Why Data-as-a-Product Management Matters for DRL Progression
Traditional data management treats data as a technical by-product, leaving organisations stuck at lower DRL levels (1-4) where data quality issues prevent reliable analytics. The Data-as-a-Product approach proactively designs data with analytical consumption in mind, enabling progression through DRL levels toward full AI readiness.
The Data Readiness Level (DRL) Journey:
DRL Levels 1-3 (Foundation): Data exists but is inconsistent, inaccessible, or poorly documented. Analytics possible but unreliable. Data managed as operational by-product.
DRL Levels 4-5 (Capability): Data becoming more consistent and accessible. Diagnostic analytics feasible. Beginning to think about data quality, but still largely reactive.
DRL Levels 6-7 (Maturity): Data fully prepared for advanced analytics and AI. Systematic quality management. Data-as-a-product thinking embedded. Predictive and prescriptive analytics delivering ROI.
Connection to ROI:
Reduced time-to-insight: Well-managed data products eliminate data preparation bottlenecks that typically consume 60-80% of analytics project time, enabling faster progression through DRL levels
Higher model accuracy: Proactive data quality management ensures AI models train on reliable data, improving prediction accuracy by 20-40%, only achievable at higher DRL levels
Scalable analytics: Product thinking enables reusable data assets that serve multiple use cases, multiplying ROI across the organisation, which is characteristic of DRL 7 maturity
Risk mitigation: Governance built into data products prevents compliance failures and biased AI outcomes that damage reputation and incur regulatory penalties
The DPM responsibility frameworks provide the systematic toolkit for achieving these outcomes by methodically addressing the root conditions that prevent organisations from progressing through DRL levels toward the Predictive People Analytics and AI-ready maturity that DRL 7 represents.
Data Consumer Responsibilities Framework: A Systematic Toolkit for Analytics Enablement
Who Are Data Consumers?
Data Consumers are the people analytics teams, HR analysts, data scientists, and business leaders who need reliable data to generate insights, build predictive models, and make informed talent decisions. They rely on data being accessible, consistent, trustworthy, and fit-for-purpose. Their needs evolve as the organisation progresses through DRL levels.
Understanding the Data Consumer Framework as a Systematic Toolkit
The Data Consumer responsibility framework serves as a systematic diagnostic and progression toolkit that enables the DPM to assess where consumers currently struggle (indicating current DRL level) and what must be addressed to enable progression toward DRL 7. This structured approach ensures that consumer needs are met systematically rather than through ad hoc firefighting.
The framework provides:
Current state assessment: Identifying red flags that indicate by-product thinking and lower DRL levels
Target state definition: Describing product thinking characteristics of DRL 7 maturity
Gap analysis: Understanding what must change to enable DRL progression
Action pathways: Specific steps consumers can take to support maturity advancement
The framework covers critical areas including:
Accessing consistent data across multiple sources
Working with appropriate data volumes and performance
Balancing security requirements with analytical needs
Handling complex multi-format data
Understanding coding and classification systems
Integrating data from fragmented systems
From By-Product to Product Thinking: The DRL Progression:
The Consumer framework illustrates the systematic progression from lower DRL levels to DRL 7:
By-Product Approach (DRL 1-3):
Consumers spend 60-80% of project time finding, cleaning, and reconciling data
Each analytics project requires custom data integration and quality remediation
Cannot trust analytical outputs due to known data inconsistencies
Self-service analytics is impossible, as there is a requirement for specialist data engineering for each analysis
Predictive People Analytics is unreliable due to data quality issues
AI initiatives fail during pilot stage due to inadequate data foundations
Product Approach (DRL 7):
Consumers access curated, documented, quality-assured data products specifically designed for analytical consumption
Spend 70-80% of time on actual analysis and insight generation
Data products come with clear definitions, lineage, quality metrics, and support
Self-service analytics enabled for business users
Predictive models train on reliable data, delivering accurate, trustworthy results
AI deployment successful at enterprise scale, delivering transformational ROI
Impact on Predictive People Analytics:
When data operates as a product (DRL 7) rather than a by-product (DRL 1-3), consumers can build sophisticated predictive models with confidence. Flight risk predictions, succession planning algorithms, skills matching, and workforce planning models all depend on consistent, comprehensive, timely data. The Consumer framework ensures analytics teams have what they need to deliver these capabilities at the quality level required for business impact.
