Mastering IBM Industry Data Warehousing Models

Explore the overview and importance of data warehousing, including staging, atomic data, data marts, etl, and architecture, with banking-focused applications for informed decision making.

Master data warehousing enables strategic decision making and advanced analytics with a consolidated data view. It supports real-time insights, historical trend analysis, and personalized marketing based on customer behavior.

Explore data sources, ETL, and the central data warehouse, plus data models, data marts, and reporting tools, with examples such as SQL Server, Oracle, Snowflake, Amazon Redshift, and OLAP cubes.

Explore the benefits and use cases of data warehousing, including improved decision making, real-time data access, data quality, and consolidated view across sales, finance, customer relationship management, and supply chain.

Explore banking data warehouse architecture, from integrated source systems to normalized DW and data marts, with ETL, data integration layers, and analytics via IBM Cognos and IBM DataStage.

Explore basics of IBM Infosphere Data Architect and its role in data modeling. Learn its features for conceptual, logical, and physical models, including a graphical interface.

Explore the purpose and functionality of IBM Infosphere Data Architect across conceptual, logical, and physical data models, including automatic DDL generation and seamless transformation between logical and physical models.

Explore how IBM Infosphere Data Architect integrates with the IBM ecosystem to align data modeling with business objectives, improve data quality with Analyzer, and enable collaboration via Workbench and Cognos.

Leverage IBM Infosphere Data Architect's industry templates and standards, highlighted by the banking and financial market atomic warehouse model, to accelerate banking data modeling with Basel-aligned compliance.

Discover how IBM Infosphere Data Architect enables data governance and metadata management with a centralized repository for data model metadata, easy access, documentation, collaboration, and governance-aligned decision making.

Explore IBM Infosphere Data Architect's code generation and DDL scripting to automatically generate and customize DDL scripts for multiple database platforms, enabling seamless implementation of data models.

Explore IBM Infosphere Data Architect impact analysis as a safeguard that foresees consequences of data model changes, assesses effects on data flow, system interactions, and applications.

Explore IBM Infosphere Data Architect's reporting and documentation capabilities, selecting logical and physical data models to tailor reports, then publish on the web to enhance stakeholder communication and interactive exploration.

Explore IBM Infosphere Data Architect's cross-platform compatibility, enabling unified data modeling across DBMS like DB2, MySQL, Oracle, PostgreSQL, SQL Server, with diverse import sources and flexible deployment.

Explore reverse engineering in IBM Infosphere Data Architect to extract metadata, visualize database structures with ERDs, and generate documentation for seamless migration across MySQL, Oracle, SQL Server, and other DBMS.

Explore custom naming conventions in IBM Infosphere data architect to tailor data models to organizational and industry standards, and support transformations from logical to physical data models.

Explore the traditional data modeling process, moving from logical data modeling to physical data modeling, focusing on business concepts, entities, relationships, constraints, and performance-driven implementation.

Explore top down and bottom up data modeling strategies, compare with pre-designed IBM industry data models, and learn when to leverage existing data sources and hybrids.

Translate use cases and service specifications into logical and physical data models by defining business terms, building the logical model, refining into the physical model to align with business needs.

Break down a complex data model into packages and diagrams to simplify navigation and maintenance, enhance communication, and provide focused model abstractions for specific business domains.

Master forward and reverse engineering to create physical data models from designs, defining entities, attributes, data types, primary keys, foreign keys, relationships, and constraints, drag-and-drop import from databases or DDL.

Import a logical data model into IBM Infosphere Data Architect, refine it, and perform forward engineering to transform it into a physical data model and generate a DDL script.

Reverse engineer a database to create a physical data model, modify it, and synchronize with the source. Generate delta DDL, deploy changes, visualize with Data Source Explorer, and publish documentation.

Install a MySQL database server with MySQL workbench, create a schema named source rental for car rental data, manage users, monitor performance, and visualize data for IBM Infosphere Data Architect.

Install IBM Infosphere Data Architect on Windows by downloading version 9.1.4 and meeting 2.5 gb requirements, then complete administrative installation to access a graphical interface for modeling and standardizing data.

Demonstrate forward engineering in IBM Infosphere Data Architect by importing a logical data model, transforming it to a physical model, and generating and saving a DDL script.

Demonstrates publishing logical and physical data models to web files using Infosphere Data Architect. View the generated web site via index.html and explore table, column, and diagram resources.

Demonstrates reverse engineering with IBM Infosphere Data Architect: create and compare a physical data model; generate and deploy delta DDL to MySQL and view changes in Data Source Explorer.

Explore IBM industry data models, tailored frameworks that address finance, healthcare, insurance, retail, and telecommunications data challenges, highlighting benefits, success factors, and strategies for improved data management.

IBM industry data models provide pre-designed, industry-specific data models and blueprints tailored to industry needs, with best practices and regulatory alignment to accelerate BI development and unify enterprise data.

Explore IBM industry data models, including a business driven data model, pre designed components for warehouses and relational models, and industry terminology, analytics requirements, and bridges to Basel and Fatca.

Implementing IBM industry data models accelerates time to value and ROI by enabling collaborative and agile data modeling for consolidated data assets and reporting across business units.

Understand why IBM industry data models may replace bespoke in-house data modeling, and weigh staffing, resource pools, industry changes, chargeback systems, costs, duplication, and alignment with organizational goals.

