Overview of Enterprise Data Architecture – What's in YOUR Data
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Enterprise Architecture
Enterprise Architecture The Zachman Framework: Intro to Sample Models © 1990-2011 John A. Zachman, Zachman International® Observation Enterprises are COMPLEX The US Pentagon GM Plant © 1990-2011 John A. Zachman, Zachman International® Agenda I. Enterprise Models from Literature II. Implementation Discussion III. Architecture Discussion IV. Column 1 Model Samples V. Row 1 Model Samples VI. Column 2 Model Samples VII.Etc., Etc. ‘till Time Is Up © 1990-2011 John A. Zachman, Zachman International® Models on my Bookshelf 1. "Requirements Analysis" by David C. Hay Activity Management A Complete Sarson and Gane Data Flow Diagram Physical Asset Value Constraints 2. "Information Modeling and Relational Databases" by Terry Halpin and Tony Morgan IT Company Schema and University Schema 3. "Enterprise Architecture for Integration" by Clive Finkelstein Strategic Model for sample solution Order entry data map with all attributes 5BNF data map - ORG and ROLE STRUCTURES 4. "Designing Quality Databases with IDEF1X Information Models" by Thomas A. Bruce Case Study Supplementary Material (Logical Data Model) 5. "Business Process Management"by Roger T. Burlton The Scope for Global Software Human Resources © 1990-2011 John A. Zachman, Zachman International® Models on my Bookshelf 6. "Enterprise Architecture at Work" by Marc Langhorst Services provided by Handle Claims Process Handle Claims and IT Support 7. "Business Process Engineering" by August Scheer ERM for Human Resource Planning Event-driven process chain for inbound logistics 8. "Data Model Resource -
Enterprise Architecture As Explanatory Information Systems Theory for Understanding Small- and Medium-Sized Enterprise Growth
sustainability Article Enterprise Architecture as Explanatory Information Systems Theory for Understanding Small- and Medium-Sized Enterprise Growth Aurona Gerber 1,2,* , Pierre le Roux 1,3 and Alta van der Merwe 1 1 Department of Informatics, University of Pretoria, 0083 Pretoria, South Africa; [email protected] (P.R.); [email protected] (A.v.d.M.) 2 CAIR, Center for Artificial Intelligence Research, 0083 Pretoria, South Africa 3 Moyo, 0157 Centurion, South Africa * Correspondence: [email protected] Received: 31 July 2020; Accepted: 7 October 2020; Published: 15 October 2020 Abstract: Understanding and explaining small- and medium-sized enterprise (SME) growth is important for sustainability from multiple perspectives. Research indicates that SMEs comprise more than 80% of most economies, and their cumulative impact on sustainability considerations is far from trivial. In addition, for sustainability concerns to be prioritized, an SME has to be successful over time. In most developing countries, SMEs play a major role in solving socio-economic challenges. SMEs are an active research topic within the information systems (IS) discipline, often within the enterprise architecture (EA) domain. EA fundamentally adopts a systems perspective to describe the essential elements of a socio-technical organization and their relationships to each other and to the environment in order to understand complexity and manage change. However, despite rapid adoption originally, EA research and practice often fails to deliver on expectations. In some circles, EA became synonymous with projects that are over-budget, over-time and costly without the expected return on investment. In this paper, we argue that EA remains indispensable for understanding and explaining enterprises and that we fundamentally need to revisit some of the applications of EA. -
Powerdesigner 16.6 Data Modeling
SAP® PowerDesigner® Document Version: 16.6 – 2016-02-22 Data Modeling Content 1 Building Data Models ...........................................................8 1.1 Getting Started with Data Modeling...................................................8 Conceptual Data Models........................................................8 Logical Data Models...........................................................9 Physical Data Models..........................................................9 Creating a Data Model.........................................................10 Customizing your Modeling Environment........................................... 15 1.2 Conceptual and Logical Diagrams...................................................26 Supported CDM/LDM Notations.................................................27 Conceptual Diagrams.........................................................31 Logical Diagrams............................................................43 Data Items (CDM)............................................................47 Entities (CDM/LDM)..........................................................49 Attributes (CDM/LDM)........................................................55 Identifiers (CDM/LDM)........................................................58 Relationships (CDM/LDM)..................................................... 59 Associations and Association Links (CDM)..........................................70 Inheritances (CDM/LDM)......................................................77 1.3 Physical Diagrams..............................................................82 -