Impact on AI Enablement:
AI model development requires extensive experimentation and iteration. Lower DRL levels mean data scientists waste time on data preparation, causing AI initiatives to stall. DRL 7 maturity provides reliable training data, clear documentation, and consistent pipelines, enabling data scientists to focus on model innovation and achieving the AI-driven competitive advantage the organisation invested in.
Using the Data Consumer Framework: Systematic DRL Progression
The DPM uses this framework toolkit to:
Assess current state: Survey consumers to identify which red flags are present, indicating current DRL level
Prioritise gaps: Determine which consumer challenges most significantly constrain DRL progression
Design solutions: Implement data products that address identified gaps systematically
Measure progress: Track improvements in consumer satisfaction, time-to-insight, and self-service adoption
Sustain maturity: Continuously gather consumer feedback to maintain DRL 7 standards
Data Consumer Actions for Supporting DRL Progression:
Document data quality issues encountered, helping the DPM identify systemic problems
Request single sources of truth for critical data elements
Ask for automated data integration and pipelines
Suggest performance improvements based on usage patterns
Request appropriate access levels for analytical needs
Identify requirements for better search and integration capabilities
Provide feedback on data product quality to inform continuous improvement
Data Manufacturer Responsibilities Framework: A Systematic Toolkit for Infrastructure Excellence
Who Are Data Manufacturers?
Data Manufacturers are the IT professionals, data engineers, platform architects, and infrastructure specialists who build and maintain the technical systems that store, process, and deliver data. They are responsible for ensuring data infrastructure is reliable, performant, secure, and scalable. Their capabilities fundamentally determine what DRL level the organisation can achieve.
Understanding the Data Manufacturer Framework as a Systematic Toolkit
The Data Manufacturer responsibility framework serves as a systematic infrastructure assessment and development toolkit that enables the DPM to evaluate whether current technical capabilities support progression to DRL 7, and what infrastructure investments are required to enable AI-ready data maturity.
The framework provides:
Infrastructure assessment: Evaluating whether systems support analytical workloads characteristic of DRL 7
Capability gaps: Identifying technical barriers preventing DRL progression
Architecture guidance: Describing infrastructure patterns required for DRL 7 maturity
Investment priorities: Determining which technical improvements deliver maximum DRL advancement
The framework covers critical areas including:
Building adequate infrastructure and resource capacity
Architecting security and access management
Implementing scalable storage and processing
Creating enterprise integration architecture
From By-Product to Product Thinking: The DRL Progression:
The Data Manufacturer framework illustrates the systematic infrastructure evolution from lower DRL levels to DRL 7:
By-Product Approach (DRL 1-3):
Infrastructure sized and optimised solely for operational system requirements
Analytical workloads are afterthoughts that strain systems never designed for them
Integration happens through point-to-point connections built reactively as needs arise
Security focuses on system protection without considering legitimate analytical access
Performance degradation as data volumes grow, indicating that it is impossible to scale to meet analytics demands
No enterprise architecture, as each system operates independently
Product Approach (DRL 7):
Infrastructure explicitly designed for both operational and analytical workloads
Dedicated resources for data processing, machine learning, and analytics
Integration follows enterprise architecture patterns with planned, governed approaches
Security enables appropriate access whilst managing risk effectively
Performance continuously monitored and optimised for analytical consumption patterns
Scalable architecture supports growing data volumes and sophisticated AI workloads
Impact on Predictive People Analytics:
Predictive models require processing large volumes of data, running complex algorithms, and delivering insights in timeframes that support decision-making. Infrastructure designed as a by-product (DRL 1-3) cannot support these requirements, as models take days to train or cannot run at all. DRL 7 infrastructure enables real-time flight risk monitoring, enterprise workforce planning, and sophisticated scenario modelling that deliver strategic advantage.
Impact on AI Enablement:
AI model training demands substantial computational resources. Infrastructure that has not been designed for these workloads becomes the bottleneck preventing AI deployment. Manufacturers must build with AI requirements in mind to enable the organisation to reach DRL 7 and successfully deploy AI at enterprise scale.
