Phase I: Scoping & Baseline Analysis

Objective: Understand your current situation and identify improvement opportunities.
Key Activities:

1. Scope of Investigation: Define the project objectives (e.g., improving sales forecast accuracy).
2. Current Data Architecture & Capabilities: Assess current tools (e.g., does your ERP system collect enough customer data?).
3. Opportunities & Improvement Potential: Identify gaps and opportunities (e.g., missing consolidated dashboards).

Real-World Example:
An e-commerce business discovers its system doesn’t capture customer behavior data, such as items added to the cart but not purchased. In Phase I, they identify this as an opportunity to understand purchase intent and optimize sales.

Phase II: Business Outcomes & Solution Requirements

Objective: Translate business objectives into technical requirements and potential solutions.
Key Activities:

1. Business Outcome Definition: Identify desired results (e.g., reduce product returns by 20% through better stock management).
2. Data Product & Use Case Analysis: Highlight priority use cases (e.g., analytics tools to predict product returns based on past behavior).
3. Solution Context Consolidation: Clarify how proposed solutions fit into existing processes.

Real-World Example:
A pharmacy chain wants to anticipate medication shortages. They identify a use case where historical sales data and seasonal forecasts are used to predict future demand.

Phase III: Capability Map & Solution Architecture

Objective: Design and validate a solution architecture that meets business objectives.
Key Activities:

1. Capability Analysis & Solution Mapping: Map necessary capabilities (e.g., a warehouse requiring real-time tracking).
2. Develop Architecture Options: Propose architecture options (e.g., an SAP HANA-based system for real-time calculations).
3. Validate & Finalize Target Architecture: Validate the best solution with stakeholders.

Real-World Example:
A manufacturing plant uses IoT sensors to collect real-time machine data. This phase helps design an architecture to integrate these data points into SAP EWM, optimizing stock management and reducing downtime.

Phase IV: Impact Analyses & Roadmaps

Objective: Define an implementation roadmap and ensure effective data governance.
Key Activities:

1. Data Governance: Establish rules to ensure data quality and security.
2. Roles & Organization: Define roles (e.g., who will manage the data?).
3. Roadmaps / Project Plan: Create a clear action plan with concrete steps.

Real-World Example:
A company plans to create a dashboard to visualize key performance indicators (KPIs). This phase establishes a project plan to deploy dashboards using SAP Analytics Cloud, with clear milestones and responsibilities.

Why Is This Methodology Essential for Your Business?

Data is often called the “new oil” of the 21st century. Without a structured methodology, its potential remains untapped. The SAP Data & Analytics Advisory Methodology helps businesses:

• Optimize business processes: Using reliable and actionable data.
• Enhance decision-making: Leveraging advanced tools like SAP Analytics Cloud.
• Prepare for future challenges: By integrating scalable solutions.

In Summary

The SAP Data & Analytics Advisory Methodology provides a clear framework to transform your data into a strategic asset. Whether improving stock management, forecasting demand, or optimizing customer experience, every step is designed to align data with business objectives.

Do you have a project to launch? Think of this methodology as a compass to guide your efforts and maximize results.