organization needs a different strategy to get good at getting business results from data. When you think about data strategy like this, what you immediately realize is that the quality and culture of your team is more important than any infrastructure decision. They are the ones at the tip of the spear helping change your organization to become better at getting results from data. And they're the ones who make your data strategy a success or failure.

Too often, data in organizations stays locked within specific teams, limiting its potential to drive real change. But when used effectively, data becomes a powerful tool for smarter decision-making and business transformation. Without the right infrastructure, skilled analysts, and a culture that values evidence-based decisions, data can quickly become overwhelming or even misleading. But when teams collaborate to extract and apply meaningful insights, that’s when organizations see the biggest impact. One of the greatest benefits of using data is the ability to make quick, informed decisions. I’ve seen this firsthand in my work with Fortune 500 companies and fast-growing SaaS startups—those that prioritize data and insights can pivot faster, respond to market shifts more effectively, and consistently outperform their competitors. However, it’s not just about having the right data, it’s also about asking the right questions. Contextualizing data from a product perspective is important. For example, when I help companies with product strategy, I always kick off by having an analyst create a holistic view of the business. Many companies already have dashboards with metrics like total ARR, retention, etc. But that is not enough. You need to segment these financial metrics with customer cohorts, product performance data, and other product related insights to get a better picture. This enables the C-suite to make informed decisions around our products. The challenge is building a culture where data drives decisions at every level. But when companies make that shift, the rewards—better products, stronger teams, and faster pivots—are well worth the effort. How is your organization leveraging data to make better decisions? Let me know in the comments.

This morning I was reading about how companies are using data more strategically, and it reminded me of something simple but powerful: data isn’t the goal—solving business problems is. One of the examples is, helping Sales and Marketing run more targeted, effective campaigns. But that only works if you start with the right questions: Which customers are most likely to respond to this offer? Who’s at risk of leaving? Where can we grow share of wallet? With that clarity, data and AI become tools for action—connecting customer insights across systems, spotting behavioral patterns, and helping teams move faster and with more precision. It’s not about building massive data infrastructure—it’s about creating value from what you already have. The real impact comes when data is unified, focused, and tied to real decisions. Start small, solve something meaningful, and let momentum do the rest. The smartest data strategies always begin with a smart question. #data #ai #genAI