Showing posts with label data-lineage. Show all posts
Showing posts with label data-lineage. Show all posts

Sunday, 11 February 2024

Backward Data lineage

Backward data lineage, refers to the procedure of tracking data elements from the destination (like a report or dashboard) back to the original source. It stands in contrast to conventional data lineage, which monitors how data moves from its source to its eventual endpoint.

 

There are various use cases for employing backward data lineage:

 

1.   Understanding data point calculation: It aids in debugging errors or comprehending the rationale behind a specific report.

2.   Evaluating the alterations in data flow: If adjustments to data collection or processing are contemplated, reverse data lineage helps pinpoint all affected reports and dashboards.

3.   Regulatory compliance: Certain regulations, such as GDPR, necessitate organizations to trace the origin of personal data. Reverse data lineage facilitates meeting these obligations.

 


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Forward Data lineage

 

Forward lineage traces the path of data from its origin to the destination. It provides insights into how data flows through various processes and transformations, highlighting where it ends up for analysis, reporting, or other purposes. This type of lineage is crucial for tasks like impact analysis, where understanding how changes in one part of the system affect downstream processes is essential, as well as for ensuring data quality throughout its journey.

 

Let's illustrate forward lineage with an example scenario of a retail company managing sales data:

 

Example: Sales Data Forward Lineage

Data Collection and Initial Cleaning

Source: Point-of-sale (POS) systems in retail stores.

Transformation: Raw sales data is collected. Initial cleaning is applied to remove duplicates and correct obvious errors (e.g., negative sales quantities, missing values in critical fields).

Destination: Cleaned data is temporarily stored in a staging area for further processing.

 

Data Validation and Enrichment

Source: Cleaned sales data from the staging area.

Transformation: Data undergoes validation checks for consistency and completeness. It is then enriched with additional information, such as linking product IDs to product names and categories, and appending customer segmentation information.

Destination: Enriched data is moved to a data warehouse, ready for analysis and reporting.

 

Aggregation for Reporting

Source: Enriched sales data in the data warehouse.

Transformation: Data is aggregated by various dimensions (e.g., time period, product category, store location) to support reporting needs. Further data quality checks are applied to ensure aggregation accuracy.

Destination: Aggregated data is stored in a reporting database or data mart, optimized for fast query performance for business intelligence tools.

 

Analysis and Business Intelligence

Source: Aggregated sales data from the reporting database.

Transformation: Data is analyzed to identify trends, measure performance against sales targets, and generate insights into customer behavior. Advanced analytics may be applied to forecast future sales and inform inventory management.

Destination: Insights and reports are generated and made available to business users through dashboards and reporting tools, supporting decision-making processes across the organization.

 

JSON Doc

{
  "sales_data_forward_lineage": {
    "data_collection_and_initial_cleaning": {
      "source": {
        "name": "Point-of-sale (POS) systems",
        "location": "Retail stores",
        "type": "Transactional data"
      },
      "transformation": {
        "steps": [
          {
            "name": "Data Collection",
            "description": "Raw sales data collection from POS systems"
          },
          {
            "name": "Initial Cleaning",
            "description": "Removal of duplicates and correction of obvious errors"
          }
        ]
      },
      "destination": {
        "name": "Staging Area",
        "location": "Internal data storage",
        "type": "Temporary storage"
      }
    },
    "data_validation_and_enrichment": {
      "source": {
        "name": "Staging Area",
        "location": "Internal data storage",
        "type": "Cleaned transactional data"
      },
      "transformation": {
        "steps": [
          {
            "name": "Data Validation",
            "description": "Checks for data consistency and completeness"
          },
          {
            "name": "Data Enrichment",
            "description": "Augmentation of data with additional information (e.g., product details, customer segmentation)"
          }
        ]
      },
      "destination": {
        "name": "Data Warehouse",
        "location": "Internal data storage",
        "type": "Long-term storage for analysis"
      }
    },
    "aggregation_for_reporting": {
      "source": {
        "name": "Data Warehouse",
        "location": "Internal data storage",
        "type": "Enriched transactional data"
      },
      "transformation": {
        "steps": [
          {
            "name": "Data Aggregation",
            "description": "Summarization of sales data by various dimensions (e.g., time period, product category)"
          },
          {
            "name": "Data Quality Checks",
            "description": "Further validation to ensure accuracy and completeness"
          }
        ]
      },
      "destination": {
        "name": "Reporting Database/Data Mart",
        "location": "Internal data storage",
        "type": "Optimized for fast query performance"
      }
    },
    "analysis_and_business_intelligence": {
      "source": {
        "name": "Reporting Database/Data Mart",
        "location": "Internal data storage",
        "type": "Aggregated sales data"
      },
      "transformation": {
        "steps": [
          {
            "name": "Data Analysis",
            "description": "Identification of trends, performance evaluation, and insights generation"
          },
          {
            "name": "Advanced Analytics",
            "description": "Forecasting future sales, supporting inventory management decisions"
          }
        ]
      },
      "destination": {
        "name": "Business Intelligence Tools",
        "location": "Internal data analysis platforms",
        "type": "Dashboards and reporting tools for business users"
      }
    }
  }
}

