The Data Organization

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1 C V I T F E P A O TM The Data Organization Best Practices Metadata Dictionary Application Architecture Prepared by Rainer Schoenrank January 2017

2 Table of Contents 1. INTRODUCTION PURPOSE OF THE DOCUMENT SCOPE OF THE DOCUMENT ORGANIZATION OF THE DOCUMENT CHANGE LOG SIGN-OFF APPLICATION OVERVIEW BACKGROUND GOALS SCOPE ASSUMPTIONS ANTICIPATED BENEFITS PROCESS ARCHITECTURE INTRODUCTION METADATA DICTIONARY PROCESS MODEL PROCESS MODEL COMPONENTS DATA ARCHITECTURE INTRODUCTION CONCEPTUAL DATA MODEL DATA MODEL COMPONENTS DATABASE MANAGEMENT SYSTEM PHYSICAL ARCHITECTURE INTRODUCTION HARDWARE ARCHITECTURE APPENDIX DEFINITIONS, ACRONYMS AND ABBREVIATIONS /1/2017 Page 2

3 1. INTRODUCTION 1.1 Purpose of the Document This document outlines the context of the Metadata Dictionary Application. The application architecture is the fundamental organization of the system. The Metadata Dictionary organization is embodied in its components, their relationships to each other, the computing environment, and the principles guiding the Metadata Dictionary s design and evolution. This document describes the results of the policy decisions that created the Metadata Dictionary and the organization of the IT support environment. 1.2 Scope of the Document This document outlines the Metadata Dictionary Application architecture. This architecture is based on the principles and policies for employing technology to meet IT management requirements. It serves as a reference for management and other interested parties to make sure that the ongoing decisions for the Metadata Dictionary application are consistent with the underlying policies and needs of the business. 1.3 Organization of the Document INTRODUCTION specifies the purpose, scope and organization of this document. APPLICATION OVERVIEW describes the context of the Metadata Dictionary Application, its functionality, and the organization of the IT processes. PROCESS ARCHITECTURE describes the primary IT processes and how they work together to support the required functionality. DATA ARCHITECTURE describes the organization and functionality of the database for the Metadata Dictionary Application. PHYSICAL ARCHITECTURE describes the processing environment, hardware, and system services in which the Metadata Dictionary Application operates. 3/1/2017 Page 3

4 1.4 Change Log Date Description Author 18 Feb 2005 Created document Implemented Databases only Rainer Schoenrank 21 Jan 2011 Added Implemented Applications Layer Rainer Schoenrank 31 Aug 2013 Added Enterprise Logical Data Model layer Rainer Schoenrank 15 Dec 2015 Added Business Reality and Data Model Semantics Rainer Schoenrank Layers 1.5 Sign-off Date Role Name Signature Program Manager Manager 3/1/2017 Page 4

5 2. APPLICATION OVERVIEW The Metadata Dictionary is the primary tool used by the Information Technology (IT) business unit to manage the metadata used to create the databases required to automate the business processes. 2.1 Background The business of IT is to produce reports for the business stakeholders Investors want financial status reports Executives want product performance reports Employees want process performance reports Governments want regulatory compliance and tax reports Customers want purchase history reports To meet these reporting needs, IT must know which business processes are being measured, what data is being stored, what does the data mean, what applications are being used, etc. To answer these questions, IT needs an inventory system for data, databases and applications. This is the role of the Metadata Dictionary. The Metadata Dictionary application is part of the IT shared services infrastructure. The location of the Metadata Dictionary within the IT architecture and the Metadata Dictionary s relationships to the Business Intelligence Architecture is shown in Figure 1. 3/1/2017 Page 5

6 Data Governance Data Errors Metadata Dictionary Business Model Create Business Models Customers OLTP Applications (Data Capture) Data Transfer Data Repository Data Marts (Data Selection) OLAP Tools Business Users Business Users Business Processing Enhancement Project Business Process Improvements Gain New Business Insights Manage Improvement and Change Figure 1: Context for Data Architecture Figure 1 shows the business intelligence architecture (center) in relation to the data governance (upper) and process improvement (lower) processes. The center block is the implementation of the data life cycle. The light arrows in the diagram show how data architecture, data governance and process improvement processes interact. The short heavy arrows show how business data flows from the OLTP applications to the data repository and the data marts. As the data is created by the OLTP applications, it is extracted and transformed into the data repository. The repository data is available for reporting using the OLAP tools on the data marts. The dashed arrows show how the data specifications and usages flow into the Metadata Dictionary. 3/1/2017 Page 6

