Financial Data Quality and Reconciliation : A Practical Guide to Finance Data Pipelines, Validation Controls, and Error Detection
Language: English
Published by Amazon Digital Services LLC - Kdp Mai 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
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Add to basketItem description from seller
Neuware - Reactive PublishingFinancial reporting depends on data that is complete, accurate, consistent, and traceable. Yet many finance teams still rely on fragmented spreadsheets, manual reconciliations, inconsistent source systems, and ad hoc validation processes that make errors difficult to detect before they reach reports, forecasts, dashboards, or management decisions.Financial Data Quality and Reconciliation provides a practical framework for improving finance data reliability across the full reporting workflow. Written for finance professionals, analysts, controllers, FP&A teams, accounting teams, and data practitioners working with financial information, this book explains how to design stronger data pipelines, validation controls, reconciliation processes, and error detection methods.Inside, readers will learn how to identify common finance data quality issues, structure source-to-report workflows, create validation checks, compare balances across systems, investigate discrepancies, document reconciliation logic, and build repeatable processes that support more dependable financial analysis.Topics include: Data quality principles for finance and accounting workflowsCommon sources of financial data errorsFinance data pipelines and reporting architectureValidation controls for completeness, accuracy, and consistencyReconciliation models for comparing systems, accounts, and reportsException handling and discrepancy investigationError detection methods for financial datasetsDocumentation, auditability, and process governancePractical approaches for reducing manual review and improving reporting confidenceRather than treating reconciliation as a last-minute cleanup task, this book presents financial data quality as a structured operating discipline. It shows how finance teams can move from reactive error correction toward proactive validation, stronger controls, and more transparent reporting workflows.Financial Data Quality and Reconciliation is designed for professionals who want to strengthen the foundation of financial reporting, improve analytical confidence, and build finance data processes that are easier to review, maintain, and trust.…
Seller Inventory # 9798197273970
- Title
- Financial Data Quality and Reconciliation : A Practical Guide to Finance Data Pipelines, Validation Controls, and Error Detection
- Author
- Graham Wexler
- Publisher
- Amazon Digital Services LLC - Kdp Mai 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798197273970
- Item weight
- 463 grams
- Dimensions
- 229x152x24 mm
Financial reporting depends on data that is complete, accurate, consistent, and traceable. Yet many finance teams still rely on fragmented spreadsheets, manual reconciliations, inconsistent source systems, and ad hoc validation processes that make errors difficult to detect before they reach reports, forecasts, dashboards, or management decisions.
Financial Data Quality and Reconciliation provides a practical framework for improving finance data reliability across the full reporting workflow. Written for finance professionals, analysts, controllers, FP&A teams, accounting teams, and data practitioners working with financial information, this book explains how to design stronger data pipelines, validation controls, reconciliation processes, and error detection methods.
Inside, readers will learn how to identify common finance data quality issues, structure source-to-report workflows, create validation checks, compare balances across systems, investigate discrepancies, document reconciliation logic, and build repeatable processes that support more dependable financial analysis.
Topics include:
Data quality principles for finance and accounting workflows
Common sources of financial data errors
Finance data pipelines and reporting architecture
Validation controls for completeness, accuracy, and consistency
Reconciliation models for comparing systems, accounts, and reports
Exception handling and discrepancy investigation
Error detection methods for financial datasets
Documentation, auditability, and process governance
Practical approaches for reducing manual review and improving reporting confidence
Rather than treating reconciliation as a last-minute cleanup task, this book presents financial data quality as a structured operating discipline. It shows how finance teams can move from reactive error correction toward proactive validation, stronger controls, and more transparent reporting workflows.
Financial Data Quality and Reconciliation is designed for professionals who want to strengthen the foundation of financial reporting, improve analytical confidence, and build finance data processes that are easier to review, maintain, and trust.
"Synopsis" may belong to another edition of this title.
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