Enterprise Information Architecture

First Arab Conference on Information Science “Information Architecture” – IA2015

Enterprise Information Architecture: Concepts and applications Mohammad Ibraheem Ahmad IT PhD Researcher, M.S., MAIS Alexandria Univ., Egypt, PGD IT, Amity Univ. India. E-mail : [email protected]

Introduction

Enterprise Information Architecture Concept

Enterprises tend to be made up of a number of domains, each with their own set of information semantics, goals and characteristics defined at both the business level and application level. The business level, at the top of the semantic information model, includes the enterprise vocabulary and concepts that describe the interactions between the people and systems involved in business processes. The application level is part of the internal value chain of an enterprise. Activities at this level tend to be disjointed and are as such regarded as a set of independent domains, in which each application manages its information, tailored to its particular needs [1]. Architecture is defined as high-level planning that shows the overall shape of things to come. Architects design or redesign the overall environment. [5]. In this essence , the term architecture is used in a broad way and there are different types of architectures such as Business Architecture, Application Architecture, Information Architecture, Infrastructure Architecture, Integration Architecture, Operational Architecture, Security Architecture, and Network Architecture. All of these architectures address specific situations or problems to be solved within an enterprise and are thus related in some way to the overall Enterprise Architecture [6]. Enterprise Architecture provides the alignment across business strategy, IT strategy, and IT implementation. It tightly integrates the business and IT strategies to create an ongoing way to use IT to sustain and grow the business [6].

Enterprise information architecture (EIA) is quite simply, the practice of information in the enterprise setting [9]. Gartner defines Enterprise Information Architecture as that part of the enterprise architecture process that describes — through a set of requirements, principles and models — the current state, future state, and guidance necessary to flexibly share and exchange information assets to achieve effective enterprise change. [10] Enterprise Information Architecture is the framework that defines the information-centric principles, architecture models, standards, and processes that form the basis for making information technology decisions across the enterprise. EIA translates the business requirements into informational strategies and defines what data components are needed by whom and when in the information supply chain. Furthermore, it addresses the need of the business to generate and maintain trusted information that is derived by relevant data components [6]. EIA, as a component of enterprise architecture, provides the plan to enable organizations achieve business strategies by flexibly sharing and exchanging information assets for advantage. [10]. An enterprise-wide information architecture should define a model for working with disparate business units in a manner that presents employees and customers alike with a unified way of accessing information across the whole company [3]. EIA is one of the three primary viewpoints in enterprise architecture (along with the enterprise business architecture and enterprise technical architecture). Each viewpoint includes multiple levels of abstraction and specificity. The minimum levels of abstraction for EIA are conceptual, logical and implementation [10]. The EIA is viewed as a structured set of interrelated elements, represented as a pyramid, that support all information processes [14]. The EIA is a core component of the required framework for effective decision making by defining the guiding principles that dictate the organization’s strategy to address business needs and the information-centric technology infrastructure that supports them. The EIA defines the technical capabilities and processes the organization needs to manage data and information over its lifetime, optimize content-based operational and compliance processes, establish, govern and deliver trusted information, and optimize business performance [6]. Types of EIA deliverables are requirements, principles and models [10].