7 The data governance processes are in the Data Governance block above the business intelligence architecture. The data governance process attempts to implement enterprise-wide policies about the creation, use and disposition of business data. Central to this effort is the Metadata Dictionary. Using the data specification in the Metadata Dictionary, data quality processes are created to detect errors in the transfer of business data between the OLTP applications. The data quality process produces lists of data records that do not meet the data quality requirements of the Metadata Dictionary. These reports are sent to the OLTP applications so that the data errors will be corrected and the data capture processes can be improved. The business improvement processes are in the Manage Improvement and Change block below the business intelligence architecture. As business problems are identified, the Metadata Dictionary is used to help build a model of the data that describes the problem and to determine where the business data is created and how it flows through the application layer. This business model is implemented in a data mart so that the data can be sliced and diced to gain insight to the problem and to suggest a solution. 3/1/2017 Page 7

8 2.2 Goals The goal of maintaining a metadata repository is to develop the inventory of data objects and the processes that use those data objects as sources and sinks of data for the Information Technology organization. The metadata repository enables the IT organization to meet the business requirement for describing the data, reporting its use and tracking the transformations as the data moves from source to destination. The Metadata Dictionary must be able to resolve three main data issues: 1. Synonyms business data objects that have different names but the same meanings 2. Homonyms business data objects that have the same names but different meanings 3. Improve Data Quality to reduce the need for: Data Cleansing checking data values when data is moved Data Redundancy keeping derived data values Data Duplication keeping multiple copies of data structures and values Data Reconciliation explaining why similar reports contain different data values Database inventory Application inventory 2.3 Scope The business requirements are to develop a firm-wide cross-application Metadata Dictionary that contains the list of the data used to do business, and the definition and properties of that data. The scope of the Metadata Dictionary is: An inventory of all the business data used in the company The applications where the business data is used The applications that automate the business processes The mapping of how the business data is transferred from one application to another The data marts that provide the business intelligence reports 3/1/2017 Page 8

9 The Metadata Dictionary application will provide users access to the metadata-related information. The Metadata Dictionary is used to 2.4 Assumptions 1. record the data definitions used by the business o Descriptions of the data used to do business (Business lexicon) The business data name the data definition, The business definition who is responsible for it (data steward) o The conceptual data model Logical entities o The logical data model Logical attributes The logical data type 2. inventory the processes that create and store the data o the business applications o the implemented databases Implemented tables Implemented columns The physical field name The physical data type 3. document and report the properties of the data o The source of the data o the location of data o which applications use a particular piece of data o ETL mapping how the data has been transformed and transferred between applications. The collection of metadata must be an automated function of the metadata repository so that the metadata is always current. If metadata collection is not automated, then it becomes one of the development documentation tasks and the Metadata Dictionary will not reflect the current IT architecture. 3/1/2017 Page 9

10 2.5 Anticipated Benefits The implicit benefits include improvement of the firm s performance, facilitation of all employees work, and consistency and integrity of information available to all. The benefits of creating a Metadata Dictionary are realized in process improvements in three areas. In the business performance area the benefits are: Alignment of IT business case with business strategy Consistency and integrity of information In the area of OLTP application development, the benefits are: Detailed models provide a common frame of reference across major business areas Up to date inventory of applications and databases provides the foundation for IT strategy In the area of cost avoidance, the benefits are: More reuse lowers software development costs Reduced rework lowers software maintenance costs There are risks associated with not creating, having, and using a Metadata Dictionary. The business has to pay up-front for the right to exercise options in the future. In the past, the business has often settled for cheaper, less permanent solutions, rather than focusing on options that provide increased future maneuverability. Without the Metadata Dictionary, multiple OLTP application will probably never become interoperable or establish and adhere to open and flexible standards. Continuing negative consequences will impact application support's ability to provide flexible services and responses to the business, and respond to unknown and endemic systems integration problems and needs. 3/1/2017 Page 10