Study of Implementing Zachman Framework for Modeling Information Systems for Manufacturing Enterprises Aggregate Planning
Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management Kuala Lumpur, Malaysia, January 22 – 24, 2011 Study of Implementing Zachman Framework for Modeling Information Systems for Manufacturing Enterprises Aggregate Planning Radwan, A., and Majid Aarabi Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, MALAYSIA [email protected] , [email protected] Abstract Response time on orders of customers is becoming a critical issue to achieve a competitive advantage. A major problem is a lack of strategic perspective of information systems. A need for adequate tools and methodologies to model the system structure and to define integrated requirements is a must for the manufacturing enterprises. This paper presents a study of implementing Zachman Framework ZF to model information systems for aggregate planning activities within the manufacturing enterprises. An Information System for simulating the aggregate planning activities such as evaluating the various planes for determining the production rate, inventory levels, subcontracting and the required human resources is modeled. This is assumed to provide control over the order processes and reporting the orders status online. It is assumed that the customer can have access to the system to follow up with his order progressing status. Managers would have control on orders transactions, and workshop floor supervisors could monitor and control the orders processing online. Some artifacts are introduced to ZF to enhance its capability for modeling of information systems for Aggregate Planning in Manufacturing Enterprises. Keywords: Manufacturing Information Systems, Zachman Framework, Enterprise architecture, Aggregate planning, Modeling. 1. Introduction Nowadays, the enterprises encounter with numerous amount of data. -
Data Model Standards and Guidelines, Registration Policies And
Data Model Standards and Guidelines, Registration Policies and Procedures Version 3.2 ● 6/02/2017 Data Model Standards and Guidelines, Registration Policies and Procedures Document Version Control Document Version Control VERSION D ATE AUTHOR DESCRIPTION DRAFT 03/28/07 Venkatesh Kadadasu Baseline Draft Document 0.1 05/04/2007 Venkatesh Kadadasu Sections 1.1, 1.2, 1.3, 1.4 revised 0.2 05/07/2007 Venkatesh Kadadasu Sections 1.4, 2.0, 2.2, 2.2.1, 3.1, 3.2, 3.2.1, 3.2.2 revised 0.3 05/24/07 Venkatesh Kadadasu Incorporated feedback from Uli 0.4 5/31/2007 Venkatesh Kadadasu Incorporated Steve’s feedback: Section 1.5 Issues -Change Decide to Decision Section 2.2.5 Coordinate with Kumar and Lisa to determine the class words used by XML community, and identify them in the document. (This was discussed previously.) Data Standardization - We have discussed on several occasions the cross-walk table between tabular naming standards and XML. When did it get dropped? Section 2.3.2 Conceptual data model level of detail: changed (S) No foreign key attributes may be entered in the conceptual data model. To (S) No attributes may be entered in the conceptual data model. 0.5 6/4/2007 Steve Horn Move last paragraph of Section 2.0 to section 2.1.4 Data Standardization Added definitions of key terms 0.6 6/5/2007 Ulrike Nasshan Section 2.2.5 Coordinate with Kumar and Lisa to determine the class words used by XML community, and identify them in the document. -
Integration Definition for Function Modeling (IDEF0)
NIST U.S. DEPARTMENT OF COMMERCE PUBLICATIONS £ Technology Administration National Institute of Standards and Technology FIPS PUB 183 FEDERAL INFORMATION PROCESSING STANDARDS PUBLICATION INTEGRATION DEFINITION FOR FUNCTION MODELING (IDEFO) » Category: Software Standard SUBCATEGORY: MODELING TECHNIQUES 1993 December 21 183 PUB FIPS JK- 45C .AS A3 //I S3 IS 93 FIPS PUB 183 FEDERAL INFORMATION PROCESSING STANDARDS PUBLICATION INTEGRATION DEFINITION FOR FUNCTION MODELING (IDEFO) Category: Software Standard Subcategory: Modeling Techniques Computer Systems Laboratory National Institute of Standards and Technology Gaithersburg, MD 20899 Issued December 21, 1993 U.S. Department of Commerce Ronald H. Brown, Secretary Technology Administration Mary L. Good, Under Secretary for Technology National Institute of Standards and Technology Arati Prabhakar, Director Foreword The Federal Information Processing Standards Publication Series of the National Institute of Standards and Technology (NIST) is the official publication relating to standards and guidelines adopted and promulgated under the provisions of Section 111 (d) of the Federal Property and Administrative Services Act of 1949 as amended by the Computer Security Act of 1987, Public Law 100-235. These mandates have given the Secretary of Commerce and NIST important responsibilities for improving the utilization and management of computer and related telecommunications systems in the Federal Government. The NIST, through its Computer Systems Laboratory, provides leadership, technical guidance, -
Data Analytics EMEA Insurance Data Analytics Study Contents