Enterprise Vs Information Architecture An enterprise defines the information it needs to run a profitable business as well as the characteristics of that information (static, structured, event-driven, real-time, transactional or streamed) [1]. Enterprise information needs to be relevant, available and all parts of an organization need to share a common understanding of it. As the significance of enterprise information and business agility rises, an information architecture that can capitalize on the changing nature of information, how it is generated, and how it is consumed, is an important enabler for business evolution and growth [1]. Enterprise Architecture Is an architecture in which the system in question is the whole enterprise, especially the business processes, technologies, and information systems of the enterprise [12]. IBM define Enterprise Architecture as ―A tool that links the business mission and strategy of an organization to its IT strategy. It is documented using multiple architectural models that meet the current and future needs of diverse user populations, and it must adapt to changing business requirements and technology‖ [6]. Enterprise information architecture must be viewed as part of the overall enterprise architecture (EA), which provides the structure and discipline required to align an organization’s business operations, organizations and information technologies in support of its business goals and strategies [13]. Enterprise Architecture provides a framework for the business to add new applications, infrastructure, and systems for managing the lifecycle and the value of current and future environments [6]. Enterprise Architecture Methodology Architecture methodology field has several leading methods developed by governments and other large institutions [6]. Perhaps 90 percent of the field use one of these four methodologies [12]:  The Open Group Architectural Framework (TOGAF)—divides an EA into four categories: Business architecture, Application architecture, Data architecture, and Technical architecture  The Zachman Framework for Enterprise Architectures—proposed six descriptive foci (data, function, network, people, time, and motivation) and six player perspectives (planner, owner, designer, builder, subcontractor, and enterprise) which can be arranged in a grid of 36 cells.  The Federal Enterprise Architecture—simply consisting of five reference models: Business, Components, Technical, Data, and Performance.  The Gartner Methodology (Meta Framework)—EA is about bringing together three constituents: business owners, information specialists, the technology implementers. Each of these methodologies dedicates a significant part of its content to the creation of an Information Architecture and an EIA as a core component of an EA [6] On the other hand, Business success depends on effective information architecture [13]. Information architecture is a single, shared and stable information environment that is trustworthy and can be used by all the applications of an enterprise. It separates functionality from information, hides integration aspects, and is responsible for storage and persistence [1]. Developing effective information architectures at the enterprise level presents another dimension altogether [3]. An information-architecture model provides enterprises with centralized and shared set of information services and functions, making the vital connec-tion between information and business processes – the key to flexibility [1]. New characteristics for an information-enabled enterprise empower it to combine vast amounts of structured and unstructured information in new ways, integrate it, analyze it, and deliver it to decision-makers in powerful new formats and timeframes, and give the organization a line of sight to see the future and anticipate change. [6]. Enterprise Information Architecture describes the Information layer from an architecture point of view [6]. Information Architecture Guiding Principles The following principles help guide the design of IA structures [2]: 1. The principle of objects – Treat content as a living, breathing thing, with a lifecycle, behaviors and attributes. 2. The principle of choices – Create pages that offer meaningful choices to users, keeping the range of choices available focused on a particular task. 3. The principle of disclosure – Show only enough information to help people understand what kinds of information they’ll find as they dig deeper. 4. The principle of exemplars – Describe the contents of categories by showing examples of the contents. 5. The principle of front doors – Assume at least half of the website’s visitors will come through some page other than the home page. 6. The principle of multiple classification – Offer users several different classification schemes to browse the site’s content. 7. The principle of focused navigation – Don’t mix apples and oranges in your navigation scheme. 8. The principle of growth – Assume the content you have today is a small fraction of the content you will have tomorrow. TEMPLATE DESIGN © 2008

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Enterprise Information Architecture Principles Enterprise information architecture starts with the high-level business definitions and descriptions, setting standards for data throughout the organization [5]. Alignment, agility and architecture are the goals. Metadata is the key, and models feed the metadata repositories used to achieve the goals. However, models also provide abstraction to simplify complexity, increase understanding through visual representations and provide governance to increase consistency and reusability throughout the organization. As the need to align business and IT increases, the need for more levels of abstraction increases [5]. EIA Goal In fact, the goal of EIA is no different than any other flavor of IA: identify the few most efficient means of connecting users with the information they need most [9]. The primary goal of the EIA is to reduce complexity and thereby contribute to the elimination of all the factors that act as the inhibitors to change and address new business paradigms. [6] EIA Characteristics Primary characteristics that can be used to distinguish a well-defined EIA implementation include the following [6]: Gaining transparency—The information remains independent from application specifications, application implementations, and user interfaces. It provides a transparency layer between the information and application domains. Considering enterprise business requirements—The architecture takes into account the overall information needs of the entire enterprise and specific LOBs or individual organizations. Avoiding inconsistencies—It helps identify inconsistencies, conflicts, overlaps, and gaps in the data and information, and offers a concept, framework, and methods to resolve this, and it is useful to select adequate solutions. Major foundational components or building blocks—They help to describe an end-to-end architecture solution. Common language—It simplifies communication when talking about systems of a given type. Framework—The Reference Architecture is a framework for scope identification, roadmap definition, risk assessment, and gap assessment. Foundation—It is a proven foundation for all solution designs in a domain (e.g., e-business solutions). To capture the complete picture of EIA we must 1) define and describe business processes and map the data definitions to conceptual data models, 2) transform the conceptual data models into logical and/or physical data models and 3) store all this metadata together in a single repository [5]. What is missing are the details about the data itself, and how it has been moved and transformed between systems. if we know the sources, the destinations and the path, and we store that information together with the conceptual logical and physical data models, we have the complete depth and breadth of the enterprise information architecture collected and collated into something very useful. [5]