11 3. PROCESS ARCHITECTURE 3.1 Introduction This section describes the organization of the processes used to create, update and publish the Metadata Dictionary data. The Metadata Dictionary is available to users through the web and as Microsoft EXCEL files that are turn-around documents. Application architects use the EXCEL files to specify and modify the tables within an application. When the specifications are complete, the EXCEL files are used to update the Metadata Dictionary and to generate data definitions for the development tools. 3/1/2017 Page 11

12 3.2 Metadata Dictionary Process Model Figure2 shows the major functionality required for the Metadata Dictionary application to be successful. Data Architecture Capture Logical Data Model Create Business Reports Business Dictionary Process Improvement Project Capture Business Terms Map Business Terms To Logical Data Model Reverse Engineer Database Metadata Dictionary Create Data Model Reports Create SOX Compliance Reports Metadata Dictionary Compliance And Mappings Implemented Application Database Map Database To Logical Data Model Create Database Reports Database Mappings And Usage Capture ETL Mapping Create ETL Reports Data Lineage Reports Figure 2: Process Model for the Metadata Dictionary 3/1/2017 Page 12

13 3.3 Process Model Components Each symbol in Figure 2 is a separate data source or process that uses the Metadata Dictionary. Each process symbol is described below. The lines show how data moves between components of the system Capture Logical Data Model The Capture Logical Data Model Process enables the logical data model to be loaded into the logical entity and related to the logical data types and business data names. This enables the synchronization of data fields (e.g., business data names, data types, etc.) and the correction of spelling mistakes by the data stewards Capture Business Terms The Capture Business Lexicon Process enables the requirements document to be entered into the Metadata Dictionary by the data stewards. As new applications are developed and existing applications are changed, the technical architects create specifications that contain the business data names used by the applications. These specifications are used as input to the Metadata Dictionary Map Business Terms to Logical Data Model The Map Business Lexicon to Logical Data Model Process enables the data stewards to relate the business process to the logical data model. As business processes evolve, the new understandings need to be captured into the Metadata Dictionary Reverse Engineer Database The Reverse Engineer Database Process enables the implemented database to be entered into the Metadata Dictionary by the data stewards. 3/1/2017 Page 13

14 3.3.5 Map Database to Logical Data Model The Map Implemented Database to Logical Data Model Process enables the data stewards to relate the implemented applications to the logical data model. As SME s gain insight into the implemented applications, the insights need to be captured into the Metadata Dictionary Capture ETL Mapping The Capture ETL (Extract Transform and Load) Mapping Process enables the application architect to map the source of the data to the target tables and columns Create Business Reports The Create Business Lexicon Reports Process extracts the business name data for the business process data stewards. The data stewards can use these reports to validate the contents of the Metadata Dictionary Create Data Model Reports The Create Logical Data Model Reports Process extracts the logical data model entity, attribute and logical data type data for the data stewards, application architects and application SME s Create SOX Compliance Reports The Create SOX (Sarbanes-Oxley) Compliance Reporting Process extracts the SOX data lineage reports required for the legal department Create Database Reports The Create Implemented Database Reports Process extracts the implemented database tables and columns for the application SME s Create ETL Mapping Reports The Create ETL (Extract Transform and Load) Mapping Reports Process extracts the ETL mapping for the application SME s and the data stewards. 3/1/2017 Page 14

15 4. DATA ARCHITECTURE 4.1 Introduction Now we will look at the Metadata Dictionary application from a data point of view. We show the data about the applications, implemented databases and logical entities and the relationships between them. 4.2 Conceptual Data Model The data model organizes the physical databases and connects them to the logical data model and business process lexicon. The data model for Metadata Dictionary application is shown in Figure 3. The data model shows the levels of the data dictionary that are used by different people in the business. The business users enter their understanding of the business terms in the Business Requirements Vocabulary box. The enterprise data architect uses the Data Model Semantics box to document the conceptual data model and the analysis for the Enterprise Logical Data Model. The DBA uses the Enterprise Logical Data Model to document the implemented data warehouse. The data governance group uses the Implemented Databases box to track the data inventory of the business and the PMO uses the Implemented Applications box to track the functional inventory of the business. The right hand vertical column of the data model diagram shows the entities involved with tracking the lineage of a column of data used in an implemented application. 3/1/2017 Page 15