 

Explanation of the JSON document:

a.   Each stage of the Sales Data Forward Lineage is represented as an object within the "sales_data_forward_lineage" object.

b.   Detailed information about the source, transformation, and destination components is provided for each stage.

c.    The source component includes details such as the name, location, and type of data.

d.   The transformation component includes a list of steps with names and descriptions describing the transformation process applied to the data.

e.   The destination component specifies where the transformed data is stored or utilized.

This JSON document provides a comprehensive and structured representation of the forward lineage of sales data, highlighting the various components involved in each stage of the data processing workflow.

 

 

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Operational lineage

Operational lineage is instrumental in tracking the execution history of data processes, providing insights into runtime, frequency, and performance metrics. This type of lineage aids in monitoring and managing the operational aspects of data processing activities, facilitating optimization and troubleshooting efforts. Let's elucidate this with an example scenario of ‘Customer Support Ticketing System’.

 

Example: Customer Support Ticketing System

Ticket Creation Process

Execution Time: Triggered by customer interactions.

Frequency: Variable, based on customer inquiries.

Performance Metrics: Ticket creation time, number of tickets generated.

Purpose: Creates new support tickets for customer inquiries or issues.

Operational Lineage: Logs each ticket creation event, including timestamp, customer details, and nature of inquiry.

 

Ticket Assignment Process

Execution Time: Immediately after ticket creation.

Frequency: N/A (Triggered by ticket creation).

Performance Metrics: Assignment time, ticket assignment rate.

Purpose: Assigns support tickets to available agents or teams based on workload and expertise.

Operational Lineage: Tracks ticket assignment events, recording agent/team assignments and response times.

 

Ticket Resolution Process

Execution Time: Varies based on ticket complexity and agent availability.

Frequency: Continuous, as tickets are resolved.

Performance Metrics: Resolution time, first response time, customer satisfaction ratings.

Purpose: Resolves customer inquiries or issues to provide timely assistance.

Operational Lineage: Captures ticket resolution events, documenting resolution times, agent interactions, and customer feedback.

 

Ticket Escalation Process

Execution Time: When a ticket requires specialized expertise or managerial intervention.

Frequency: Occasional, triggered by escalated issues.

Performance Metrics: Escalation time, escalation rate.

Purpose: Escalates tickets to higher-level support or management for resolution.

Operational Lineage: Records ticket escalation events, indicating reasons for escalation and actions taken.

 

In this example, operational lineage tracks the execution history of various processes within a customer support ticketing system. It captures key operational aspects such as execution times, frequencies, and performance metrics for each process, facilitating effective monitoring and management of customer support activities. Operational lineage enables insights into process efficiency, agent workload, resolution times, and customer satisfaction, supporting continuous improvement efforts and enhancing overall customer support operations.

