1. Data Strategy and Audit
Purpose: Understand your current data landscape by mapping sources, identifying duplication, and reviewing spreadsheet dependencies.
- Clarifies: Data ownership, quality questions, and structural priorities.
- Starting Inputs: Current system lists, primary reporting spreadsheets.
- Check First: Where is manual data entry currently causing the most friction?
- Practical Note: A data audit provides visibility; it requires internal commitment to address discovered quality issues.
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2. Business Intelligence
Purpose: Plan dashboards that support actual operational decision-making, rather than displaying vanity metrics.
- Clarifies: KPI definitions, reporting cadence, audience requirements, and access controls.
- Starting Inputs: Existing reports, management meeting agendas.
- Check First: Do the metrics currently reported actually influence business actions?
- Practical Note: Dashboards are only as reliable as the underlying source data and definitions.
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3. AI Readiness
Purpose: Evaluate business use cases for artificial intelligence to ensure ideas are feasible, safe, and aligned with operations.
- Clarifies: Business problem definition, data suitability, human oversight requirements, and pilot planning.
- Starting Inputs: Identified bottlenecks or desired use cases.
- Check First: Are we clear on what business problem we are trying to solve?
- Practical Note: AI integration requires appropriate human oversight and does not replace professional judgement.
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4. Automation Workflows
Purpose: Map manual reporting processes to identify where ETL (Extract, Transform, Load) and workflow automation may save time.
- Clarifies: Workflow triggers, spreadsheet consolidation steps, handoffs, and exception handling.
- Starting Inputs: Documentation of current manual reporting routines.
- Check First: Is the manual process consistent enough to be mapped?
- Practical Note: Automating a flawed process simply produces errors faster. Processes must be stable first.
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5. Data Governance Basics
Purpose: Establish foundational rules for how data is managed, protected, and accessed across your organisation.
- Clarifies: Ownership, data definitions, access control matrices, and documentation standards.
- Starting Inputs: Current user roles, sensitive data locations.
- Check First: Who currently authorises access to operational information?
- Practical Note: This provides operational guidance, but does not constitute legal or regulatory compliance certification.
6. LLM / RAG Planning for Internal Knowledge
Purpose: Plan how internal documents could safely support Large Language Models (LLMs) via Retrieval-Augmented Generation (RAG).
- Clarifies: Internal knowledge discovery, document preparation, retrieval concepts, and source attribution.
- Starting Inputs: Policy documents, knowledge base articles, training materials.
- Check First: Are the source documents accurate and up-to-date?
- Practical Note: LLMs can generate incorrect information. Systems must include human review and explicit source attribution.