Four 4s (4x4x4x4) Formula: Structuring the Data Practice in Organizations

The Four 4s (4x4x4x4) Formula provides a structured approach for organizations to build and manage their data practice effectively. It is divided into four sets of dimensions that guide the data teams through the lifecycle of data products, from identification and development to problem-solving and optimization. The first set focuses on managing the lifecycle of data products, ensuring they are relevant and valuable. The second set emphasizes the process of transforming business inquiries into actionable insights and creating tangible value. The third set places emphasis on understanding the decision-making context, aligning stakeholders, and addressing challenges. Finally, the fourth set covers the governance and impact of data initiatives, focusing on metrics like ROI, Time to Market, and ownership structure. This holistic approach enables organizations to drive business value while maintaining strong data governance.

1. Data Product Lifecycle & Problem-Solving Focus
2. From Inquiry to Value Creation
3. Decision-Making Context or Data Story
4. Impact & Governance

Data Product Lifecycle & Problem-Solving Focus

1. Evaluate and Decommission Data Products (Products On Self) 📦: Continuously assess data products, manage their lifecycle, and decide whether to evolve, retire, or replace them based on business value and relevance.
2. Solve Problems for Existing Data Products (Products with Challenge) 🔧: Ensure ongoing optimization, addressing gaps, inefficiencies, and business challenges tied to existing data products.
3. Provide Data Products for New Use Cases (New Products in View) 🔍: Identify emerging business needs and develop data products that enable innovation and competitive advantage.
4. Discover and Advocate for New Use Cases (New Products not in View) 🚀: Actively explore new opportunities for leveraging data and champion the adoption of valuable data products across the organization.

From Inquiry to Value Creation

1. Define the Key Business Questions (Questions) ❓: Work with stakeholders to understand what the business wants to ask and solve through data products.
2. Identify Expected Insights & Outcomes (Answers) 💡: Clarify what answers, predictions, or intelligence the business seeks from data products.
3. Drive Actionable Decisions (Actions) 🏃♂️: Ensure that insights from data products lead to well-defined actions and measurable business decisions.
4. Maximize Business Value (Value) 💰: Translate data-driven actions into tangible benefits such as revenue growth, cost savings, efficiency gains, or risk mitigation.

Decision-Making Context or Data Story

1. Engage Key Stakeholders (Characters) 👥: Collaborate with business leaders, data teams, regulators, and end-users to align data initiatives with organizational needs.
2. Understand Business Drivers & KPIs (Context) 📊: Ensure data products address real business challenges by linking them to strategic goals and performance metrics.
3. Assess the Cost of Inaction (COI) ⚠️: Highlight the risks, inefficiencies, and missed opportunities if data-driven decisions are not made.
4. Define a Strategic Resolution Path (Road to Resolution) 🛣️: Establish a clear vision, milestones, and execution plan to drive meaningful data-driven outcomes.

Impact & Governance

1. Measure ROI of Data Initiatives (ROI) 📈: Ensure data investments generate financial and strategic returns by tracking adoption, efficiency, and business impact.
2. Optimize Time to Market (TTM) ⏱️: Drive efficiency in data product development and deployment to deliver value faster.
3. Align Data Products with Business Domains (Domain) 🌍: Structure and categorize data products according to business functions (e.g., sales, marketing, finance) for better alignment and usability.
4. Define Data Product Ownership Structure (Ownership) 🏢: Establish clear accountability by assigning business owners for strategic direction and technical owners for maintaining product health.

Cheers.