How to Create a Data-First Strategy

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.

Where do I start? This is arguably the question I’ve been asked the most by data leaders tasked with a large scale transformation initiative. The transformation could be a cloud migration, an ERP consolidation, or any large data-centric replatforming that involves a complex web of people, process, and technology. Quite often, many leaders have convinced themselves, or have been guided by a consultant, that taking a ‘bottoms up’ approach that starts with with an inventory of the data, often along with some form of a maturity assessment, is the right way to go. It’s not. The right way to go is to take an outcome-driven approach where you are rabidly focused on solving a very limited number of business problems. Each problem would have a well defined and limited scope, and would be accompanied by a business case where the financial benefits of that initiative are quantified, and aligned upon by your customers. Instead of focusing on all data, you’ll instead inventory, observe, govern, steward, master and integrate only the data needed to solve your immediate problem. Yes, some idea of the ‘future state’ must be defined and you need to ensure you’re building out an architecture that is scalable and flexible, but complete clarity on all aspects of every individual deliverable between now and that future state do not need to be defined. If you focus each of your phases around solving specifc problems, you will build the momentum and business support you need to get more funding, and slowly grow the program over time. Instead of taking a ‘framework driven’ approach that ensures your customers will have to wait 18+ months to see any value, your customers will get benefits now. Don’t be foooled into thinking that you need to catalog and govern everything in order to transform your data estate. You don’t. Focus on solving business problems and in time, you’ll catalog and govern what matters the most. What do you think? If you have different ideas on where to start, I would love to hear them? #cdo #datagovernance #datamanagement

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