Designing Enterprise Information Architecture There's no "right way" to design enterprise information architecture. However, the wide range of possible IA design components was summed up into four categories [9]: Top-Down Navigation and EIA—1.Bypass the main page; 2.Repurpose your sitemap; 3.Slim down your site index; and 4.Develop guides Bottom-Up Navigation and EIA—1.Build single-silo content models; 2.Limit dependence on metadata; and 3."Telescoped" metadata development Search Systems and EIA —1.Simple consistent interface; 2.Analyze those logs; 3.Prioritize your queries; and 4 Reverse-engineering content and metadata "Guerrilla" EIA —1.K-logs for internal experts; 2.Wikis for groups; 3.Accessing internal expertise through the staff directory; 4.Aggregating staff expertise...and everything else; and 5.Social bookmarking in the enterprise. Rosenfeld’s Roadmap breaks down information architecture design into these four major tracks, and plots concrete steps within each track over time with the goal of making information easier to find across silos [11].

Enterprise Information Architecture Domain

EIA Reference Architecture

Information Architecture Domain of Oracle EA consists of Data Realms, and Capability Model, which will be discussed in this section [13]: Information Architecture Domain: Data Realms Different types and structures of data exist within an organization. They can be categorized into the following seven data realms [13]: Transaction data are business transactions that are captured during business operations and processes. Metadata, defined as ―data about the data‖, is the description of the data. Master data refers to the enterprise-level data entities that are of strategic value to an organization. Reference data are internally managed or externally sourced facts to support an organization’s ability to effectively process transactions, manage master data, and provide decision support capabilities. Unstructured data make up over 70% of an organization’s data and information assets. They include documents, digital images, geo-spatial data, and multi-media files. Analytical data are derivations of the business operation and transaction data used to satisfy reporting and analytical needs. Big data refer to large datasets that are challenging to store, search, share, visualize, and analyze. The growth of such data is mainly a result of the increasing channels of data in today’s world. A common misconception is that the scope of enterprise information architecture is all information in the enterprise. Although true at an abstract level, in reality, the focus of EIA is on information assets that are deemed to have enterprise significance and that are necessary to achieve effective business change [10]. The Information Reference Model is a framework for describing the relationship between data domains. This model considers five of the sven data domains addressed by [13]. According to Information Reference Model, there are five data domains [6]: • Metadata Domain—is the information that describes the characteristics of each piece of corporate data asset and other entities. • Master Data Domain—Refers to instances of data describing the core business entities. • Operational Data Domain—Also referred to as transactional data capturing data, which is derived from business transactions. • Unstructured Data Domain—Also known as content, typically managed by an enterprise content management application. • Analytical Data Domain—Usually derived through transformation from operational systems to address specific requirements of decision support applications.

The four key components of the EIA Reference Architecture which are addressed by [6] are: Conceptual Architecture—This includes a more detailed level of the Architecture Overview Diagram for the EIA, the description of the data classification criteria, and data domains, and it includes a high-level description of the capabilities, key architecture principles for EIA, and architecture decisions. It also includes IT governance and Information Governance topics. Logical Architecture—This contains the logical EIA description, the EIA Reference Architecture Logical View diagram (including the data domains in the context of this diagram), key aspects of the enterprise information integration, and a high-level description of the information services. Component Model—This is a detailed description of the EIA building blocks and their functionality including a detailed description of the EIA components, a service description (for instance MDM Services, Data Management Services, Metadata Management Services and so on), an information-centric Component Relationship Diagram, and Component Interaction Diagrams (including some exemplary scenario descriptions). Operational Model—This includes the Logical Operational Model (LOM) and Physical Operational Model (POM); information-centric Operational Patterns; Service Qualities applicable for information services; the Cloud Computing delivery model for information services; best practices and integration patterns.