{
  "ticketing_processes": [
    {
      "name": "Ticket Creation Process",
      "execution_time": "Triggered by customer interactions",
      "frequency": "Variable, based on customer inquiries",
      "performance_metrics": {
        "creation_time": "Duration of ticket creation",
        "tickets_generated": "Number of tickets generated"
      },
      "purpose": "Creates new support tickets for customer inquiries or issues"
    },
    {
      "name": "Ticket Assignment Process",
      "execution_time": "Immediately after ticket creation",
      "frequency": "N/A (Triggered by ticket creation)",
      "performance_metrics": {
        "assignment_time": "Duration of ticket assignment",
        "assignment_rate": "Rate of ticket assignments"
      },
      "purpose": "Assigns support tickets to available agents or teams based on workload and expertise"
    },
    {
      "name": "Ticket Resolution Process",
      "execution_time": "Varies based on ticket complexity and agent availability",
      "frequency": "Continuous, as tickets are resolved",
      "performance_metrics": {
        "resolution_time": "Duration of ticket resolution",
        "first_response_time": "Time to first response",
        "customer_satisfaction": "Customer satisfaction ratings"
      },
      "purpose": "Resolves customer inquiries or issues to provide timely assistance"
    },
    {
      "name": "Ticket Escalation Process",
      "execution_time": "When a ticket requires specialized expertise or managerial intervention",
      "frequency": "Occasional, triggered by escalated issues",
      "performance_metrics": {
        "escalation_time": "Duration of ticket escalation",
        "escalation_rate": "Rate of ticket escalations"
      },
      "purpose": "Escalates tickets to higher-level support or management for resolution"
    }
  ]
}

 

Explanation of the JSON document:

 

a.   Each process within the Customer Support Ticketing System is represented as an object within the "ticketing_processes" array.

b.   Each process object includes details such as name, execution time, frequency, performance metrics, and purpose.

c.    Performance metrics are provided to measure the efficiency and effectiveness of each process, including metrics like creation time, resolution time, first response time, and customer satisfaction ratings.

This JSON document outlines the operational lineage of various processes within the customer support ticketing system, providing insights into their execution history, frequencies, and performance metrics.

 

 

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Physical lineage

 

Physical lineage delves into the granular technical details of data movement, offering a comprehensive view of how data traverses through various hardware and software components, including databases, servers, and ETL (Extract, Transform, Load) jobs. This level of lineage is crucial for IT professionals responsible for managing, troubleshooting, and optimizing the data infrastructure of an organization. Here's an example illustrating physical lineage:

 

Example: E-commerce Data Processing Pipeline

Data Collection

Hardware/Software: Web servers collect user interaction data from the e-commerce website.

ETL Job: Apache Kafka streams capture and buffer website activity logs.

Database: Raw data is stored in a MongoDB database for temporary storage.

 

Data Transformation

ETL Job: Apache Spark job reads data from MongoDB, transforms it into a structured format, and enriches it with additional product information.

Server: Apache Spark cluster processes data transformations.

 

Data Loading

ETL Job: Transformed data is loaded into a PostgreSQL database for analytical purposes.

Database: PostgreSQL database stores structured data for analysis and reporting.

 

Analytics and Reporting

Software: Business intelligence tools like Tableau connect to the PostgreSQL database to generate reports and visualizations.

Server: Tableau Server hosts and serves interactive dashboards to business users.

 

In this example, physical lineage provides detailed insights into the hardware and software components involved in the e-commerce data processing pipeline. It outlines the exact path and transformations of data, from collection on web servers to buffering in Kafka streams, storage in MongoDB, transformation using Apache Spark, loading into PostgreSQL, and visualization with Tableau. This level of detail is essential for IT professionals to effectively manage, troubleshoot, and optimize the performance of each component within the data infrastructure.