Using the Data Manufacturer Framework: Systematic DRL Progression
The DPM uses this framework toolkit to:
Assess infrastructure: Evaluate whether current systems support DRL 7 requirements
Identify constraints: Determine which technical limitations prevent DRL progression
Design architecture: Develop infrastructure roadmap for achieving DRL 7 maturity
Prioritise investments: Focus resources on infrastructure improvements that most significantly enable DRL advancement
Monitor performance: Continuously track infrastructure metrics against DRL 7 standards
Manufacturer Actions for Supporting DRL Progression:
Monitor and continuously optimise performance for analytical workloads
Plan proactively for capacity growth based on analytical strategy and DRL targets
Implement scalable, cloud-native architectures that support DRL 7 requirements
Create risk-based security policies that enable analytics whilst managing compliance
Build automated, role-based access controls appropriate for DRL 7 maturity
Establish enterprise data architecture standards that support AI deployment
Implement modern integration platforms enabling unified data products
Build automated, monitored data synchronisation meeting DRL 7 quality standards
Data Supplier Responsibilities Framework: A Systematic Toolkit for Quality at Source
Who Are Data Suppliers?
Data Suppliers are the people who create and input data, the HR administrators entering employee records, managers completing performance reviews, employees updating their profiles, recruiters logging candidate information. The quality of data products depends fundamentally on data creation practices at source. Supplier practices determine whether organisations can progress beyond DRL 3.
Understanding the Data Supplier Framework as a Systematic Toolkit
The Data Supplier responsibility framework serves as a systematic quality management toolkit that enables the DPM to establish data creation practices that produce DRL 7 quality from the point of origin. This is exponentially more effective than attempting to remediate quality issues downstream, ensuring quality at source is the foundation of data maturity.
The framework provides:
Quality standards: Defining what DRL 7 data creation practices look like
Process design: Creating data collection workflows that naturally produce quality data
Training guidance: Ensuring all suppliers understand their role in achieving DRL 7
Validation mechanisms: Implementing checks that prevent quality issues at source
The framework covers critical areas including:
Managing data across multiple sources to establish single source of truth
Reducing subjectivity in data collection through structured protocols
Implementing data entry standards with built-in validation
Adapting collection practices to evolving analytical requirements
From By-Product to Product Thinking: The DRL Progression:
The Supplier framework illustrates the systematic quality evolution from lower DRL levels to DRL 7:
By-Product Approach (DRL 1-3):
Data creation seen as administrative burden disconnected from analytical value
Standards positioned within subjective knowledge rather than objective documentation
Data entry happens inconsistently across people and departments
Shortcuts and workarounds developed to complete required fields quickly
Quality issues discovered only when analytics fail, requiring expensive remediation
Changes happen reactively when systems break
No understanding of downstream analytical impact
Product Approach (DRL 7):
Data creation understood as foundational to organisational decision-making capability
Clear standards, comprehensive training, and intuitive processes ensure consistent quality
Validation happens at point of entry, preventing downstream issues
Suppliers understand how their data enables Predictive People Analytics and AI
Collection practices evolve proactively as analytical needs change
Continuous quality monitoring with feedback loops to suppliers
Quality at source eliminates need for expensive downstream remediation
Impact on Predictive People Analytics:
Analytics is only as good as the data it's built upon, or "garbage in, garbage out." Manager subjectivity in performance ratings creates bias in talent predictions. Incomplete skills profiles prevent accurate talent matching. Delayed performance reviews create timing biases. Lower DRL levels reflect poor supplier practices; DRL 7 requires systematic quality at source where issues are easiest and cheapest to solve.
Impact on AI Enablement:
AI models learn from the data they're trained on. Biased, inconsistent, or incomplete data creation produces biased, unreliable AI systems that cannot be deployed. Quality at source is not optional for AI, it's foundational to achieving DRL 7. The Data Supplier framework ensures AI training data is representative, consistent, and fit-for-purpose, enabling ethical, accurate, trustworthy AI deployment that delivers ROI.
