Asif Gill Blogs

AI System AI system receive inputs (data), process them via data processing applications, which can use AI algorithms for further advanced processing of data or intermediate outputs for generating final outputs for desired outcomes. These outputs could be classifications, clustering, contents, decisions, detections, insights, predictions or recommendations. There are several examples of AI systems such as Autonomous Vehicles, Chatbots,  Digital Assistants, Face Recognition and Voice Recognition etc.    A. AI System Architecture AI system architecture consists of at least three interconnected layers and underpinning components: applications, data and algorithms.   Figure 1 . AI System Architecture 1. Application  AI system has AI enabled or augmented or embedded applications that receive initial inputs such as data and apply data processing programs. Based on the processing needs, it makes the data ready for further advanced processing using AI algorithms. As such application captures a

Circular economy is about recycling, remanufacturing, reusing  products, services and information for continuous value re-co-creation while reducing waste and enhancing positive economic, environmental and social impacts. This requires to re-conceptualize and re-design the economic ecosystems and business models for optimisation and innovation.  Information management is extremally important to provide intelligence & insights for effective decision making  and actions enabling circular economy. This article presents the following circular information lifecycle management for enabling circular economy.  In circular information lifecycle management (CILM), information can be restored and recycled like another product or service in the circular economy.  The CILM is composed of six key stages: discover & collect, classify & secure, prepare & release, access & use, archive & purge, and restore & recycle.  Circular Information Lifecycle Management (CILM) - Circul

Information or data strategy is a part of the overall corporate or business strategy. Information (including data) is an important strategic business asset. Thus, information assets should be managed and treated in the similar way as we manage other business assets. An actionable information strategy is required to for the effective handling of information across the enterprise, irrespective of their size, for achieving the business goals and reducing risk including compliance to regulatory requirements and standards.   This post provides and discusses a simple and practical template for defining the information strategy as a part of the business or cooperate strategy. Information strategy can be defined at the ecosystem, enterprise, business function, business area or capability level as appropriate to your context and needs. Information strategy has following key 8 elements (Figure 1): Mission & Vision, Levers & Value Driver, Goals & Objective, Strategies & Tactics, C

A Theory of Information Trilogy for Information Ecosystem by Asif Gill Information is/of Everything (IioE) We hear many definitions of information and related concepts such as data, information itself, knowledge and intelligence. Well, all these definitions seem useful from information probability, quantification, measurement, storage, processing and management perspective. We still wonder what is "it" and how to identity "it", which we call "information". This blog defines and describes nature ecosystem inspired definition of information, which is called here a "Theory of Information Trilogy" (ToIT).  This theory is defined with a view to define and identify information in complex and heterogenous information ecosystems.  This theory defines that the information is (1) matter and (2) energy, which has different (3) states or forms. Matter and energy flow in the natural ecosystem. Further, from natural ecosystem, these 3 matter, energy and their

Agile processes are often considered challenging to adopt in larger and formal project development and management environments. It may not be possible and need for an organisation to adopt a pure agile process for a large project. How can organizations adopt agile planning and estimation best practices within their formal and generic process lifecycle (GPL)? This article presents an agile planning and estimation model that can be used for large projects as a guide. Agile Process The core of the agile process is a collection of sprints or small increments. The agile process can be classified into three key categories or stages: ·        Pre-Sprint – focus on the analysis activities of the GPL ·        Sprint – focus on the planning and development activities of the GPL ·        Post-Sprint – focus on the sprint post-mortem and release activities of the GPL.   The agile process has practices that can be executed within a GPL to produce the specific project deliverables. The

Enterprise Architecture (EA) could be considered a descriptive and/or prescriptive design of an enterprise. Traditional approaches to EA heavily  focus on the modelling and documentation aspects and tend to overlook the actual stakeholders and their needs, in particular, when dealing with complex and large digital innovation and transformation. This calls  for the need of an adaptive EA. Based on ISO/IEC/IEEE 42010:2011, an adaptive EA is the fundamental concepts or properties of an adaptive enterprise service system or ecosystem: situated in its environment (e.g. political, economic, sociological, technological, legal and environmental),  embodied in its elements (e.g. interaction, human, technology, facility),  relationships (e.g. type and strength) to each other and its environment, and  in the adaptive principles of its secure design and evolution. The Gill Framework ® V4.0 Adaptive EA requires a fundamental shift the way currently organisations operate their E