{
  "data_collection": {
    "components": [
      {
        "type": "hardware",
        "name": "Web Servers",
        "description": "Collect user interaction data from the e-commerce website.",
        "details": {
          "location": "Data Center A",
          "type": "Virtual Machine",
          "IP_address": "192.168.1.100",
          "CPU": "4 cores",
          "RAM": "16 GB",
          "Storage": "500 GB SSD"
        }
      },
      {
        "type": "software",
        "name": "Apache Kafka",
        "description": "Stream website activity logs for buffering.",
        "details": {
          "location": "Data Center B",
          "type": "Docker Container",
          "FQDN": "kafka.example.com",
          "CPU": "2 cores",
          "RAM": "8 GB",
          "Storage": "100 GB HDD"
        }
      },
      {
        "type": "database",
        "name": "MongoDB",
        "description": "Store raw data for temporary storage.",
        "details": {
          "location": "Data Center C",
          "type": "Dedicated Server",
          "IP_address": "10.0.0.50",
          "CPU": "8 cores",
          "RAM": "32 GB",
          "Storage": "1 TB HDD"
        }
      }
    ]
  },
  "data_transformation": {
    "components": [
      {
        "type": "ETL Job",
        "name": "Apache Spark",
        "description": "Read data from MongoDB, transform it into a structured format, and enrich it with additional product information.",
        "details": {
          "location": "Data Center A",
          "type": "Virtual Machine",
          "IP_address": "192.168.1.200",
          "CPU": "8 cores",
          "RAM": "32 GB",
          "Storage": "1 TB SSD"
        }
      },
      {
        "type": "server",
        "name": "Apache Spark Cluster",
        "description": "Process data transformations.",
        "details": {
          "location": "Data Center A",
          "type": "Cluster",
          "IP_addresses": ["192.168.1.201", "192.168.1.202", "192.168.1.203"],
          "CPU": "64 cores",
          "RAM": "512 GB",
          "Storage": "10 TB SSD"
        }
      }
    ]
  },
  "data_loading": {
    "components": [
      {
        "type": "ETL Job",
        "name": "PostgreSQL Loader",
        "description": "Load transformed data into PostgreSQL for analytical purposes.",
        "details": {
          "location": "Data Center B",
          "type": "Virtual Machine",
          "IP_address": "172.16.0.100",
          "CPU": "4 cores",
          "RAM": "16 GB",
          "Storage": "500 GB SSD"
        }
      },
      {
        "type": "database",
        "name": "PostgreSQL",
        "description": "Store structured data for analysis and reporting.",
        "details": {
          "location": "Data Center B",
          "type": "Dedicated Server",
          "IP_address": "172.16.0.50",
          "CPU": "16 cores",
          "RAM": "64 GB",
          "Storage": "2 TB HDD"
        }
      }
    ]
  },
  "analytics_and_reporting": {
    "components": [
      {
        "type": "software",
        "name": "Tableau",
        "description": "Connect to PostgreSQL database to generate reports and visualizations."
      },
      {
        "type": "server",
        "name": "Tableau Server",
        "description": "Host and serve interactive dashboards to business users.",
        "details": {
          "location": "Data Center C",
          "type": "Virtual Machine",
          "IP_address": "10.20.30.40",
          "CPU": "12 cores",
          "RAM": "64 GB",
          "Storage": "500 GB SSD"
        }
      }
    ]
  }
}

 

 

 

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Business Lineage

 

Business lineage provides a business-centric perspective on the movement of data, abstracting technical details to offer a clear understanding of how information flows within various business processes and functions. This approach is invaluable for business users seeking insight into how data evolves and supports operational workflows, enabling them to grasp the broader implications of data movement within the organization.

 

In many contexts, the terms "logical lineage" and "business lineage" are used interchangeably to describe the flow of data in terms of business processes and concepts rather than technical implementation details.

 

Let's deep dive into an example of logical lineage to better understand how it works:

 

Example: Customer Relationship Management (CRM) System

1. Lead Generation Process

Business Function: Identifying and attracting potential customers.

Logical Lineage: Customer leads are generated through various channels such as website inquiries, social media interactions, and trade shows.

Outcome: A list of potential customers is created, containing their contact details and interests.

 

2. Lead Qualification Process

Business Function: Assessing the quality and potential of generated leads.

Logical Lineage: Leads are evaluated based on criteria such as demographics, interests, and buying behaviour.

Outcome: Qualified leads are identified as prospects for further engagement.

 

3. Sales Engagement Process

Business Function: Interacting with qualified leads to convert them into customers.

Logical Lineage: Sales representatives engage with prospects through phone calls, emails, and product demonstrations.

Outcome: Prospects express interest and move further along the sales funnel.

 

4. Customer Onboarding Process

Business Function: Welcoming and integrating new customers into the company's ecosystem.

