Data Architecture and Business Models: Netflix, Walmart, and More

Posted on Mar 8, 2025 in Business Management and Marketing

Data Architecture at Netflix

Netflix utilizes a complex data system to support its streaming service, encompassing TV shows and movies. This architecture is crucial for managing large data volumes generated from viewer interactions, enhancing the recommendation system, and optimizing streaming quality.

Data Collection

Netflix gathers data from user interactions across various devices and content streaming logs.

Data Storage

The company employs a large-scale data lake using Amazon S3 to store vast amounts of raw data in a semi-structured format.

Data Processing

Netflix uses Extract, Transform, Load (ETL) processes and real-time data processing streams for stateful computations on data streams. It also analyzes and processes these large datasets.

Data Analysis & Business Intelligence

Netflix utilizes AWS analytics tools and internal tools, built on big data technologies, to analyze viewing patterns and inform programming decisions. Machine learning algorithms power the recommendation engine, analyzing viewing habits to personalize content for each user.

Data Governance & Security

Netflix implements robust data governance frameworks to ensure data quality, consistency, and usability. Security is managed using a combination of AWS security tools and proprietary Netflix solutions to maintain data integrity and privacy.

Data Reactive Company Example

Santa Gloria Coffee Shop Chain

  1. Limited Data Infrastructure: The coffee shop may have basic systems, like a simple point-of-sale system, to track sales and inventory. However, it lacks sophisticated data management tools or integrated platforms for comprehensive data analytics.
  2. Ad Hoc Data Analytics: The owner might occasionally review sales data to identify popular items or peak hours. However, this analysis is sporadic and not part of a structured, strategic planning approach.
  3. Limited Data Integration: Different types of data (sales, inventory, customer preferences) are stored separately and not effectively integrated. For example, customer feedback or loyalty data may not be linked to sales data.
  4. Manual Processes: Many data-related tasks, such as inventory tracking or employee scheduling, rely heavily on manual input and paper-based systems, leading to inefficiencies and potential errors.
  5. Limited Data Literacy: Coffee shop staff may have a limited understanding of how to leverage data for decision-making. For instance, they may not know how to interpret sales reports or use customer feedback to improve services.

Digital Company with a Data-Driven Business Model

Netflix

What? – Value Proposition: Netflix offers extensive entertainment content, including movies, TV shows, and original programming, accessible on various devices with personalized recommendations.

Who? – Value Chain: Netflix primarily serves individual consumers seeking entertainment. It leverages relationships with production studios, content creators, and technology providers to enrich its library and streaming quality.

How? – Revenue Model: Netflix generates revenue through subscription-based models with different pricing tiers based on streaming quality and the number of screens available simultaneously.

Why? – Business Model Elements Integrated by Data:

  • Data Integration: Uses viewer data to refine algorithms that recommend content based on viewing habits.
  • Customer Experience: Personalizes the viewing experience, making it easy for users to find content matching their preferences, thereby increasing user engagement and satisfaction.
  • Business Processes: Data analytics inform content acquisition and production decisions, optimizing investment and minimizing content-related risks.

Traditional Company with Data Integration

Walmart

What? – Value Proposition: Walmart offers a wide range of products at low prices, focusing on affordability and convenience both in-store and online.

Who? – Value Chain: Walmart interacts with a broad demographic of consumers and maintains a complex supply chain involving suppliers, distributors, and logistics providers to ensure wide product availability and quick delivery.

How? – Revenue Model: Walmart’s revenue comes from direct sales in both physical stores and online platforms, leveraging scale to maintain low prices and high-volume turnover.

Why? – Business Model Elements Integrated by Data:

  • Data Integration: Collects and analyzes customer data across online and offline channels to improve inventory management and customer service.
  • Customer Experience: Uses data to tailor marketing and in-store setups according to customer preferences and shopping behavior, enhancing the shopping experience.
  • Business Processes: Integrates advanced predictive analytics for inventory management, ensuring optimal stock levels and reducing waste and shortages.

