Data Community: The Force Multiplier

Data is a strategic asset.

We've all heard this waxed poetically so often that the phrase has become almost cliché. Yet how often have you witnessed leadership make this declaration only to slash their analytics team when budget constraints arise? These leaders fail to recognize or believe that such cuts risk intensifying cost pressures rather than alleviating them.

This paradoxical behavior exemplifies an organization whose data fluency level is "data aware" or possibly "data proficient." In stark contrast, organizations that have achieved "sata savvy" or "data mastery" instinctively leverage their data, analytics, and AI capabilities to navigate challenges, drive innovation, and ride the inevitable wave of prosperity that follows—at times expanding their analytics teams during downturns.

I've been fortunate to have been part of this journey at Amazon during the dot-com bust—as I watched my equity and early retirement evaporate. Amazon's data mastery enabled them to laser-focus on high-value opportunities that systematically reduced costs while ushering in a new era of automation and personalization. Remarkably, we expanded our analytics resources during this turbulent period.

This pattern repeated when I joined Expedia during the 2008 financial crisis—again witnessing my retirement portfolio shrink dramatically. Expedia's data mastery empowered them to thoroughly evaluate and transform their business model, optimize the consumer’s digital experience, and negotiate advantageous long-term rates for inventory that positioned them for explosive growth as markets recovered.

I believe healthcare stands at a similar crossroads today. How is your organization responding? Are they demonstrating mere data awareness, basic data proficiency, advanced data savvy, or comprehensive data mastery? If your organization falls into the latter two categories, it likely has cultivated a mature, vibrant data community. If not, evolving this data community represents the critical next step in your data innovation journey, a force multiplier in value creation. Let’s dive in.

Building Your Data Community

As we discussed, the journey to data mastery requires a comprehensive approach that addresses technical infrastructure and human capabilities. While many organizations focus primarily on technology investments, truly data-fluent organizations recognize that human capital development is equally—if not more—important.

The Four Levels of Data Fluency (Recap)

As discussed previously, organizations progress through four levels of data fluency:

1. Data Aware: These organizations recognize the importance of data but lack the alignment and data literacy to leverage it effectively. Data exists in silos, analytics is performed ad-hoc, there is no single source of truth, there is significant duplication and waste, and decisions are still heavily based on intuition and anecdotal evidence. The data community has yet to take hold.
2. Data Proficient: At this level, organizations have established a data governance framework and standardized analytics processes. They become increasingly proficient at telling stories with data. That said, they still struggle with more complex, cross-functional analyses. The data community begins the form.
3. Data Savvy: These organizations have integrated data into their operational DNA. Cross-functional teams regularly collaborate using data, predictive analytics inform strategic and operational decisions, and data literacy is widespread across departments. The data community expands and becomes an influencing force.
4. Data Mastery: The pinnacle of data fluency, these organizations use data as a competitive advantage. They seamlessly combine human judgment with algorithmic insights, continuously innovate through data experimentation, and maintain a thriving data community that evolves with business needs. The data community is unified.

The illustration below shows this evolution and the change in focus from data and building a reusable, single source of truth to building analytic capabilities that leverage this truth to drive business value and impact. The data community is the force multiplier that accelerates business impact and moves an organization from data proficiency to data mastery.

The Backbone of Data Fluency

A thriving data community forms the backbone of any data-fluent organization. This community isn't limited to data scientists, analysts and engineers but encompasses individuals across functions who understand, value, and effectively utilize data in their decision-making processes.

The data community serves several critical functions:

• Knowledge Sharing: Creating channels for cross-functional collaboration and insight exchange.
• Decision Making: Shared context promotes decision-making where the work is done and improves the speed and quality of decision-making across the enterprise.
• Skill Development: Establishing formal and informal learning opportunities to elevate data literacy.
• Innovation Incubation: Providing safe spaces for experimentation and novel applications of data.
• Standardization: Developing common frameworks, practices, metrics, and tools for consistent analysis.
• Culture Creation: Championing data-driven decision-making throughout the organization. Data innovation is no longer the domain of a group or organization, it is part of the enterprise DNA—everyone innovates with data as a community.

Healthcare organizations that have successfully cultivated robust data communities demonstrate significantly higher returns on their analytics investments and greater resilience during periods of market volatility.

Practical Steps for Evolving Your Data Community

Moving from basic data awareness to true data mastery requires deliberate action across multiple dimensions. It is also about promoting agency and allowing evolution to happen organically as people test new ideas, make mistakes and grow from these mistakes. Here are key strategies to consider:

Establish a Compelling Brand Identity

The most successful data communities have distinct identities that resonate throughout the organization. In the book Sapiens, A Brief History of Humankind, Yuval Noah Harari shares that one of the theories for why Sapiens survived when other human species didn’t was their ability to organize around an idea. Without this ability, other species (Neanderthals, Homo Erectus, etc.) could organize a hundred people at best. Sapiens could organize thousands or more around an idea—a decisive advantage.

So, leverage our inner Sapiens to unify your data community and shape how analytics capabilities are perceived and utilized. No amount of restructuring or changes to reporting relationships will do more than branding if done right. Here are some branding guidelines:

• Distinctive Name and Visual Identity: Create a memorable brand that reflects your community culture while standing out (e.g., “Data & Analytics (DNA),” “Analytics Resource Center (ARC),” or “Enterprise Analytics”). Remember, this brand doesn’t belong to any single organization, it belongs to the data community.
• Clear Value Proposition: Articulate what makes your data community unique and the specific value it delivers to the enterprise. Be big, bold and aspirational.
• Consistent Messaging: Develop standard language that communicates capabilities, success stories, and opportunities.
• Recognition Programs: Establish branded awards and recognition for data-driven achievements.
• Rebranding: If an existing brand exists but has a bad reputation or is old, rebranding is a great way to get a fresh start.