Logical Lineage: New customers are provided with welcome emails, orientation materials, and access to support resources.

Outcome: Customers feel valued and informed about the products or services they've purchased.

 

5. Customer Support Process

Business Function: Addressing customer inquiries, concerns, and issues.

Logical Lineage: Customer support teams handle inquiries via phone, email, chat, or helpdesk systems.

Outcome: Customers receive timely assistance, leading to satisfaction and loyalty.

 

6. Customer Feedback and Improvement Process

Business Function: Gathering feedback from customers to improve products and services.

Logical Lineage: Surveys, feedback forms, and review platforms are used to collect customer opinions and suggestions.

Outcome: Insights from customer feedback drive product enhancements, service improvements, and business strategy adjustments.

 

7. Customer Relationship Management (CRM) System

Business Function: Centralizing customer data and interactions for effective management.

Logical Lineage: Data from lead generation, sales engagement, customer onboarding, support interactions, and feedback mechanisms are consolidated within the CRM system.

Outcome: A unified view of customer interactions and relationships facilitates personalized communication, targeted marketing, and informed decision-making.

 

In this example, logical lineage outlines the flow of data and activities across various business processes within a Customer Relationship Management (CRM) system. It focuses on the conceptual aspects of how data moves and transforms to support key business functions, such as lead generation, sales engagement, customer support, and feedback management. By abstracting away technical details and emphasizing business processes, logical lineage helps business users understand how data supports and enhances their operations, leading to better customer relationships and business outcomes.

 

Example

{
  "business_processes": [
    {
      "name": "Lead Generation",
      "description": "Identifying and attracting potential customers.",
      "data_sources": ["website_inquiries", "social_media", "trade_shows"],
      "output": "customer_leads"
    },
    {
      "name": "Lead Qualification",
      "description": "Assessing the quality and potential of generated leads.",
      "input": "customer_leads",
      "output": "qualified_leads"
    },
    {
      "name": "Sales Engagement",
      "description": "Interacting with qualified leads to convert them into customers.",
      "input": "qualified_leads",
      "output": "new_customers"
    },
    {
      "name": "Customer Onboarding",
      "description": "Welcoming and integrating new customers into the company's ecosystem.",
      "input": "new_customers",
      "output": "onboarded_customers"
    },
    {
      "name": "Customer Support",
      "description": "Addressing customer inquiries, concerns, and issues.",
      "input": "onboarded_customers",
      "output": "satisfied_customers"
    },
    {
      "name": "Customer Feedback and Improvement",
      "description": "Gathering feedback from customers to improve products and services.",
      "input": "satisfied_customers",
      "output": "improved_products_services"
    },
    {
      "name": "CRM System Management",
      "description": "Centralizing customer data and interactions for effective management.",
      "input": ["qualified_leads", "new_customers", "onboarded_customers", "satisfied_customers", "improved_products_services"],
      "output": "CRM_system"
    }
  ]
}

 

Explanation of the JSON document:

 

a.   Business Processes: Describes each business process within the CRM system, including lead generation, lead qualification, sales engagement, customer onboarding, customer support, customer feedback and improvement, and CRM system management.

b.   Name: The name of the business process.

c.    Description: A brief description of the business process.

d.   Data Sources: Lists the sources contributing to each process.

e.   Input: Indicates the input data for each process, derived from the previous process or sources.

f.     Output: Represents the output data generated by each process, which serves as input for subsequent processes or systems.

 

This JSON document outlines the flow of data and activities across various business processes within a CRM system, providing a conceptual understanding of how data moves and transforms to support key business functions.

 

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End to end lineage

 

End-to-end lineage offers a comprehensive view of data movement and transformation across all stages of its lifecycle, from the initial data source to its final destination. Let's illustrate this with an example scenario of a retail company.

 

Data Collection

The process begins with the retail company collecting sales data from various channels, including online stores, in-store transactions, and partner platforms. This data includes information about customers, products, orders, and transactions.

 

Data Ingestion

The collected sales data is ingested into a centralized data platform, such as a data lake or data warehouse. This step involves transferring the raw data from its source systems into a unified format for storage and processing.

