The connectivity or loop between the architecture-operations is referred to here as the ArcOps, which is important for enabling the mapping and spritz of information between tracery and operations for constructive visualization making and value realisation. The ArcOps pipeline aims to provide much needed end-to-end tracery design, execution, governance, observability, provenance, and traceability. This vendible discusses the ArcOps pipeline and toolchain examples or options from the information-driven adaptive EA (AEA) framework. The ArcOps pipeline uses data, analytics, and intelligence ( AI, generated AI, machine learning) capabilities for sourcing tracery data from operations, then converting it into violating information for visualization making well-nigh transition and future states of the enterprise tracery including roadmaps, implementation, assurance, governance and investment plans. The AEA framework has been published in the “Adaptive Enterprise Tracery as Information: Architecting Intelligent Enterprises.
1. Introduction
Enterprise tracery (EA) is a key enabler of planning, implementing and governing changes in the diamond of structure, behaviour, value proposition and purpose of an enterprise for adaptation. Being adaptive refers to the worthiness to handle changes for desired efficiency and innovation. Efficiency refers to sustaining and improving the enterprise. Innovation refers to continuously growing and transforming an enterprise. Traditional slow and heavy documentation driven EA practices are challenged by the fast pace digital version and visualization information needs. Further, in the current context of digitalisation, while the EA can be used for planning digital initiatives, it is moreover important to squint at transforming the traditional EA practice itself into an information-driven adaptive EA (AEA) for architecting digitally enabled intelligent enterprises. The AEA framework has been published in the “Adaptive Enterprise Tracery as Information: Architecting Intelligent Enterprises” typesetting (Gill 2022a).
My older vendible “Adaptive enterprise tracery practice – data to decisions” (Gill 2022b) discussed the adaptive enterprise tracery as information (AEAI) to support version decisions and deportment for desired efficiency and innovation (outcomes and impacts). The information driven AEAI tideway uses data, analytics, and intelligence (e.g. Artificial Intelligence, Generated Artificial Intelligence, Machine Learning) for architecting intelligent enterprises. This vendible focuses on the connectivity of enterprise tracery (Arc) and operations (Ops), which are often considered disjointed capabilities of an enterprise. Architects spend significant time in modelling the perceived or unsupportable current state of the tracery information via strategic stakeholders’ workshops while having limited to no use of the information from the Merchantry and IT operations. Operations present the true and dynamic current state of the enterprise tracery in use, which must be looped when to the enterprise tracery and vice versa. The connectivity or loop between the architecture-operations is referred to here as the ArcOps, which is important for enabling the mapping and spritz of information between tracery and operations for constructive visualization making and value realisation. This vendible discusses the ArcOps pipeline and toolchain examples or options from the information-driven adaptive EA (AEA) framework.
2. The AEA Framework
The AEA framework is organised into six interacting tracery information layers or domains: interaction, human, technology, facility, environment, and security. The AEA focuses on the information-driven adaptive sufficiency to continuously scan and sense the internal and external environment tracery wideness layers for data well-nigh known and unknown events (complex event processing), changes or disruptions, interpret and analyse the placid data, and decide and respond based on intuition and rationale to expected and unexpected changes (threats and or opportunities) for adaptations (Figure 1) (Gill 2022a). This refers to the use of data to visualization in the ArcOps tideway in the AEA framework, which involves sourcing tracery data from operations, then converting it into violating information for visualization making well-nigh transition and future states of the enterprise tracery including roadmaps, implementation, assurance, governance and investment plans.
3. The ArcOps
The ArcOps is an integrated pipeline of enterprise tracery (Arc) and operations (Ops) that aims to provide end-to-end tracery design, execution, governance, observability, provenance, and traceability. ArcOps is not an volitional to the existing AIOps, BizOps, DevOps, DataOps, MLOps etc. practices, rather ArcOps is an information-driven overarching pipeline, which can encompass these existing practices as a sub-set of the ArcOps. The ArcOps pipeline can be supported via variegated tools or toolchain. ArcOps is organised into 3 key parts: Architecture, Operations and ArcOps information fabric or enterprise knowledge graph (see Figure 2).
Figure 2. The ArcOps: Unfluctuating Architecture-Operations Pipeline
Architecture
Architecture data or information can be organised into six interacting tracery information layers or domains: interaction, human, technology, facility, environment, and security. The technie information can be stored in variegated tracery repositories or tracery information systems. Architects can use variegated tools for handling the tracery information. For instance, they can use Archi or Miro for generating the conceptual tracery information models and storing them in the tracery information systems or repository or library. They can moreover turn these conceptual models into data and store them in the property graph or semantic knowledge graph tools such as Neo4j or Stardog. This data can be remoter processed via the analytics tools such as Tableau and Power BI. Further, architects can moreover process the data using the AI/GAI/ML tools such as ChatGPT and MonkeyLearn. Architects can moreover use specialist EA software tools such as Abacus, Jalapeno or LeanIX to manage the tracery data and information. EA specific tools have started providing and/or integrating with some of the data, analytics and AI/GAI/ML capabilities or tools including operations (business-IT information systems), to support the information-driven ArcOps pipeline.
Operations
Operations data or information can be stored in both merchantry and IT operations information systems. Merchantry operations refers to very merchantry transactions, history and related data or information that can be stored in merchantry specific enterprise systems (e.g. Salesforce CRM, SAP ERP), merchantry using data stores (e.g. SQL Server, Mongo DB), data warehouses, data lakes, data lakehouse (e.g. Databricks) and merchantry intelligence reporting information systems. IT operations refer to IT service management, which refers to managing of hardware and software assets, services and associated operational support and maintenance functions (e.g. help desk, configuration management, incident management, problem management). Operations information can be stored in variegated IT operations information systems such as configuration management databases (CMDBs), windfall registers and service catalogs etc. There are several IT operations tools such as ServiceNow, JIRA and Confluent etc. The unfluctuating merchantry and IT operations provide the true current state of the tracery related resources and services in operations. Such information can be linked to tracery information via the enterprise knowledge graph.
The ArcOps: Enterprise Knowledge Graph
The ArcOps information fabric or enterprise knowledge graph is a semantic metadata layer (Information Catalog) that connects the information residing in tracery and operations information systems or repositories. This does not require establishing a large and centralised ArcOps data warehouse. Rather, the tracery and operations information stay in their relevant repositories or information systems. However, the ArcOps information is unfluctuating via the semantic metadata driven enterprise knowledge graph layer. There are several ways to map, integrate or connect the tracery and operations information elements. However, “Product Service” is a key information element that can serve as an integration point between the tracery and operations elements. The ArcOps pipeline can be remoter unfluctuating to AI/GAI assisted search and discovery tools to provides wangle to integrated ArcOps information. The data can be processed (interpret and analyse) using the data analytics and intelligence capabilities for generating the violating information. The information can be used for making informed decisions (decide and respond) for adaptation. Like any other information asset, ArcOps information needs to be governed and quality assured.
4. Conclusion
This vendible provided an overview of the integrated ArcOps tideway from the “Adaptive Enterprise Tracery as Information: Architecting Intelligent Enterprises” book. This provides new perspectives in terms of provisioning the operations information (current state architecture) for making version decisions well-nigh the transition and future state architectures. It moreover pointed out the use of an enterprise knowledge graph layer to connect the tracery and operations information elements in the ArcOps. Finally, it is mentioned that the “Product Service” is a key information element that can serve as an integration or mapping point between the tracery and operations information elements.
