Data integration and scalability: A framework for model assessment and optimization

AvnishGoel

Author: Avnish Goel is a Certified Master Anaplanner and Founder at ScriptStory.

Recently, I led a model assessment initiative to identify suboptimal design elements in the data architecture for a leading investment banking firm. The project involved analyzing source systems and Anaplan models, leveraging both Anaplan best practices and general data exploration methodologies. I developed a comprehensive framework to guide Anaplan practitioners in conducting standardized assessments, ensuring scalability and data integrity as foundational principles in Anaplan model design and development. An overview is below, and the detailed framework can be found in the Excel attachment at the bottom of this article.

Objective

The primary objective of this study is to analyze the data integration framework between source systems and Anaplan to ensure seamless, accurate, and efficient data flows. The study aims to identify anomalies within the current integration processes, propose actionable recommendations, and provide detailed remediation steps for each identified gap.

Scope of study

1. Framework analysis: Examine the existing integration workflows, including data extraction, transformation, and loading processes between source systems and Anaplan.
2. Identification of anomalies: Document key issues such as data inconsistencies, validation failures, manual intervention points, scalability challenges, and performance bottlenecks.
3. Recommendations: Propose targeted recommendations to resolve each anomaly, emphasizing best practices, automation, and process optimization.
4. Remediation plan: Develop a detailed step-by-step remediation strategy to address identified gaps, ensuring sustainable improvements.

Deliverables

1. Key findings: Comprehensive list of anomalies, categorized by their impact on data quality, performance, and operational efficiency.
2. Recommendations document: A focused action plan for addressing issues, aligned with industry best practices for data integration.
3. Remediation framework: A detailed roadmap for implementing corrective measures, including timelines, responsibilities, and success metrics.

Outcome

This study will result in a robust data integration framework that minimizes errors, enhances automation, ensures data integrity, and optimizes the use of Anaplan for planning and modeling. The improved framework will provide a scalable, efficient, and future-proof solution for data integration challenges.

The attached Excel document provides this detailed framework.

Rapid Model Assessment Framework.xlsx

Leave a comment with thoughts or questions!

Iverv

Hi Anish, thanks for this. Have you considered Anaplan Data Orchestrator in your study ?