 

Data Processing and Transformation

Once ingested, the raw sales data undergoes processing and transformation to prepare it for analysis. This involves cleaning the data, standardizing formats, enriching it with additional information, and aggregating it for analysis.

 

For example, the raw data may be transformed to create consolidated customer profiles, standardized product identifiers, and aggregated sales metrics.

 

Analytics and Insights

The processed data is then analyzed to extract meaningful insights and derive actionable conclusions. Data scientists and analysts use various techniques such as statistical analysis, machine learning, and data visualization to uncover patterns, trends, and correlations in the data.

 

For instance, the retail company may analyze sales trends, customer segmentation, product performance, and marketing effectiveness to inform business decisions.

 

Reporting and Decision-Making

The insights derived from the data analysis are presented to stakeholders through reports, dashboards, and visualizations. This enables decision-makers to understand the findings and make informed decisions to drive business growth and optimization.

 

Decision-makers may use the insights to adjust marketing strategies, optimize inventory management, personalize customer experiences, and identify areas for operational improvement.

 

Action and Impact

Finally, based on the insights and decisions made, the retail company takes action to implement changes and improvements across its operations. This may involve launching new marketing campaigns, adjusting pricing strategies, introducing new product lines, or optimizing supply chain logistics.

 

The impact of these actions is monitored and evaluated over time to assess their effectiveness and refine future strategies.

 

In this example, end-to-end lineage provides a comprehensive view of how sales data moves through each stage of its lifecycle, from collection and ingestion to processing, analysis, decision-making, and action. It enables stakeholders to trace the flow of data across the organization, supporting data governance, compliance, and strategic decision-making efforts.

{
  "data_sources": [
    {
      "name": "online_stores",
      "description": "Sales data collected from online retail stores."
    },
    {
      "name": "instore_transactions",
      "description": "Sales data collected from in-store transactions."
    },
    {
      "name": "partner_platforms",
      "description": "Sales data collected from partner platforms or third-party vendors."
    }
  ],
  "data_platforms": [
    {
      "name": "centralized_data_platform",
      "description": "Centralized data platform for storing and processing sales data."
    }
  ],
  "data_processing_steps": [
    {
      "name": "data_ingestion",
      "description": "Ingests raw sales data from various sources into the centralized data platform.",
      "input_sources": ["online_stores", "instore_transactions", "partner_platforms"],
      "output": "raw_sales_data"
    },
    {
      "name": "data_transformation",
      "description": "Transforms raw sales data to prepare it for analysis.",
      "input": "raw_sales_data",
      "output": "processed_sales_data"
    }
  ],
  "analytics_steps": [
    {
      "name": "data_analysis",
      "description": "Analyzes processed sales data to extract insights and patterns.",
      "input": "processed_sales_data",
      "output": "analyzed_sales_data"
    }
  ],
  "reporting_and_decision_making": [
    {
      "name": "reporting",
      "description": "Generates reports and visualizations based on analyzed sales data.",
      "input": "analyzed_sales_data",
      "output": "reports_visualizations"
    },
    {
      "name": "decision_making",
      "description": "Uses insights from analyzed data to make informed business decisions.",
      "input": "analyzed_sales_data",
      "output": "business_decisions"
    }
  ],
  "actions_and_impact": [
    {
      "name": "action_plan_implementation",
      "description": "Implements action plans based on business decisions to drive business growth and optimization.",
      "input": "business_decisions",
      "output": "business_impact"
    }
  ]
}

 

Explanation of the JSON document:

 

1.   Data Sources: Describes the sources from which sales data is collected, including online stores, in-store transactions, and partner platforms.

2.   Data Platforms: Represents the centralized data platform used for storing and processing sales data.

3.   Data Processing Steps: Outlines the steps involved in processing sales data, including data ingestion from various sources and data transformation to prepare it for analysis.

4.   Analytics Steps: Describes the analytics step where processed sales data is analyzed to extract insights and patterns.

5.   Reporting and Decision Making: Represents the steps involved in generating reports and visualizations based on analyzed data and using insights to make informed business decisions.

6.   Actions and Impact: Illustrates the implementation of action plans based on business decisions to drive business growth and optimization, and the resulting business impact.

 

 

 

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