Information Architecture Domain: Capability Model Various capabilities are needed in order to manage the different data types and to process different data structures, or the lack thereof. Following are the key top-level capabilities an organization needs to manage the data and information assets [13]: Enterprise Information Delivery and Sharing addresses how information is propagated directly to its consumers within an organization. Business Intelligence and Data Warehouse provides users and stakeholders insights into the health of the business. Data Integration—Organizations are increasingly dependent on Data Integration to tie together cacophonies of application systems and data stores into cohesive solutions. There is a wide spectrum of Data Integration capabilities that provide coverage from batch-based to real-time Integration needs including. Master Data Management consists of a number of sub-capabilities unique to the management of the master data for an enterprise. Enterprise Data Model is a key discipline to instill within the organization to ensure no one solution drives the data model but rather the enterprise data needs. Content Management is recognized as a key top-level capability to manage content, records, multimedia, and image capture. Data Governance, Quality, and Lifecycle Management ensures that organization only retain information necessary to their longevity and legal compliance. Data Security Management controls whether the right individuals have access to the right information at the right time. Data Technology Management—Organizations will need to develop or engage core data technology management skills to address the increasing amount of raw information that exists in enterprises today.

Future and Next Generation EIA Gartner believes that soon most EA teams will be forced by the business to spend as much time on information architecture as they currently spend on technical architecture. Enterprise Information Architecture must not be limited to the more traditional information capabilities such as data integration or content management [6]. Also Gartner is predicting that enterprise information architecture will be an area of increasing focus and influence [10]. New themes and emerging capabilities are required to deliver the vision of a Next-Generation Enterprise Information Architecture. These must be considered when developing the EIA Reference Architecture [6]: Cloud Computing—To facilitate the role of data and content in the Cloud, this, for instance, will require improved capabilities and even new concepts regarding multi-tenancy, the ease-of-use of programming models, and more flexible scalability properties. Metadata Management—It facilitates business-initiated exploitation of Business and Technical Metadata to gain a pervasive end-to-end insight into coherences in the information infrastructure (for example, data lineage) and also links the business and technical domains. Mashup—Capabilities to deliver data and information for Web 2.0 and other similar situational applications to essentially deliver new functions and insights. Dynamic Warehousing—This addresses the new aspects of Data Warehousing, such as optimizing business processes through real-time information insight and analytics as well as integrating Unstructured Data into the analytical domain. New Trends in Business Analytics and Optimization (BAO)—The Intelligent Enterprises exploits smarter more advanced analytics to optimize business performance.

EIA in Practice : Cases from Real World University of Pittsburgh’s Enterprise-wide information architecture The University of Pittsburgh’s adopted a unique approach to designing an enterprise-wide information architecture and a framework for engaging the University community in Business Process Reengineering (BPR). That approach included building consensus on a general philosophy for information systems, utilizing pattern-based abstraction techniques, applying data modeling and application prototyping, and tightly coupling the information architecture with efforts to reengineer the workplace. [7] BPR methodology identified all business processes in the enterprise. A ―process dump‖ was performed then resultant activities and processes were analyzed and organized the into categories. These categories were reviewed with individual end users and further refined. One of the high-level processes that was in a state of disrepair, the procurement process, then was selected for a pilot reengineering effort. [7] In this approach, the enterprise-wide architecture evolves as user-proposed projects are developed in adherence to the guidelines, standards, and information processing patterns articulated in the architecture. The organizational structures created to support the architecture and to address the prerequisite infrastructure issues continue to evolve. [8] Commonwealth Enterprise Information Architecture Enterprise Information Architecture (EIA) promotes the governance, asset management and sharing of the Commonwealth’s data assets, Implementing EIA strategies under four domains [4]: Data Governance: High-level stewardship and management of data architecture and assets; strategic level planning, monitoring and policy-making to govern the array of data management across the enterprise. Data Asset Management: Management of data asset inventory; metadata defining or describing data assets, including taxonomies, subject areas and information classes; data documentation in metadata repositories. Data Standards: Establishment and maintenance of adopted agreements on common data definitions, specifications and vocabularies for data assets; publication and maintenance of data standards in shared repositories. Data Sharing: Trust framework and legal agreements enabling the exchange of information across organizational domains and source data systems; security, privacy, consent and authorization compliance through applicable law.

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