A strong brand transforms your data community from an abstract concept to a tangible resource that stakeholders actively seek to engage with. It creates an emotional connection and professional pride among community members while signaling an organizational commitment to data excellence.

For more on branding, refer to the book Hooked—How to Build Habit-Forming Products by Nir Eyal. It offers many great insights from decades of marketing lessons.

Design a Shared Operating Model

Your data community's operating model determines how efficiently it delivers value and integrates seamlessly with existing organizational structures. Here are a few considerations:

• Engagement Framework: Create an approach that aligns technical and clinical business resources into small, multidisciplinary, agile teams that deliver on strategic and operational priorities.
• Governance Structure: Define clear decision rights, escalation paths, and accountability frameworks.
• Guiding Principles: Fundamental beliefs or values that direct decision-making and shape organizational behavior.
• Measurement System: Implement metrics that track both community health and business impact.
• Funding Mechanisms: Establish sustainable funding approaches (centralized, chargeback, hybrid) aligned with organizational priorities.
• Talent Model: Design career paths, role definitions, and skill matrices that enable growth.

The right operating model balances centralization and decentralization, as well as standardization and flexibility. It aligns business, clinical and technology stakeholders, promotes agency and decision-making where the work is done, quickly adapts to the rapidly evolving healthcare landscape, and delivers value incrementally and often.

We discussed the operation model in detail in the Accelerate Your Data Innovation Journey (AYDIJ) series, “Operate Like a Start-Up” and “Think Like an Entrepreneur.” The only thing I would change is referring to the operating model as federated. Since writing that piece, “federated” has become so overloaded with different definitions that it makes the characterization meaningless, if not confusing. More on how to conceptualize and evolve the operating model will be discussed in future articles.

Cultivate Executive Sponsorship

Transformation begins at the top. Executives must verbally support data initiatives and demonstrate their commitment through resource allocation and personal engagement. This means:

• Including data literacy as a core leadership competency.
• Requiring data-backed justifications for major decisions.
• Participating in data community events and recognizing contributions.
• Protecting analytics resources during budget constraints, recognizing their multiplicative value.
  

Develop Tiered Learning Pathways

Different roles require different levels of data proficiency. Develop customized learning journeys that address the following:

• Basic Data Literacy: For all employees, focusing on interpreting common visualizations, understanding key metrics, and recognizing biased analyses.
• Intermediate Analytics Skills: For managers and frequent data consumers, covering tool proficiency, basic statistical concepts, and effective data communication.
• Advanced Analytics Expertise: For specialists, encompassing advanced statistical methods, machine learning applications, and data engineering principles.
  

Create a Community Commons

Formal and informal structures that facilitate community growth include:

• Analytics Centers of Excellence: Centralized teams that cultivate standards, provide consulting services, and enable major initiatives.
• Data Champions Networks: Cross-functional representatives who serve as local experts and change agents.
• Community Events: Hackathons, town halls, demo days, knowledge-sharing sessions, and guest speakers that energize and inspire.
• Collaboration Platforms: Digital spaces where community members can share challenges, solutions, and resources.
  

Connect Data to Business Outcomes

Data initiatives must demonstrate tangible value to maintain organizational support:

• Establish clear ROI metrics for major data projects.
• Celebrate and communicate successes through case studies and testimonials.
• Create feedback loops that tie data insights to operational improvements.
• Develop dashboards that track both technical metrics and business impact.
  

Foster Psychological Safety

Innovation requires a willingness to challenge assumptions and occasionally fail:

• Recognize and reward data-driven decisions, regardless of the outcome.
• Encourage transparency around limitations and uncertainties in analyses.
• Establish processes for testing hypotheses in low-risk environments.
• Create forums where assumptions can be challenged respectfully.

Healthcare's Data Fluency Imperative

The healthcare sector faces unique challenges in building data fluency. Regulatory complexity, legacy systems, fragmented data sources, and traditional hierarchical cultures can all impede progress. However, several factors make data mastery particularly crucial for healthcare organizations today:

• Value-Based Care Transition: Success in risk-bearing arrangements requires sophisticated analytics to manage populations, predict costs, and identify intervention opportunities.
• Personalized Medicine: Genomics, wearables, and remote monitoring advances generate unprecedented data volumes that must be effectively harnessed.
• Operational Efficiency: With persistent margin pressures, data-driven optimization of workflows, resource allocation, and supply chains becomes essential.
• Consumer Expectations: Patients increasingly expect personalized, transparent, and seamless experiences similar to what they receive in other industries.
• Government and Payers: Evolving relationship with government and payers in the delivery of patient care. Data, analytics and AI are essential to navigating and adapting to this ever-changing environment.

Healthcare organizations that achieve data mastery will be positioned to survive economic uncertainty and transform challenges into opportunities for innovation and growth.

Final Thoughts

The journey to data mastery isn’t just about maturing technology or expanding capabilities—it’s about cultivating a community. A community where data is not just the domain of a few but a shared language that unites an entire organization around a common purpose: to improve population health, advance research, reduce per capita cost, and enrich patient and care-team experience.

The data community is your organization’s greatest underutilized asset. When nurtured, it becomes the force multiplier that turns fragmented efforts into enterprise-wide impact. It’s the spark that ignites innovation, the glue that connects silos, and the engine that accelerates transformation.

If you're serious about unlocking the full potential of your data, invest in your community. Give it a voice, a vision, and the agency to shape the future. Build the brand. Foster the culture. Empower the people.

Because in the end, it’s not just about better data—it’s about better outcomes. And the organizations that win tomorrow are building their communities today.

Let’s get to work.