Barber | Income Statement Semantic Models | Buch | 979-8-8688-0329-1 | www.sack.de

Buch, Englisch, 433 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 703 g

Barber

Income Statement Semantic Models

Building Enterprise-Grade Income Statement Models with Power BI
1. Auflage 2024
ISBN: 979-8-8688-0329-1
Verlag: Apress

Building Enterprise-Grade Income Statement Models with Power BI

Buch, Englisch, 433 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 703 g

ISBN: 979-8-8688-0329-1
Verlag: Apress


This comprehensive guide will teach you how to build an income statement semantic model, also known as the profit and loss (P&L) statement.

Author Chris Barber— a business intelligence (BI) consultant, Microsoft MVP, and chartered accountant (ACMA, CGMA)—helps you master everything from designing conceptual models to building semantic models based on these designs. You will learn how to build a re-usable solution based on the trial balance and how to expand upon this to build enterprise-grade solutions. If you want to leverage the Microsoft BI platform to understand profit within your organization, this is the resource you need.

What You Will Learn

  • Modeling and the income statement: Learn what modelling the income statement entails, why it is important, and how income statements are constructed
  • Calculating account balances: Learn how to optimally calculate account balances using a Star Schema
  • Producing external income statement semantic models: Learn how to produce external income statement semantic models as they enable income statements to be analyzed from a range of perspectives and can be explored to reveal the underlying accounts and journal entries
  • Producing internal income statement semantic models: Learn how to create multiple income statement layouts and further contextualize financial information by including percentages and non-financial information, and learn about the various security and self-service considerations

Who This Book Is For

Technical users (solution architects, Microsoft Fabric developers, Power BI developers) who require a comprehensive methodology for income statement semantic models because of the modeling complexities and knowledge needed of the accounting process; and finance (management accountants) who have hit the limits of Excel and have started using Power BI, but are unsure how income statement semantic models are built

Barber Income Statement Semantic Models jetzt bestellen!

Zielgruppe


Professional/practitioner


Autoren/Hrsg.


Weitere Infos & Material


Part I: Modelling and the Income Statement.-Chapter 1: What is an income statement semantic model?.-Chapter 2: How to Construct an Income Statement.- Chapter 3: Building a Reusable Solution.- Chapter 4: Why model the income statement?.- Part II: Calculating Account Balances Chapter 5: Conceptual Account Balance Models.- Chapter 6: Logical Account Balance Models.- Chapter 7: The Trial Balance Semantic Model.- Chapter 8: A Journal Entry Semantic Model.- Part III: Producing External Income Statement Semantic ModelsChapter 9: The Four Subtotal and Subset Types.- Chapter 10: External Reporting Logical Models.- Chapter 11: External Reporting Semantic Models.- Part IV: Producing Internal Income Statement Sematic ModelsChapter 12: Internal Reporting Logical Models.- Chapter 13: Internal Reporting Semantic Models.- Chapter 14: Security and Self-service Considerations.- Chapter 15: Review of the Sixteen Challenges.


Chris Barber is a chartered accountant (ACMA, CGMA) and Microsoft MVP. He has trained over 1,000 people on how to build income statements in Power BI, delivered multiple talks on using the Microsoft BI stack within finance at various in-person and online events, and runs StarSchema.co.uk.




Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.