Strategies for Implementing Data-Driven Innovation

GenAI has taken the world by storm and entered the boardrooms, exec suites and labs of most major firms. However the question of how to effectively enable for impact and scale these capabilities is not discussed enough and most have yet to overcome this challenge. My co-authors (Joe Caserta, Holger Harreis, Nikhil Srinidhi and Dr. Asin Tavakoli) and I have identified seven actions that data leaders should consider as they move from experimentation to scale. These include: 1) Let value be your guide. CDOs need to be clear about where the value is and what data is needed to deliver it. 2) Build specific capabilities into the data architecture to support the broadest set of use cases. Build relevant capabilities (such as vector databases and data pre- and post-processing pipelines) into the existing data architecture, particularly in support of unstructured data. 3) Focus on key points of the data life cycle to ensure high quality. Develop multiple interventions—both human and automated—into the data life cycle from source to consumption to ensure the quality of all material data, including unstructured data. 4) Protect your sensitive data, and be ready to move quickly as regulations emerge. Focus on securing the enterprise’s proprietary data and protecting personal information while actively monitoring a fluid regulatory environment. 5) Build up data engineering talent. Focus on finding the handful of people who are critical to implementing your data program, with a shift toward more data engineers and fewer data scientists. 6) Use generative AI to help you manage your own data. Generative AI can accelerate existing tasks and improve how they’re done along the entire data value chain, from data engineering to data governance and data analysis. 7) Track rigorously and intervene quickly. Invest in performance and financial measurement, and closely monitor implementations to continuously improve data performance Happy reading. #data #genai #datascience #ai #analytics #mckinsey

Few have ever done what I’m lucky enough to be doing for the third time: building a data and AI strategy for a startup at zero. To build Parachute Group as a data and AI-first company, I must overcome the data cold start problem: Where do you begin at a business with no data? Here’s how to start. The answer is built from the frameworks I teach in my courses, which I have used for over a decade for clients. Phase 1: Engineer Access To Data Generating Processes. The first 6 months focus on engineering the data flywheel’s foundations. We’re starting with the highest value internal operations, apprentice, and client workflows. Data that’s connected to a user or customer workflow is contextualized and can be transformed into information. Phase 2: Introduce Data & Analytics Into The Workflow. Going directly to AI is always a mistake. Once we have workflow transparency, it’s time to measure the impact of providing experts with information at critical task and decision points. Phase 2 is iterative and experimental. If we introduce relevant information into a workflow (experiment), we expect to see improvements in key outcomes (results). We validate or refute our understanding of the workflow one experiment at a time. That’s how knowledge graphs are assembled. For an AI-first business, its knowledge graph creates information asymmetry. We know something competitors don’t, and we will use AI to monetize and scale that competitive advantage. Startups are a tug-of-war between value delivery and efficiency. The data and AI strategy helps the business rapidly scale value delivery without scaling costs. Start by engineering access to key internal and external customer workflows and iterate forward using the simplest approach possible.

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.

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