16 Data Modeling Metadata Inventory Business Requirements Vocabulary Business Data Term Thesaurus Business Data Term Definition Business Data Term Data Model Semantics Conceptual Entity Logical Attribute Logical Attribute Resolution Enterprise Logical Data Model Table Relationship Table Table Column IT Inventory Implemented Databases Database Database Implementaton Database Table Database Table Column Implemented Applications Application Application Implementation Application Function Application Field Figure 3: Conceptual Data Model for the Metadata Dictionary 3/1/2017 Page 16

17 4.3 Data Model Components Each symbol in Figure 3 is a separate entity. Each entity is described below. The lines show the relationships between the entities Business Requirements Vocabulary Business Data Term The business data name (business lexicon) is the entity that contains the list of all of the terms used by the business to describe the data used and collected by the automated business processes. The business data name is a set of tables that contains all of the data (identification and detailed description) of all the business jargon collected by the IT organization during the requirements gathering step of business process automation. Business Data Term Thesaurus The logical entity is the entity that contains the list of all the entities used to construct the logical data model of the business processes. The list of logical entities contains all of the data (identification and detailed description) of all the logical entities used by the IT organization in modeling the business processes. Business Data Term Definition The logical entity is the entity that contains the list of all the entities used to construct the logical data model of the business processes. The list of logical entities contains all of the data (identification and detailed description) of all the logical entities used by the IT organization in modeling the business processes. 3/1/2017 Page 17

18 4.3.2 Data Model Semantics Entity The logical entity is the entity that contains the list of all the entities used to construct the conceptual data model of the business processes. The list of logical entities contains all of the data (identification and detailed description) of all the logical entities used by the IT organization in modeling the business processes. Logical Attribute The logical attribute is the entity that contains the list of all the attributes used to construct the logical entity pattern for each logical entity. Logical Attribute Resolution The logical entity is the entity that contains the list of all the entities used to construct the logical data model of the business processes. The list of logical entities contains all of the data (identification and detailed description) of all the logical entities used by the IT organization in modeling the business processes. 3/1/2017 Page 18

19 4.3.3 Enterprise Logical Data Model Table The table is the entity that contains the list of all the tables used to construct the logical enterprise data model of the business processes. The list of tables contains all of the data (identification and detailed description) of all the tables used by the IT organization in modeling the business processes. The enterprise logical data model organizes the use of business data in the same way that the chart of accounts organizes the business money and the organizational chart organizes the business processes. Table Column The table column is the entity that contains the list of all the columns used to construct the tables of logical enterprise data model. The list of columns contains all of the data (identification and detailed description) of all the columns used by the IT organization in modeling the business processes. Table Relationship The table relationship the entity that contains the list of all the relationships between the tables used to construct the logical enterprise data model. The list of table relationships contains all of the data (identification and detailed description) of all the relationships used by the IT organization in modeling the business processes. 3/1/2017 Page 19

20 4.3.4 Implemented Databases Database The database is the entity that contains the list of all the databases used by the applications. The list of databases contains all of the data (identification and detailed description) of all the databases used by the IT organization in processing data and generating reports. Database Implementation The implemented database is the entity that contains the list of the physical implementations of a database. A database can be implemented for a variety of purposes, such as development, QA, staging, production or hot backup. The implemented database contains all of the data (identification and detailed description) of the databases used by the IT organization. Database Table The physical table entity is a set of tables that contains all of the data (identification and detailed description) of all the physical tables and/or files contained in an implemented database. Although each implementation is of the same database, the implementation s list of tables can be different in structure or in name. Database Table Column The ETL mapping is the entity that contains all of the data (identification and detailed description) of all the transformations that took place to create the record that is stored in the physical table. The ETL Mapping tells you the list of source columns that contained the data that was aggregated or transformed to create the value in the target column. 3/1/2017 Page 20