A little less conversation, a lot more action – tactics to get satisfaction from data analytics EMEA Insurance data analytics study Contents Foreword 01 Introduction from the authors 04 Vision and strategy 05 A disconnect between analytics and business strategies 05 Articulating a clear business case can be tricky 07 Tactical projects are trumping long haul strategic wins 09 The ever evolving world of the CDO 10 Assets and capability 12 Purple People are hard to find 12 Data is not always accessible or trustworthy 14 Agility and traditional insurance are not natural bedfellows 18 Operationalisation and change management 23 Operating models have no clear winner 23 The message is not always loud and clear 24 Hearts and minds do not change overnight 25 Are you ready to become an IDO? 28 Appendix A – The survey 30 Appendix B – Links to publications 31 Appendix C – Key contacts 32 A little less conversation, a lot more action – tactics to get satisfaction from data analytics | EMEA Insurance data analytics study Foreword The world is experiencing the fastest pace of data expansion and technological change in history. Our work with the World Economic Forum in 2015 identified that, within financial services, insurance is the industry which is most ripe for disruption from innovation owing to the significant pressure across the value chain. To build on this work, our report ‘Turbulence ahead – The future of general insurance’, set out various innovations transforming the industry and subsequent scenarios for The time is now the future. It identified that innovation within the insurance industry is no longer led by insurers themselves. -
Metamodeling and Method Engineering with Conceptbase”
This is a pre-print of the book chapter M. Jeusfeld: “Metamodeling and method engineering with ConceptBase” . In Jeusfeld, M.A., Jarke, M., Mylopoulos, J. (eds): Metamodeling for Method Engineering, pp. 89-168. The MIT Press., 2009; the original book is available from MIT Press http://mitpress.mit.edu/node/192290 This pre-print may only be used for scholar, non-commercial purposes. Most of the sources for the examples in this chapter are available via http://merkur.informatik.rwth-aachen.de/pub/bscw.cgi/3782591 for download. They require ConceptBase 7.0 or later available from http://conceptbase.cc. Metamodeling and Method Engineering with ConceptBase Manfred Jeusfeld Abstract. This chapter provides a practical guide on how to use the meta data repository ConceptBase to design information modeling methods by using meta- modeling. After motivating the abstraction principles behind meta-modeling, the language Telos as realized in ConceptBase is presented. First, a standard factual representation of statements at any IRDS abstraction level is defined. Then, the foundation of Telos as a logical theory is elaborated yielding simple fixpoint semantics. The principles for object naming, instantiation, attribution, and specialization are reflected by roughly 30 logical axioms. After the language axiomatization, user-defined rules, constraints and queries are introduced. The first part is concluded by a description of active rules that allows the specification of reactions of ConceptBase to external events. The second part applies the language features of the first part to a full-fledged information modeling method: The Yourdan method for Modern Structured Analysis. The notations of the Yourdan method are designed along the IRDS framework. -
Constructing a Meta Data Architecture
0-471-35523-2.int.07 6/16/00 12:29 AM Page 181 CHAPTER 7 Constructing a Meta Data Architecture This chapter describes the key elements of a meta data repository architec- ture and explains how to tie data warehouse architecture into the architec- ture of the meta data repository. After reviewing these essential elements, I examine the three basic architectural approaches for building a meta data repository and discuss the advantages and disadvantages of each. Last, I discuss advanced meta data architecture techniques such as closed-loop and bidirectional meta data, which are gaining popularity as our industry evolves. What Makes a Good Architecture A sound meta data architecture incorporates five general characteristics: ■ Integrated ■ Scalable ■ Robust ■ Customizable ■ Open 181 0-471-35523-2.int.07 6/16/00 12:29 AM Page 182 182 Chapter 7 It is important to understand that if a company purchases meta data access and/or integration tools, those tools define a significant portion of the meta data architecture. Companies should, therefore, consider these essential characteristics when evaluating tools and their implementation of the technology. Integrated Anyone who has worked on a decision support project understands that the biggest challenge in building a data warehouse is integrating all of the dis- parate sources of data and transforming the data into meaningful informa- tion. The same is true for a meta data repository. A meta data repository typically needs to be able to integrate a variety of types and sources of meta data and turn the resulting stew into meaningful, accessible business and technical meta data. -
Examining Capabilities As Architecture