Master IBM industry data models by embracing agile methods, building proficient data modeling skills, and ensuring data quality and governance to align enterprise data resources with business goals.

Leverage IBM banking and financial markets data warehouse models to help financial organizations meet regulatory compliance and support asset and liability management, investment, profitability, risk, wealth management, and relationship marketing.

Explore IBM industry data warehousing models, including the insurance information warehouse and healthcare retail telecommunications data warehouse, designed with pre-designed components and predefined analytic requirements.

Explore IBM financial and banking data models, including the financial services data model (Fsdsm) with nine core concepts, analytical requirements and dimensional data model, plus data warehouse model.

Explain how data flows from OLTP sources through ETL into the enterprise data warehouse, enabling data marts, cubes, and decision support reporting using IBM banking data warehouse models.

Explore extensive banking data models tailored for the financial services industry. Identify how fstmb defines concepts, enables enterprise-wide data depiction, and supports analytical needs through data warehouse models.

Explore the nine data concepts in the financial services data model, including involved party, arrangement, condition, product, location, classification, business direction item, event, and resource item.

Explore the FSDM business conceptual model by examining the nine data concepts and their interrelationships, including involved party, arrangement, product, event, accounting unit, location, classification, and channel.

Explore the analytical requirements model and how data items in data marts and reports shape KPIs, measures, dimensions, and mappings aligned to the data warehouse model.

Explore the analytical requirements model in focus areas: asset and liability management, investment management, payments, profitability, regulatory compliance, relationship marketing, risk management, and wealth management, focusing on measures and dimensions.

Explore asset and liability management within the IBM banking data model, analyzing maturities, cash flows, capital allocations, rate variations, and liquidity to maximize long-term wealth.

Explore how investment management analyzes custody, cash management, fund accounting, performance and attribution, and governance within IBM banking data models, including corporate actions and foreign exchange analysis.

Explore payment analytics and processing in the IBM banking data model, covering high value outward payments, inward payments, fraud analysis, merchant analysis, and service performance.

Explore profitability analysis in the IBM Banking Data Model, covering activity-based costing, channel profitability, customer lifetime value, and product profitability to optimize income, costs, and strategic decisions.

Explore how the IBM banking data model supports regulatory compliance analysis, covering Basel capital adequacy, AML controls, data subject consent, FATCA and ECB reporting, and suspicious activity and transaction analysis.

Explore relationship marketing analysis within the IBM banking data model, focusing on campaign and cross-sell analyses, customer behavior patterns, complaints, and interaction analytics to optimize marketing and relationship management.

Explore risk management analysis within the IBM banking data model, covering liquidity, credit, and market risk, along with non-performing loans and Basel II reporting.

Explore wealth management analysis within the IBM banking data model, examining asset allocation, portfolio gains, performance, and risk analyses to optimize portfolio strategies.

Explore the dimensional warehouse model and organize fact entities per analytical requirement. Apply conformed dimensions and modular packages per focus area to ensure traceability and consistency across the atomic layer.

Explore the atomic warehouse model in the banking data warehouse, detailing subject oriented, normalized data and package structure that groups nine data concepts, including core business information and associative packages.

Explore the atomic warehouse model's core data entities, associative relationships, classifications, and history to organize transactions, communications, accounts, and supporting details within the financial institution.

Discover how the fundamental entity forms the backbone of the atomic warehouse model, linking business terms to the data model, enabling time-variant versioning and history management.

Explore the history entity in the atomic warehouse model, focusing on time-variant attributes, history patterns, and invariant facts to enable efficient storage, retrieval, and historical analysis.

Capture opening and closing values, averages, and aggregations for fixed and ad hoc periods in periodic history using summary and profile tables.

Define episodic history by storing events as completed, non-volatile history entities like transactions and communications, creating immutable records for reliable historical data in the data warehouse.

Discover continuous relationships in data warehouses using associative entities to track evolving connections among involved parties, arrangements, resources, and classifications with effective and end dates.

Explore how the associative entity in the atomic warehouse model manages relationships between entities, supports business terms, enables relationship versioning, and uses a type hierarchy with timestamps for historical perspective.

Explore how the classification entity in the atomic warehouse model standardizes codes and supports a set of values. It classifies fundamental and associative entities using a type hierarchy.

Explore how the atomic warehouse model uses an associative employment arrangement to classify life cycle status, reasons, and types, with historical classification tracking for involved parties.

The lecture explains the supportive entity in the atomic warehouse model, a component for storing technical information. It supports population characteristics and metadata, with universal relationship across the warehouse package.

Summary entities store periodic and aggregated information as condensed data repositories, enabling gathering, summarization, and budgeting insights. They support periodic analysis, downstream data marts, and archiving of bulk system-of-record data.

Explore atomic warehouse model as a system of record within a data warehouse, using IBM Infosphere Data Architect with banking data model to map entities like party and location.

Demonstrates the nine core data concepts of the Fstmb financial services data model, including involved party, arrangement, condition, product, location, classification, business direction, item, event, and resource item.

Demonstrates the analytical requirements model and dimensional data model within the IBM banking data model, detailing focus areas, measures, dimensions, and mappings for analytics.

Explore the atomic warehouse model components, including fundamental, associative, classification, temporal, and supportive entities, using IBM Infosphere Data Architect to map logical to physical models and generate DDL for DBMS.

Explore data warehousing in banking with IBM Infosphere Data Architect and the banking data model. Apply hands-on concepts like staging atomic data, data marts, and ETL processes.