21 4.3.5 Implemented Applications Application The application is the entity that contains the list of all the application systems installed by the IT organization to automate business processes. The list of applications contains all the data, identification and names of the applications as recognized by the end users, for example, Accounts Receivable, Material Movement, etc. Application Implementation The application is the entity that contains the list of all the application systems installed by the IT organization to automate business processes. The list of applications contains all the data, identification and names of the applications as recognized by the end users, for example, Accounts Receivable, Material Movement, etc. Application Function The application is the entity that contains the list of all the application systems installed by the IT organization to automate business processes. The list of applications contains all the data, identification and names of the applications as recognized by the end users, for example, Accounts Receivable, Material Movement, etc. Application Field The application is the entity that contains the list of all the application systems installed by the IT organization to automate business processes. The list of applications contains all the data, identification and names of the applications as recognized by the end users, for example, Accounts Receivable, Material Movement, etc. 4.4 Database Management System IT will provide a reference to the current database management system to be used. 3/1/2017 Page 21

22 5. PHYSICAL ARCHITECTURE 5.1 Introduction The Physical Architecture describes the hardware components and the connectivity that is required to implement the Metadata Dictionary application. 5.2 Hardware Architecture The Hardware Architecture describes how the Metadata Dictionary application organizes its functionality on the workstations and servers. The hardware architecture is based on the separation of the processing architecture into three layers: The presentation layer is implemented in the browser and Web server The business application layer is implemented in the Metadata Dictionary Application Server The data access layer and the database layer are implemented in the Data Base Server IT will provide a reference to the current architecture standards to be used. 3/1/2017 Page 22

23 6. APPENDIX 6.1 Definitions, Acronyms and Abbreviations Architecture - a description of the structure of connections (or interfaces) among a collection of technology components. An architecture consists of major software subsystems and their interaction methods, major data structures, databases, and architecture types. An architecture specifies a set of rules or structures providing a framework for the overall design of an application, system or product. Its purpose is to ensure flexibility and adaptability. A well-architected solution features loose coupling among well-encapsulated components so that one part can be easily replaced without major disruption of other connected parts. A Data Architecture provides a framework by identifying and understanding how the data will move throughout the corporation. Data Datum - something known or assumed as fact and made the basis of reasoning or calculation. Data Mart - a subset or selective summary of a data warehouse. Mathematically, a data mart is a projection of the Data Warehouse. Data marts are community - specific data stores that focus on DSS end-user requirements. Data marts present only the data that an end-user constituency requires in a form close to the constituency business model. Data Mining - is the process of looking in a data base to find hidden patterns without a predetermined idea or hypothesis about what the pattern might be. Data Model is an abstraction that groups the data required by an application into structures, defines the detailed organization of the structures and ensures that the structures obey certain consistency and relationship rules, not business rules. Data Modeling is a means of describing the organization of and relationships within the data of a current or planned application, system or enterprise. Data modeling uses entity relationship diagrams that show entities as box symbols and relationships as connectors between the symbols. The entity types are: Entity (fundamental) some real world object that you need to model. Associative Entity is a relationship about which you want to store information. It is the resolution of a many to many relationship. It can only exist between two (or more) other entities. Attributive Entity is used to show data that is wholly dependent upon the existence of a fundamental entity. It is also used to show repeating subgroups of data. Data Surfing - The process of iteratively calculating metrics over a subset of the available data by perusing combinations of one or more dimensions in an attempt to find trends or anomalies. 3/1/2017 Page 23