Using the Data Supplier Framework: Systematic DRL Progression
The DPM uses this framework toolkit to:
Assess supplier practices: Evaluate current data creation quality against DRL 7 standards
Identify quality gaps: Determine which supplier issues most significantly constrain DRL progression
Design processes: Create intuitive data collection workflows that naturally produce DRL 7 quality
Implement training: Ensure all suppliers understand standards and their importance
Monitor quality: Track data creation quality metrics and provide continuous feedback
Supplier Actions for Supporting DRL Progression:
Establish and communicate single sources of truth for each data element
Implement data lineage tracking to maintain data integrity
Develop detailed, accessible data collection standards
Implement comprehensive training programmes for all data creators
Establish systematic quality monitoring with feedback loops
Create intuitive, standardised data entry templates with validation
Implement real-time data validation preventing errors at source
Establish regular requirement review cycles to evolve with analytical needs
Create active stakeholder feedback loops to continuously improve
The Data-as-a-Product Manager: Orchestrating Systematic DRL 7 Progression
The DPM's Strategic Role: Using the DPM Frameworks as Systematic Toolkits
The Data-as-a-Product Manager doesn't work within just one perspective, they orchestrate across all three using the responsibility frameworks as systematic toolkits for achieving DRL 7 maturity. Rather than addressing data quality issues reactively or in isolation, the DPM uses these frameworks to:
Systematically diagnose current DRL levels:
Apply Data Consumer framework to assess analytics team challenges
Apply Data Manufacturer framework to evaluate infrastructure capabilities
Apply Data Supplier framework to examine data creation quality
Synthesise findings to determine overall organisational DRL level
Identify barriers preventing DRL progression:
Use frameworks to pinpoint which root conditions constrain advancement
Determine whether barriers are consumer-facing, infrastructure-related, or supplier-driven
Prioritise issues that most significantly prevent progression to next DRL level
Create comprehensive view of data maturity gaps
Design systematic solutions:
Use frameworks to design solutions addressing root causes across all stakeholders
Ensure consumer needs, manufacturer capabilities, and supplier practices align
Implement changes systematically rather than through one-off fixes
Build sustainable processes that maintain DRL 7 maturity once achieved
Measure and sustain progress:
Track improvements using framework metrics across all three perspectives
Ensure solutions deliver intended DRL progression
Maintain DRL 7 standards through continuous monitoring and improvement
Prevent regression by sustaining product thinking across organisation
The Three Frameworks Working in Together for DRL 7
The frameworks function as an integrated toolkit system:
Data Consumer Framework defines what DRL 7 looks like from the analytics perspective:
Self-service access to quality-assured data products
Fast, reliable insights enabling confident decision-making
Predictive models trained on consistent, comprehensive data
AI deployment delivering transformational business impact
Data Manufacturer Framework defines the infrastructure required for DRL 7:
Scalable architecture supporting analytical workloads at scale
Secure, governed access enabling appropriate data use
Integrated systems providing unified employee lifecycle views
Performance enabling real-time analytics and AI applications
Data Supplier Framework defines the quality standards necessary for DRL 7:
Consistent data creation following documented standards
Quality validated at source, preventing downstream issues
Single sources of truth eliminating conflicts
Adaptive practices evolving with analytical requirements
Together, these frameworks enable the DPM to systematically progress organisations through to DRL 7 and Data-as-a-Product
The Thinking Shift: Systematic Progression to DRL 7
The DPM responsibility frameworks collectively guide organisations through the fundamental thinking shift required to achieve DRL 7:
By-Product Thinking (DRL 1-3) Characteristics:
Data viewed as operational system exhaust, not strategic asset
Reactive problem-solving when analytics fail
Siloed ownership with no end-to-end accountability
Infrastructure designed for operations only
Quality addressed downstream through cleaning and remediation
Consumers expected to work around data limitations
Changes made reactively when systems break
Product Thinking (DRL 7) Characteristics:
Data viewed as strategic asset with defined users and value
Proactive design for analytical consumption
Clear product ownership with end-to-end accountability via DPM
Infrastructure explicitly designed for analytical workloads and AI
Quality built in at source through standards and validation
Consumers treated as customers with defined service levels
Continuous improvement based on feedback and metrics
This thinking shift doesn't happen overnight, it requires systematic work across all three perspectives, guided by the DPM using the frameworks as structured toolkits, to transform how the organisation creates, manages, and consumes data.