Unlock Business Value from Data

Introduction: Bridging Business and Technology

In today’s data-driven world, transforming raw data into actionable business value requires more than technical prowess. It demands a strategic approach that aligns data initiatives with organizational goals, maturity, and evolving technologies. This guide breaks down key insights from Chris Tab—a recognized data strategist and influencer—to help businesses achieve sustainable value through data.

Understanding Data Strategy

What is a Data Strategy? A data strategy is a structured plan that aligns data initiatives with business objectives, ensuring that every step taken with data supports the company’s goals. It encompasses data management, data security, data engineering, analytics, AI, and machine learning. A successful data strategy bridges the gap between business needs and technology, translating requirements into value at the right time and in the right order.

• Aligns data projects with business objectives
• Defines clear roadmaps and maturity paths
• Ensures scalable, flexible, and secure data architectures

Why Data Strategy Matters Without a clear data strategy, organizations risk investing in tools and technologies that don’t deliver value. A strategic approach ensures every investment and initiative is mapped to measurable business outcomes, reducing risk and maximizing ROI.

Data Maturity: Mapping the Journey

Data maturity refers to an organization’s ability to manage, govern, and extract value from data. Chris Tab emphasizes the importance of assessing where your company stands on the data maturity curve and tailoring your approach accordingly.

1. Assess Current State: Identify existing capabilities, gaps, and business needs.
2. Define Maturity Roadmap: Create a step-by-step plan to evolve processes, technologies, and culture.
3. Iterate & Optimize: Continuously review, learn, and adjust based on results and organizational shifts.

Example: A small business may rely on spreadsheets and basic reporting, while an enterprise like Coca-Cola might deploy advanced data science to optimize operations. Both can benefit from a maturity assessment to identify the next best steps.

The Modern Data Stack: Evolution, Challenges, and Opportunities

The modern data stack refers to a set of cloud-native tools and platforms (e.g., Snowflake, ThoughtSpot, orchestration, data governance, observability) designed to enable scalable, flexible data architectures. However, rapid innovation and tool proliferation can create complexity, integration challenges, and lost business value.

• First Era: Monolithic stacks (e.g., Cognos) - Comprehensive but accessible only to large enterprises.
• Second Era: Cloud and modular stacks - Democratized, but often fragmented and complex.
• Third Era: Emergence of data platforms/operating systems which orchestrate diverse tools, making data accessible and valuable to business users as well as technical teams.

Best Practices:

• Start with a strong foundation (data modeling, governance, data ops, finops).
• Adopt an iterative approach—begin with an MVP targeting a real business pain point.
• Scale and optimize as the organization matures.

From Technology to Business Value

Chris Tab stresses that successful data projects focus on business value, not just technology. Too often, teams get caught up in features and tools, losing sight of the ultimate goal: driving measurable impact for the business.

How to Focus on Value:

• Embed data professionals within business teams to foster collaboration and understanding.
• Prioritize business outcomes over technical elegance.
• Use maturity assessments to guide investments and avoid repeating past mistakes.

The Role of Influencers and Community

Influencers help shape industry trends, challenge the status quo, and share practical lessons. Chris Tab’s journey began as a business need—building credibility and sharing insights to help others avoid common pitfalls. Key lessons:

• Share both successes and failures to build community learning.
• Engage with other thought leaders to refine and expand perspectives.
• Encourage open discussion of what works and what doesn’t in data initiatives.

Critical Success Factors in Data Initiatives

1. Foundational Capabilities: Ensure data modeling, governance, observability, and finops are embedded from the start.
2. Iterative Delivery: Deliver quick wins through MVPs, then scale incrementally.
3. Community & Collaboration: Foster a culture of sharing, continuous learning, and transparency.
4. Executive Alignment: Link every data initiative to a business objective and secure sponsorship from stakeholders.
5. Balance Innovation & Governance: Allow room for experimentation while maintaining necessary controls.

Common Pitfalls and How to Avoid Them

• Over-investing in technology before establishing business value.
• Neglecting data modeling and governance, leading to chaos as complexity grows.
• Failing to iterate—trying to build a perfect system up front instead of learning and adapting.
• Lack of alignment between IT and business stakeholders.
• Emotional attachment to tools or custom solutions that don’t serve business needs.

Tip: Always map technology decisions to business goals and outcomes.

Modern Data Stack in Practice: Snowflake as an Example

Snowflake exemplifies the evolution of the data platform, offering:

• Scalability and elasticity—on-demand compute and storage.
• Zero-copy cloning and data sharing for rapid, secure collaboration.
• Support for structured, semi-structured, and real-time data.
• Integration with leading tools for analytics, governance, and machine learning.

Chris Tab’s Perspective: Snowflake’s simplicity, flexibility, and ecosystem enable faster value realization, especially when paired with robust governance and cost optimization.

Organizational Patterns: Small vs. Large Enterprises

Organizational size and maturity impact data strategy:

• Small Companies: Agile, quick to adapt, but may lack governance and process rigor.
• Large Enterprises: Strong controls and governance, but may struggle with agility and change management.

Successful strategies borrow best practices from both, tailoring approaches to organizational context.

Measuring and Demonstrating Business Value

Measuring value is challenging but essential. Chris Tab recommends:

• Define clear KPIs linked to company strategy and business outcomes.
• Start with low-hanging fruit to demonstrate quick wins and build credibility.
• Continuously assess and communicate progress, adjusting as needed.

Example Approach:

1. Map data initiatives to strategic business objectives.
2. Use dashboards and metrics that directly reflect impact on these objectives.
3. Iterate and refine based on feedback and results.

Building a Data-Driven Community

Community and collaboration are crucial for success:

• Encourage sharing of pain points, solutions, and best practices across teams.
• Foster champions within departments to drive adoption and cultural change.
• Host regular sessions (e.g., brown bags, meetups) to share lessons learned.

Future Trends: Data as an Operating System

The future of data platforms lies in abstraction and simplification—integrating diverse tools into cohesive platforms that support business users and technologists alike. Expect to see:

• Further convergence of data, devops, and business operations.
• Rise of low-code/no-code solutions for business users.
• Expansion of data-driven applications (ERP, CRM) built directly on modern data platforms.

Key Takeaway: The next generation of business applications will be powered by robust, scalable data platforms, enabling faster innovation and value creation.

Conclusion: Your Roadmap to Success

1. Assess your data maturity and align strategy with business goals.
2. Build foundational capabilities (modeling, governance, data ops) early.
3. Deliver incremental value through MVPs and quick wins.
4. Foster a culture of collaboration and continuous improvement.
5. Leverage modern data platforms like Snowflake to accelerate value realization.

By following these principles, organizations of any size can maximize the business value of their data and stay ahead in the evolving landscape of data strategy and technology.