1.0-Reporting-Analytics-Foundation-Nielseniq

Enterprise Reporting, Automation & SQL-Based Analytics Concepts

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1️⃣ Background

This repository represents the foundation phase of my analytics career at NielsenIQ, where I worked as a Reporting Analyst supporting enterprise FMCG clients.

The work focused on:

• SQL-based analytical concepts via Answers
• Large-scale reporting automation using Advanced Excel (RTL templates)
• Business analysis, visualization, and customer enablement

All examples in this repository use dummy data to respect confidentiality.

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2️⃣ Scope & Scale

• Managed approximately 1000 recurring analytical reports
• Markets covered: Switzerland & Austria
• Supported FMCG clients across multiple categories and product hierarchies
• Ensured accuracy, consistency, and on-time delivery at enterprise scale

This role required strong discipline, automation, and a deep understanding of business logic.

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📊 End-to-End Data Flow

![End-to-End Data Flow]

High-level flow from SQL-based extraction to business insights.

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1.1 Answers – SQL-Based Analytics Concepts

Answers functioned as a SQL-driven analytics layer, where business selections were translated into structured query logic.

Although users interacted via a UI, the underlying concepts mirrored SQL behavior:

• Filtering
• Aggregation
• Grouping
• Parameter-driven queries

The output of Answers was always a raw data extract, not a formatted report.

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1.2 Dynamic Selection Logic (Answers)

Market, Product, Fact & Time Selections

Report creation was driven by structured selections that controlled the raw data extract.

![Product Hierarchy Example]

Illustrative product hierarchy used for aggregation and roll-ups.

Market Selection

• Total Market
• Coop
• Migros

Product Selection

• Single products
• Multi-level hierarchies
• Custom dynamic hierarchies (thousands of items)

Fact Selection

• Sales Value
• Volume
• Price
• Market Share

Time Selection

• YTD, MAT
• Month, Year, Week
• 4-4-4 and 4-4-5 calendars

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1.3 Excel RTL Framework & Automation

![Excel RTL Architecture]

Conceptual view of the Excel RTL reporting framework.

The raw output from Answers was fed into Advanced Excel RTL templates.

Key characteristics

• Hundreds of sheets per file
• One visible reporting layer
• Hidden calculation and control layers

Automation Flow

1. Raw data refresh
2. RTL recalculation
3. KPI update
4. Visual refresh

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1.4 Analysis & Visualization

![Sample Sales Trend]

Example sales trend visualization using dummy data.

Analysis Performed

• Trend analysis
• Period comparison
• Market share evaluation
• KPI variance analysis

Visuals were designed to answer business questions, not just display data.

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1.5 Customer Enablement & Schema Explanation

Customers were supported in understanding:

• Database schemas
• Product hierarchies
• KPI definitions

This enabled:

• Self-service reporting
• Correct interpretation of outputs
• Reduced operational dependency

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How This Repository Fits in the Bigger Journey

This repository represents Phase 1

• Phase 1: Reporting & Analytics Foundation Nielseniq-reporting-analytics-foundation (this repo)
• Phase 2: BI Transformation & Data Lake Migration Nielseniq-bi-transformation-discover
• Phase 3: Database Architecture & Business Translation Nielseniq-database-architecture-business-translation

Each phase is documented separately for clarity and focus.

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🔐 Data Confidentiality

All examples in this repository use dummy data.
No proprietary NielsenIQ systems, schemas, or client data are exposed.

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Navigation

• Overview: About Me It contain links to My portfolio and all major pages and projects

• NielsenIQ: NielsenIQ-Enterprise-Analytics-Journey Links to all Nielsen Repo

• ECO3: ECO3-enterprise-analytics-journey Links to all ECO3 Repo