What We'll Achieve Together
Session Objectives
During this workshop, we will collaboratively identify high-value Finance data products that serve multiple stakeholders across your organisation. We'll document how data flows through your teams today, surface pain points in data access and quality, and establish a clear governance structure.
Most importantly, we'll map accountability — ensuring every critical data asset has named owners, stewards, and curators who understand their responsibilities within the Purview catalogue.
Your Pre-Workshop Activity
Document Your Structure
Map your Finance department hierarchy, including all sub-functions and reporting lines.
Catalogue Your Data
List all source systems, data types, and access patterns used by your teams.
Define Your Activities
Capture the core business processes and responsibilities within each function.
Surface Common Requests
Identify frequent data queries that point to high-demand information assets.
Prioritise Critical Data
Highlight must-have data for reporting, compliance, and executive decision-making.
Review Your Glossary
Share existing definitions and terminology, or flag where these need development.
Assign Responsibilities
Identify owners, stewards, curators, and consumers for each major data domain.
Identify Your Finance Department Hierarchy
What We Need
Please outline the complete structure of your Finance organisation. This foundational information enables us to map operational domains to potential data products effectively.
Key Elements to Document
- Sub-functions: FP&A, Financial Accounting, Treasury, Tax, Actuarial, Reporting, Regulatory Compliance
- Reporting lines: Direct and dotted-line relationships between teams
- Functional areas: Teams involved in consuming or producing data assets
List the Data Sources Used by Finance
Source System Inventory
For each Finance team, please provide comprehensive details about the systems and data they interact with. This inventory reveals dependencies, integration points, and potential consolidation opportunities.
Information Required
- Source systems: SAP, Oracle, TM1, BW, Anaplan, IFRS systems, actuarial models, external data feeds
- Data types: Transactions, forecasts, general ledger balances, consolidations, reconciliations, regulatory filings
- Access patterns: Frequency (real-time, daily, monthly), method (API, file transfer, direct query), and volume
Why This Matters
Understanding your data landscape helps us identify redundant sources, data quality bottlenecks, and opportunities to create unified data products that serve multiple use cases. This reduces duplication and improves consistency across Finance.
Describe Each Function's Key Business Activities
Month-End Close
Period closure, journal entries, account reconciliations, variance analysis
Regulatory Reporting
Statutory filings, prudential returns, supervisory data submissions
Budgeting & Forecasting
Annual planning, rolling forecasts, scenario modelling, performance tracking
Investment & Treasury
Cash management, liquidity planning, investment accounting, hedging activities
Cost Allocation
Expense apportionment, transfer pricing, shared services allocation
Risk & Actuarial Modelling
Reserve calculations, capital adequacy, stress testing, loss projections
Please capture the core responsibilities and workflows within your function. These business processes rely heavily on high-quality data and often represent the strongest candidates for data product development.
List Common Queries and Data Requests
High-Demand Data Signals
The questions your team fields repeatedly often point directly to high-value data that should be productised. These requests reveal what stakeholders truly need and highlight gaps in current data accessibility.
Typical Request Patterns
"Can I get the latest P&L by entity, country, and line of business?"
"Can I get the latest P&L by entity, country, and line of business?"
"What are the variances between forecast and actual results?"
"What are the variances between forecast and actual results?"
"Can you provide exposure data for our regulatory filings?"
"Can you provide exposure data for our regulatory filings?"
"What is the definitive source of truth for entity mappings and hierarchies?"
"What is the definitive source of truth for entity mappings and hierarchies?"
What to Document
- Frequent ad-hoc requests your team receives
- Recurring reports that stakeholders request manually
- Questions about data definitions or lineage
- Requests for historical trend analysis
- Cross-functional data needs
Identify Critical or "Must-Have" Data
External Reporting
Annual reports, quarterly filings, statutory accounts required by regulators and shareholders
Audit & Compliance
Data supporting internal controls, external audit requirements, regulatory examinations
Executive Reporting
Board packs, management information, KPIs for strategic decision-making
Sensitive Data
Confidential financial information, market-sensitive data, personal data requiring protection
High-Demand Assets
Data frequently queried across multiple teams, supporting cross-functional analysis
Please outline data that falls into these categories. Prioritising critical data ensures we develop the most impactful data products first, focusing on assets that drive compliance, governance, and business value.
Confirm Your Common Glossary or Dictionary
Establishing a Shared Language
A common glossary is essential for data governance success. It ensures everyone interprets financial metrics and terminology consistently, reducing misunderstandings and improving data quality.
What to Share (If Available)
- Financial KPI definitions: Revenue, EBITDA, Net Income, Return on Equity
- Common terminology: Exposure, Gross Written Premium, Expense Ratio, Combined Ratio
- Calculation logic: Documented formulae for key metrics and ratios
- Entity hierarchies: Rules for consolidation, legal entity structures, reporting units
If No Glossary Exists
Please indicate this clearly. We'll use the workshop to capture critical definitions collaboratively, establishing a foundation for your data governance programme going forward.
Identify Responsibilities for Each Domain
Clear accountability is the cornerstone of effective data governance. For each major data area, please identify the individuals or teams fulfilling these crucial roles:
Data Owner
Accountability: Data correctness, approval authority, strategic decisions
Provide: Name and function
Data Steward
Accountability: Data quality, definitions, lineage documentation, issue resolution
Provide: Name and function
Data Curator
Accountability: Creating and maintaining metadata within Purview catalogue
Provide: Name or team
Data Consumer Groups
Accountability: Using data appropriately, providing feedback on quality
Provide: List of functions
Your Pre-Workshop Checklist
- Department hierarchy documented
- Data sources list completed
- Finance function activities described
- Common data queries listed
- Critical and high-value data identified
- Glossary or definitions shared (or confirmed none exists)
- Owners, stewards, curators, and consumers identified
Completing this checklist ensures a productive workshop session where we can focus on strategic decisions rather than gathering basic information. Thank you for your thorough preparation.