Understanding the 10 Root Conditions: Data Maturity Foundations

Overview

The 10 Root Conditions framework represents the academic backbone for assessing your organisation's current data maturity and identifying specific areas for improvement. Based on research by Lee and Pipino, these conditions provide a systematic lens for understanding data quality challenges across people analytics and workforce intelligence systems.

Rather than presenting generic problems, each root condition breaks down complex data challenges into specific pain points, enabling targeted interventions and measurable progress towards data maturity.

What Are the Root Conditions?

The framework examines ten fundamental conditions that frequently appear in organisational contexts:

  1. Multiple Data Sources - Conflicting information across systems

  2. Subjective Judgement - Human interpretation affecting data consistency

  3. Resource Limitations - Infrastructure constraints impacting data access

  4. Security vs Accessibility - Balancing protection with analytical needs

  5. Diverse Coding Systems - Incompatible classification schemes across functions

  6. Complex Data Representation - Unstructured qualitative insights trapped in text

  7. Data Volume and Processing - Relationships between information quantity and capability

  8. Data Input Standards - Variable recording practices affecting reliability

  9. Evolving Information Requirements - Misalignment between historical collection and current needs

  10. System Integration - Architectural challenges in distributed information systems

Why These Conditions Matter

For Business Leaders and Organisations

The root conditions provide a diagnostic framework to:

  • Assess current state: Identify which specific conditions are limiting your data maturity

  • Prioritise investments: Focus resources on addressing the conditions creating the greatest pain points

  • Measure progress: Track improvement across specific, well-defined dimensions rather than vague "data quality" goals

  • Build competitive advantage: Systematically advance through Data Readiness Levels (DRL) towards DRL 7 and beyond

  • Enable Predictive People Analytics and AI readiness: Address foundational data issues that prevent effective Predictive People Analytics and AI implementation

  • Drive strategic decisions: Access unified people intelligence for workforce planning, succession, organisational design and AI enablement

  • Reduce operational costs: Eliminate inefficiencies caused by fragmented data and manual processes

  • Manage risk: Ensure compliance and identify potential legal exposures through systematic data quality

  • Enhance fairness: Detect and prevent bias in hiring, promotion, and compensation decisions

For Academics and Researchers

The framework enables:

  • Structured research design: Target specific root conditions with real organisational use cases

  • Comparative studies: Examine how different organisations address the same condition

  • Intervention effectiveness: Measure the impact of specific solutions against well-defined problems

  • Theory development: Build upon a shared conceptual foundation for data maturity research

  • Cross-sector insights: Apply consistent assessment criteria across industries

  • Longitudinal studies: Track organisational progression through data maturity levels over time

  • Evidence-based practice: Connect academic research directly to practical implementation challenges

  • Publication opportunities: Contribute to case study development and academic literature

  • Collaboration networks: Engage with practitioners implementing frameworks in real-world contexts

For Developers and Technical Teams

Understanding root conditions helps developers:

  • Design targeted solutions: Build tools that address specific, well-defined data challenges

  • Prioritise features: Focus development efforts on capabilities that resolve critical root conditions

  • Integrate effectively: Understand where systems need to connect to address integration-related conditions

  • Optimise architecture: Design systems that support progression through data maturity levels

  • Enable scalability: Build infrastructure that grows with organisational expansion

  • Support real-time analytics: Create in-house architecture designed for Predictive People Analytics and AI enablement.

For Policy Makers and Regulators

The framework supports policy development by:

  • Establishing standards: Create data governance policies aligned with recognised maturity conditions

  • Defining compliance: Set clear expectations around data quality dimensions

  • Assessing readiness: Evaluate organisational capability using consistent criteria

  • Guiding investment: Direct funding towards addressing systemic data infrastructure gaps

  • Enabling benchmarking: Compare data maturity across organisations and sectors

  • Protecting individuals: Ensure privacy-preserving approaches to people analytics

  • Promoting equity: Establish requirements for bias detection and fair decision-making systems

  • Facilitating transparency: Require systematic documentation of data quality and AI readiness

For Technology Vendors

Vendors can leverage this framework to:

  • Classify offerings: Clearly articulate which root conditions your solution addresses

  • Differentiate products: Position solutions based on the specific data maturity challenges they resolve

  • Map to DRL progression: Demonstrate how your technology helps organisations advance towards DRL 7

  • Bundle strategically: Combine capabilities that address complementary root conditions

  • Measure customer success: Track improvements in specific conditions rather than vague "satisfaction" metrics

  • Validate effectiveness: Provide evidence of impact against recognised data maturity dimensions

  • Partner strategically: Identify complementary vendors addressing different root conditions

Example: A vendor might position their platform as addressing Root Conditions 1, 5, and 10 (Multiple Data Sources, Diverse Coding Systems, and System Integration), enabling organisations to progress from DRL 3 to DRL 7 through unified people intelligence architecture.

The Data-as-a-Product Approach

For each root condition, the framework presents a strategic "Data-as-a-Product" use case that transforms the challenge into a competitive advantage. This approach emphasises:

  • Strategic intelligence over operational data: Treating people data as curated intelligence assets rather than administrative by-products

  • Predictive capability: Building systems designed for forecasting, proactive decision-making and seamless AI enablement

  • Unified architecture: Creating integrated platforms rather than disconnected point solutions

  • Continuous improvement: Implementing frameworks that evolve with organisational needs

Understanding Pain Points

Each root condition is accompanied by comprehensive pain points that illustrate real-world impacts:

  • Operational inefficiencies: How the condition slows down processes and increases costs

  • Strategic blind spots: Critical insights that remain hidden due to data limitations

  • Risk and compliance issues: Potential legal or regulatory exposures

  • Competitive disadvantages: Opportunities missed due to inadequate data capability

  • AI readiness barriers: Specific obstacles preventing effective AI implementation

Implementation Benefits

The framework outlines key advantages organisations gain by addressing each condition, for instance:

  • Predictive retention: Identify flight risk 6+ months in advance

  • Quality hiring: Connect recruitment sources with long-term performance

  • Fair progression: Ensure equitable career advancement through objective data

  • Optimal teams: Form project teams based on complementary skills and proven collaboration patterns

  • Strategic succession: Build leadership pipelines using complete and standardised capability assessments

  • Proactive equity: Detect and prevent compensation bias systematically

Getting Started

Whether you're a business leader, researcher, developer, policy maker, or technology vendor, the 10 Root Conditions framework provides:

  1. Common language: Shared terminology for discussing data maturity challenges

  2. Diagnostic tool: Systematic method for evaluating current state

  3. Roadmap framework: Clear pathway for progressing through data maturity levels

  4. Collaboration opportunities: Structured approach for working with researchers and other implementers

  5. Benchmarking criteria: Consistent standards for measuring progress