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GEN1797 Mastering Data and Infrastructure Strategy for Technical Leaders

$199.00
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The Executive Diagnostic and Governance Toolkit

Data and Infrastructure Leadership

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing deciding what to adopt, in what order, and defending that choice when the budget round asks why this and not that.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
You're expected to know what to adopt, when to build, and how to justify it — all while the data stack evolves beneath you.

The situation this is built for

Every budget cycle, you face the same pressure. Leadership demands faster insights, better data quality, and support for AI initiatives — but won't fund every tool request. You must decide what to invest in, what to retire, and how to sequence changes across pipelines, warehouses, and access layers. Without a clear assessment framework, you're left reacting to outages, firefighting data quality issues, and defending choices with incomplete evidence. The cost isn't just technical debt — it's lost credibility when your roadmap doesn't align with business outcomes.

Who this is for

A senior technical leader responsible for data infrastructure, data engineering, or platform architecture. You own the data stack end to end, from ingestion to consumption, and report to CTO or VP-level stakeholders. You lead roadmap decisions, vendor evaluations, and team priorities, often without dedicated strategy resources.

Who this is not for

This is not for data scientists, analysts, or developers using the data platform. It is not for vendors selling into data teams or consultants without platform ownership.

What you walk away with

  • Assess the current maturity of data pipelines and governance practices
  • Identify which data capabilities directly support business objectives
  • Create a prioritized, phased investment plan for infrastructure improvements
  • Document decision logic to defend budget requests and sequencing
  • Anticipate scaling constraints before they impact AI or analytics workloads

How this maps to your situation

  • Assessing current state
  • Identifying critical gaps
  • Prioritizing improvements
  • Leading execution

Before vs. after

Before
Overwhelmed by competing priorities, unclear on what to fix first, and lacking evidence to justify infrastructure investments.
After
Confident in your assessment, equipped with a prioritized roadmap, and ready to lead with clarity in budget and strategy discussions.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 1.5 hours per module, designed to be completed over 6–8 weeks with team input and reflection.

If nothing changes
Without a structured approach, you'll continue reacting to outages, struggle to justify investments, and lose influence when AI and analytics initiatives stall due to infrastructure limitations.

