DW 360 Degrees

1. Integration Architecture

1.1. Ingestion

1.1.1. Real-time Processing Engines

1.1.1.1. Data Ingestion

1.1.1.2. CDC

1.1.1.3. MQ Architecture

1.1.2. Batch Processing Engines

1.1.2.1. Scheduling

1.2. Transformation

1.2.1. Source Standardization

1.2.2. Transformation

1.2.2.1. Address Cleansing

1.2.2.2. Name Standardization

1.2.3. Monitoring

1.2.4. Reconciliation

1.3. External

1.3.1. Social Data Integration

1.3.2. Web Sources Integration

1.4. Deliverables

1.4.1. Preparation

1.4.1.1. Integration Questionnaire

1.4.2. Audit

1.4.2.1. Integration Architecture Assessment

1.4.2.1.1. Tool - Build vs Reuse

1.4.2.1.2. Volume vs Performance

1.4.2.1.3. Need vs Build

1.4.2.1.4. Point-Point vs Hub based

1.4.2.2. Integration Design Assessment

1.4.2.2.1. Concurrency Design

1.4.2.2.2. Pipeline Design

1.4.2.2.3. Data Transformation Design

1.4.2.2.4. Exceptions & Alerts Design

1.4.2.2.5. Data Publishing Design

1.4.2.2.6. Tool Optimization

1.4.2.2.7. Source System Assessment

1.4.2.3. Job Schedule Design Assessment

1.4.2.3.1. Dependency Design

1.4.2.3.2. Restartability Design

1.4.2.4. DQ Transformation Assessment

1.4.3. Analysis

1.4.4. Recommendation

1.4.5. Execution

2. Data Architecture

2.1. Relational Datawarehouse

2.1.1. Centralized DW

2.1.2. Departmental Data Marts

2.1.3. Operational Datawarehouse

2.2. Metadata Management

2.2.1. Data Models

2.2.1.1. Modeling Paradigms

2.2.1.2. Tool Evaluation

2.2.2. Data Dictionary

2.2.3. Data Lineage

2.2.4. Data Transformation Rules

2.3. Master Data Management

2.3.1. Product

2.3.2. Customer

2.3.3. Distributor

2.4. Deliverables

2.4.1. Preparation

2.4.1.1. Audit Checklist

2.4.1.2. Project Plan

2.4.1.3. Audit Questionnaire

2.4.1.4. Stakeholder List

2.4.1.5. Workshop Meetings Calendar

2.4.2. Audit

2.4.2.1. Architecture Assessment

2.4.2.2. Data Quality Assessment

2.4.2.3. Database Design Assessment

2.4.2.3.1. DB Logical Model

2.4.2.3.2. DB Physical Model

2.4.3. Analysis

2.4.3.1. As-is State Analysis

2.4.3.2. Gaps Analysis to Ideal State

2.4.4. Recommendation

2.4.4.1. Data Model Changes

2.4.4.2. Architecture Changes

2.4.5. Execution

2.4.5.1. Roadmap

2.4.5.2. Transition Plan

2.4.5.3. Cost

3. Process

3.1. "Live" Standards

3.1.1. 1. Naming Guidelines

3.1.2. 2. Best Practices

3.2. Change Management

3.2.1. Automation

3.2.1.1. Impact Analysis

3.3. Administration

3.3.1. Know Before Fail

3.3.2. Capacity Plans

3.3.3. DRP

3.3.4. Monitoring

3.3.4.1. Performance Metrics

3.3.4.2. Load Metrics

3.3.4.3. DQ Metrics

3.4. Deployment

3.4.1. Automation

3.5. Version Control

3.6. Source System Analysis

3.6.1. Profiling

3.7. Manuals

3.7.1. User Guides

3.7.2. Admin Guides

3.7.3. Developer Guides

3.8. Wiki

3.9. Reviews

3.10. SDLC

3.10.1. Requirements Management

3.10.1.1. Story Writeups

3.10.2. ETL Design

3.10.3. Design

3.10.3.1. Data Models

3.10.3.2. Report Design

3.10.4. Testing

3.10.4.1. DQ Testing

3.10.4.2. ETL Testing

3.10.4.3. Test Data Generation

3.10.4.4. Performance Testing

3.10.4.5. Report Testing

3.10.4.6. Bug Tracking & Reporting

3.11. Governance

3.11.1. Methodology

3.11.2. People

3.11.2.1. Availability of skills

3.11.3. Maintainability

3.11.4. Reliability

3.11.5. Integrity

3.11.6. Flexibility

3.11.7. Adaptability

4. Infrastructure

4.1. Tool Inventory

4.2. Server Inventory

5. BI / Analytics Architecture

5.1. Diagnostic Analytics Architecture

5.1.1. Visual Data Discovery

5.1.2. Self Service BI

5.1.3. Proactive Alerts

5.1.4. Spatial BI

5.1.5. Collaborative BI

5.1.6. Operational BI

5.1.7. Real-time Analytics

5.2. Social Analytics Architecture

5.3. Advanced Analytics Architecture

5.3.1. Predictive Analytics

5.3.2. Prescriptive Analytics

5.4. Deliverables

5.4.1. Preparation

5.4.1.1. Report Usage Questionnaire

5.4.1.2. Stakeholder List

5.4.1.3. Workshop Setup

5.4.2. Audit

5.4.2.1. OLAP Design Assessment

5.4.2.2. Performance Assessment

5.4.2.3. Refresh Strategy Assessment

5.4.2.4. Report Need vs Build vs Usage (Relevancy) Assessment

5.4.2.5. Latency Assessment

5.4.2.6. Quality Assessment

5.4.2.7. Publish Strategy Assessment

5.4.2.8. Self Service Assessment

5.4.3. Analysis

5.4.4. Recommendation

5.4.5. Execution

5.5. Descriptive Analytics Architecture

5.5.1. Static BI Reporting

5.5.2. Adhoc Querying

5.5.3. Search BI

5.5.4. CPM Dashboards

5.5.5. Mobile BI

5.5.6. KPI Catalog

6. Business Value

6.1. Agility

6.2. Financial Impact

6.2.1. Cost Optimization

6.2.2. New Business

6.3. Customer 360

6.4. Operational Improvements

7. Future readiness

7.1. Big Data Management

7.1.1. Columnar MPP Datastores

7.1.2. NoSQL Data Stores

7.1.3. Distributed Hadoop Stores

7.2. In-Memory Architecture

7.2.1. Cached Key-Value Data Stores

7.2.2. Real-time Big Data Analytics

7.3. CoE & Labs

7.3.1. Idea Validation

7.3.2. Conferences

7.3.3. New Technology Incubation

7.3.4. Tool Evaluation

7.3.5. Training