24 Data Warehouse - is an analytical data base that is used as the foundation of a decision support system. It is designed for large volumes of read only data. It provides intuitive access to information that will be used in making decisions. A data warehouse is an integrated system of hardware, software and network technologies designed to convert operational data into accessible business information. A Data Warehouse contains the historical transactions of the business. It presents a consistent view of the business over time. AKA information warehouse. A data warehouse is an object-oriented, integrated, time-variant, nonvolatile collection of data in support of management s decision-making process - W.H. Inmon. Data warehousing can be thought of as an automated version of the Information Center. It provides the link to operational data that end users need to make critical business decisions - Ken Orr Data warehousing is the consolidation of data from multiple sources into a query data base - Herb Edelstein Data Warehousing is the process that moves operational business data into the business analyst s DSS, EIS, Data Mining or OLAP application. The applications are based on a hypercube data model implemented as multi-dimensional data bases. Decision Support System (DSS) - A system used to support managerial decisions. Usually DSS involves the analysis of many units of data in a heuristic fashion. As a rule, DSS processing does not involve the update of data. DSS is a collection of one or more predefined reports or analyses. These are developed in advance by an application developer or a power user. These applications use data from the data warehouse. DSS is an integrated management information and planning system that allows users to integrate, analyze and predict the impact of decisions before they are made. Dimension - A conceptual qualifier that provides the context or meaning for a metric, i.e. product, customer, employee, time period, etc. If many fact tables share a dimension, it is exactly the same dimension in R. Kimball and K. Strehlo, Why decision support fails and how to fix it, Datamation, June 1, 1994, vol. 4, no 11, p.p Drill - As in drilling, drill-down, drill-across, drill-up. The process of focusing on a particular level of data and accessing the next level of data below that. For example, if certain districts make-up regions, drilling on a particular region would reveal data about the individual districts that make-up that region. Drilling is usually associated with dimensions. Executive Information System (EIS) - a set of tools that make it easy to query and analyze information. A system designed for executives, featuring trend analysis and high level reviews. 3/1/2017 Page 24

25 Highly Summarized - Data calculated at the higher level in a dimension s hierarchy several steps away from the detail source data; sometimes highly summarized data sources are lightly summarized data. Information Knowledge - intelligence or news, something not known previously. theoretical or practical understanding of an art, science, language, technology, etc. Lightly Summarized - Data calculated at the lowest level in a dimension hierarchy, about one or two steps from the detail source data. Metadata (data about data, e.g. where it came from, when it was updated, how was it calculated). It provides information about the data structures and relationships between the data structures within and between data bases. The metadata of interest in a data warehouse environment is metadata about operational data mapped to the data warehouse and data warehouse to decision support. This is metadata about transformations, original source system names and maps, etc. -- an information genealogy. Multidimensional Data Base (MDD) a very denormalized form of summary data rolled up to a level of interest defined by users. Allows users to view any combination of dimensions and see how some piece of data is related to all the other data. A database designed around a set of dimensions that can be used in decision-support analysis. Multidimensional Data Bases use their own (proprietary) data stores, data is preaggregated, summarized or precalculated in some way. A computer based software system used to establish and manage multidimensional databases An MDD is constructed as an 'n' dimensional hypercube of data cells. For example, a classification schema is a hypercube of one dimension and a spreadsheet is a hypercube of two dimensions. Each dimension of the hypercube represents a category of interest for the decision support analyst. Multidimensional conceptual view - a user s view of the enterprise is multidimensional in nature. In E. F. Codd, S. B. Codd, and C. T. Salley, Beyond Decision Support, Computerworld, July 26, 1993, pp Object-Oriented Database Management System (ODBMS) - A computer based software system used to establish and manage object-oriented databases. OLAP (On-Line Analytical Processing) - is data synthesis, analysis and consolidation. This environment supports analytical queries against data representing the organization s state at a specific point in time. On-line Analytical Processing is a technology that presents a multidimensional, logical view of data to the end user with no requirements as to how the data is stored. It sorts, forecasts, tracks trends and performs other complex analyses and lets users move from one query to another. It lets users get results quickly and easily. 3/1/2017 Page 25

26 OLTP (On-Line Transaction Processing) - The OLTP environment is transactional in nature, requiring access to current data, in the operational environment. Relational Database Management System (RDBMS) - A computer based software system used to establish and manage relational databases. Semantic Data Model - a semantic data model is a description of the names and properties of all the data objects and relationships existing in an application s world. A view of data that focuses on the meaning of data and its context. Semantic modeling moves through a number of phases, each phase defining more detail of the model. Conceptual Data Model Logical Data Model Physical Data Model Implemented Data Base Stoll s Law: Volume(data) > Volume(information) > Volume(knowledge) > Volume(wisdom) i.e., reasoning on data produces information; knowledge about a problem produces a model; a model and information produces wisdom Subject Oriented Data Base provides a stable image of business data independent of operational systems. It captures and reflects a subset of the basic nature of the business environment. A subject area is a dimension of the data warehouse. Wisdom a capacity of judging rightly in matters relating to life and conduct. Good judgment comes from experience, experience come from bad judgment 3/1/2017 Page 26

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