How this compares to the alternatives

Unlike vendor-led assessments or generic frameworks, this course gives you a neutral, repeatable method to evaluate your data platform without bias. It focuses on decisions, evidence, and leadership — not tools or marketing claims.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Understanding Your Data Ecosystem
Map all data sources, pipelines, and consumption patterns to create a living inventory of your infrastructure.
12 chapters in this module
  1. Identify all data sources feeding into the system
  2. Map data flow from ingestion to end-user reports
  3. Classify data by sensitivity and compliance requirements
  4. Document ownership for each data domain
  5. Assess frequency and reliability of data updates
  6. Inventory all data storage systems in use
  7. Track data lineage across transformation layers
  8. List all active data integration jobs
  9. Define current data retention and archiving rules
  10. Evaluate metadata management practices
  11. Identify shadow data systems outside central control
  12. Catalog tools used for data pipeline monitoring
Module 2. Defining Data Quality Standards
Establish measurable criteria for data accuracy, completeness, and timeliness across domains.
12 chapters in this module
  1. Define what data quality means per data type
  2. Set thresholds for acceptable data freshness
  3. Measure completeness of critical data fields
  4. Assess accuracy of data against source systems
  5. Track frequency of data validation failures
  6. Define alerting rules for data quality breaches
  7. Evaluate consistency of reference data
  8. Audit schema drift over time
  9. Measure duplication rates in core tables
  10. Assess reliability of automated data checks
  11. Document resolution process for data defects
  12. Benchmark data quality across business units
Module 3. Assessing Pipeline Reliability
Evaluate the stability, monitoring, and recovery mechanisms of your data workflows.
12 chapters in this module
  1. Map dependencies between pipeline components
  2. Measure failure rate of daily ETL jobs
  3. Assess alert response time for pipeline breaks
  4. Evaluate retry mechanisms for failed jobs
  5. Track mean time to repair pipeline outages
  6. Identify single points of failure in workflows
  7. Review logging practices for data jobs
  8. Assess scalability under peak load
  9. Evaluate impact of source system changes
  10. Test disaster recovery procedures
  11. Monitor resource utilization across pipelines
  12. Document pipeline ownership and escalation paths
Module 4. Evaluating Data Storage Architecture
Analyze your current data warehouse, lake, and tiered storage approach for performance and cost efficiency.
12 chapters in this module
  1. Classify data by access frequency and value
  2. Map storage tiers to data lifecycle stages
  3. Evaluate query performance on large tables
  4. Assess indexing strategies for common queries
  5. Measure storage cost per terabyte per month
  6. Review data partitioning and clustering rules
  7. Evaluate cold storage retrieval times
  8. Assess data compression effectiveness
  9. Track growth rate of data volumes
  10. Measure impact of schema changes on storage
  11. Evaluate suitability for AI and ML workloads
  12. Benchmark query concurrency limits
Module 5. Governance and Access Control
Establish clear policies for data access, roles, and auditability across teams.
12 chapters in this module
  1. Define data classification levels
  2. Map roles to data access permissions
  3. Audit active access grants quarterly
  4. Assess compliance with data privacy laws
  5. Review encryption practices at rest and in transit
  6. Evaluate anonymization for sensitive datasets
  7. Track data access request turnaround time
  8. Document data stewardship responsibilities
  9. Measure adoption of access request workflows
  10. Assess audit logging completeness
  11. Evaluate approval workflows for privileged access
  12. Monitor for unauthorized data exports
Module 6. Prioritizing Technical Debt
Identify and rank infrastructure weaknesses that increase risk or reduce velocity.
12 chapters in this module
  1. Catalog undocumented data pipelines
  2. Identify hard-coded values in transformations
  3. Assess version control coverage for data jobs
  4. Evaluate testing coverage for data logic
  5. List pipelines without monitoring alerts
  6. Measure time to onboard new data sources
  7. Track frequency of manual data fixes
  8. Assess dependency on deprecated systems
  9. Evaluate documentation completeness
  10. Identify lack of idempotency in jobs
  11. Measure pipeline rework due to poor design
  12. Benchmark technical debt against team capacity
Module 7. Planning for Scalability
Project future data volume, query load, and team needs to guide infrastructure investments.
12 chapters in this module
  1. Forecast data growth over 12 months
  2. Estimate increase in analytics users
  3. Project AI and ML data consumption
  4. Assess current system headroom
  5. Evaluate auto-scaling capabilities
  6. Plan for increased data source integrations
  7. Model impact of real-time data adoption
  8. Assess team bandwidth for new pipelines
  9. Estimate compute cost under growth
  10. Evaluate network bandwidth constraints
  11. Plan for cross-region data replication
  12. Benchmark performance under simulated load
Module 8. Aligning with Business Goals
Connect data infrastructure capabilities to key business initiatives and outcomes.
12 chapters in this module
  1. Map data products to revenue streams
  2. Identify data dependencies for product launches
  3. Assess data support for customer experience
  4. Evaluate data readiness for new markets
  5. Track data usage in executive reporting
  6. Align data roadmap with strategic goals
  7. Measure time to deliver new reports
  8. Assess data access for operational teams
  9. Evaluate self-service adoption rates
  10. Document business impact of data outages
  11. Benchmark data latency against SLAs
  12. Link data investments to OKRs
Module 9. Building a Defensible Roadmap
Create a prioritized, evidence-based plan for infrastructure improvements.
12 chapters in this module
  1. Define criteria for initiative selection
  2. Assess effort versus business impact
  3. Evaluate risk reduction potential
  4. Prioritize based on data quality gaps
  5. Sequence initiatives by dependency
  6. Align timeline with budget cycles
  7. Document assumptions behind each project
  8. Estimate resource requirements
  9. Build business case for key projects
  10. Define success metrics for each phase
  11. Map roadmap to team skill development
  12. Present plan to executive stakeholders
Module 10. Measuring Platform Performance
Establish KPIs that reflect reliability, efficiency, and business value of your data platform.
12 chapters in this module
  1. Define uptime targets for critical pipelines
  2. Measure average data freshness by domain
  3. Track query success rate and errors
  4. Assess data platform cost per active user
  5. Evaluate cost per terabyte processed
  6. Monitor end-user satisfaction with data
  7. Measure time to resolve data incidents
  8. Track adoption of new data features
  9. Benchmark platform efficiency annually
  10. Assess reliability of automated alerts
  11. Evaluate team velocity on new requests
  12. Measure reduction in manual data work
Module 11. Managing Team Structure and Skills
Assess team composition, roles, and capability gaps to support infrastructure goals.
12 chapters in this module
  1. Map roles to data platform responsibilities
  2. Assess skill coverage for core technologies
  3. Evaluate cross-training opportunities
  4. Measure team workload distribution
  5. Track time spent on incident response
  6. Assess documentation ownership
  7. Review on-call rotation effectiveness
  8. Evaluate mentorship within the team
  9. Identify gaps in data security knowledge
  10. Benchmark team velocity against backlog
  11. Plan for skill development in AI/ML
  12. Align team structure with roadmap
Module 12. Leading Through Change
Drive adoption of new practices, tools, and governance with structured change management.
12 chapters in this module
  1. Communicate vision for data platform evolution
  2. Engage stakeholders in design decisions
  3. Run pilot projects for new approaches
  4. Document change management process
  5. Measure adoption of new data standards
  6. Address resistance from business teams
  7. Train users on new data tools
  8. Celebrate early wins and milestones
  9. Update playbooks after major changes
  10. Incorporate feedback into roadmap
  11. Assess cultural readiness for governance
  12. Sustain improvements through routines

Frequently asked

Who is this course designed for?
It is for technical leaders who own the data platform end to end and must make strategic decisions about architecture, investment, and team direction.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Do I need prior experience with specific tools?
No. The course focuses on principles, decisions, and evidence, not tool-specific implementation.
Will this help me defend my budget choices?
Yes. You will build a documented, evidence-based rationale for what to invest in, retire, or delay.
Can I use this with my team?
Yes. The templates and playbook are designed to facilitate team alignment and collective ownership.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 1.5 hours per module, designed to be completed over 6–8 weeks with team input and reflection..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
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