New Home Control: Key Considerations

  1. Data Privacy and Security (High Priority – Threat): Crucial to protect user data to prevent breaches and maintain trust.
  2. Interoperability with Existing Systems (High Priority – Opportunity): Compatibility with diverse home automation devices to boost user adoption.
  3. User Experience and Interface Design (High Priority – Opportunity): An intuitive and user-friendly interface is essential for user engagement and retention.
  4. Reliability and Stability (Medium Priority – Opportunity): Must ensure minimal downtime and technical issues for reliable service.
  5. Scalability and Performance (Medium Priority – Opportunity): Must scale effectively with a growing user base to maintain performance.
  6. Regulatory Compliance (Medium Priority – Threat): Compliance with data protection laws and industry standards is critical.
  7. Market Competition (Medium Priority – Threat): Navigating a crowded market with both established and new players.
  8. Customer Support and Service (Low Priority – Opportunity): Effective support enhances user satisfaction and brand loyalty.
  9. Integration with Smart Assistants (Low Priority – Opportunity): Adding voice control features can increase functionality for users.
  10. Data Monetization Strategies (Low Priority – Opportunity): Potential for additional revenue through data analytics, but must ensure user privacy.

Relationship Between AI & Big Data

Example: Autonomous Cars

Data Collection: Autonomous vehicles collect vast amounts of data from sensors, cameras, and radar about their environment, road conditions, and traffic patterns.

Data Processing with AI: AI algorithms process this big data in real-time to make decisions, navigate routes, and avoid obstacles, effectively teaching the vehicle how to respond in various driving situations.

Application: This allows autonomous cars to drive safely without human intervention, adapting to new conditions as they learn from the data they continuously gather.

Benefits:

  • Improved Safety: Reduces human error in driving by relying on precise AI decision-making.
  • Efficiency: Optimizes route selection and reduces traffic congestion.
  • Learning Capability: Continuously improves performance through ongoing learning from collected data.

Automated Reporting Process for Zara’s Fashion Trends

  1. Understanding the Data
    • Activity: Review historical sales, product categories, and customer demographics.
    • Purpose: Grasp the scope and context of data to identify what to analyze.
  2. Define Goals & Questions
    • Activity: Focus on predicting fashion trends, like eco-friendly materials.
    • Purpose: Set clear objectives to guide the analysis.
  3. Data Cleaning in Excel
    • Activity: Remove irrelevant data, fix errors, and fill missing values.
    • Purpose: Ensure data quality for accurate analysis.
  4. Exploration & Analysis in Excel
    • Activity: Use pivot tables and charts to spot trends and patterns.
    • Purpose: Identify which fashion items are gaining popularity and where.
  5. Power BI for Automation
    • Activity: Import data into Power BI to create dynamic dashboards.
    • Purpose: Visualize trends and make real-time insights accessible.
  6. Schedule Automatic Updates
    • Activity: Set automatic data refreshes in Power BI.
    • Purpose: Maintain up-to-date insights for ongoing trend analysis.
  7. Results Presentation
    • Activity: Present findings using Power BI reports and dashboards.
    • Purpose: Effectively communicate actionable trend insights to decision-makers.

Z

Rock Festival: Data Strategies

1. Structure

Structured Data:

  • CRM data for targeted emails.
  • Early-bird ticket offers based on purchase history.

Semi-Structured Data:

  • CSV from surveys for demographic info.
  • Marketing tailored to feedback.

Unstructured Data:

  • Social media feedback analysis.
  • Adjust content to enhance public relations.

2. Collected Data

1st Party Data:

  • Data from own sources like ticket sales.
  • Personalized marketing and user experience enhancement.

2nd Party Data:

  • Partner data, e.g., food vendors.
  • Co-branded promotions and special offers.

3rd Party Data:

  • External data, e.g., from Amazon, streaming preferences.
  • Targeted advertising campaigns.

Rock Festival: Data Types and Origins

3. Types of Data

Web Analytics:

  • Monitor site traffic and user engagement.
  • Optimize website for conversions.

Static:

  • Historical sales trend analysis.
  • Predict sales peaks for strategic marketing.

Dynamic:

  • Real-time ticket purchase tracking.
  • Location-based promotions during high activity.

4. Origin

Self-Reported Data:

  • Data from surveys during ticket purchase.
  • Tailor festival line-ups and experiences.

Observed Data:

  • User interaction data on social platforms.
  • Marketing content adjustment to improve engagement.

Inferred Data:

  • Clicks on marketing emails and ads.
  • Refine advertising based on content performance.