September 2013 BPTrends ▪ Examining Capabilities as Architecture Examining Capabilities as Architecture Ralph Whittle Introduction This Article is a direct response to one written by Mike Rosen titled, “Are Capabilities Architecture?” [1] published in February 2013 by BPTrends It offers a different and contrasting point of view on accepting capability modeling and mapping as “architecture.” Business Architecture (BA) approaches and methods, while still evolving, have at least reached a point of maturity where the enterprise can fairly assess how it will develop and advance this initiative. As with any emerging field, a variety of approaches and methods will develop and enjoy success. From an historical perspective, consider the number of Business Process Management (BPM), Enterprise Architecture (EA) and Service-Oriented Architecture (SOA) approaches that have matured over the years. And no doubt, the same will eventually occur with the Business Architecture over the next few years. However, just as with the BPM, EA and SOA some different and contrasting points of view will find their way into the various Business Architecture approaches, techniques and methods. Today, at least two different and contrasting organizing principles are considered for Business Architecture. One is “capability centric” and the other is “process centric.” This article advocates and supports the “process centric” organizing principle, specifically using a value stream which is an end-to-end collection of activities that creates a result for a “customer,” who may be the ultimate customer or an internal “end user” of the value stream. The value stream has a clear goal: to satisfy (or, better, to delight) the customer.[2] This is a well known term, familiar in Six Sigma, Lean Manufacturing and BPM approaches. -
Enterprise Architecture 1 Zachman Framework
member of The Zachman Framework Prof. Dr. Knut Hinkelmann Introduction to Business-IT Alignment and Enterprise Architecture 1 Zachman Framework ■ Regarded the origin of enterprise architecture frameworks (originally called "Framework for Information Systems Architecture") ■ First version published in 1987 by John Zachman ■ It is still further developed by Zachman International (http://www.zachman.com) ■ Often referenced as a standard approach for expressing the basic elements of enterprise architecture Zachman, J.A., 1987. A framework for information systems architecture. IBM Systems Journal, 26(3). Prof. Dr. Knut Hinkelmann Enterprise Architecture Frameworks 2 Rationale of the Zachman Architecture ■ There is not a single descriptive representation for a complex object ... there is a SET of descriptive representations. ■ Descriptive representations (of anything) typically include: ♦ Perspectives Abstractions ♦ Abstractions Perspectives (Zachman 2012) Prof. Dr. Knut Hinkelmann Enterprise Architecture Frameworks 3 Dimension 1 – Perspectives Zachman originally used the analogy of classical architecture For the different stakeholders different aspects of a building are relevant - models of the building from different perspectives Bubble charts: conceptual representation delivered by the architect Architect's drawing: transcription of the owner's perceptual requirements – owner's perspective Architect's plans: translation of the owner's requirements into a product – designer's perspective Contractor's plans: phases of operation, architect's plans contrained by nature and technology – builder's perspective Shop plans: parts/sections/components of building details (out-of-context specification) – subcontractor's perspective The building: physical building itself (Zachman 1987) Prof. Dr. Knut Hinkelmann Enterprise Architecture Frameworks 4 Dimension 1: Architectural Representations with analogies in Building and Information Systems (Zachman 1987) Prof. -
Using Telelogic DOORS and Microsoft Visio to Model and Visualize Complex Business Processes
Using Telelogic DOORS and Microsoft Visio to Model and Visualize Complex Business Processes “The Business Driven Application Lifecycle” Bob Sherman Procter & Gamble Pharmaceuticals [email protected] Michael Sutherland Galactic Solutions Group, LLC [email protected] Prepared for the Telelogic 2005 User Group Conference, Americas & Asia/Pacific http://www.telelogic.com/news/usergroup/us2005/index.cfm 24 October 2005 Abstract: The fact that most Information Technology (IT) projects fail as a result of requirements management problems is common knowledge. What is not commonly recognized is that the widely haled “use case” and Object Oriented Analysis and Design (OOAD) phenomenon have resulted in little (if any) abatement of IT project failures. In fact, ten years after the advent of these methods, every major IT industry research group remains aligned on the fact that these projects are still failing at an alarming rate (less than a 30% success rate). Ironically, the popularity of use case and OOAD (e.g. UML) methods may be doing more harm than good by diverting our attention away from addressing the real root cause of IT project failures (when you have a new hammer, everything looks like a nail). This paper asserts that, the real root cause of IT project failures centers around the failure to map requirements to an accurate, precise, comprehensive, optimized business model. This argument will be supported by a using a brief recap of the history of use case and OOAD methods to identify differences between the problems these methods were intended to address and the challenges of today’s IT projects.