Business-driven IT Management

Business-driven IT management (BDIM) aims at ensuring successful alignment of business and IT through thorough understanding of the impact of IT on business results, and vice versa. In this dissertation, we review the state of the art of BDIM research and we position our intended contribution within the BDIM research space along the dimensions of decision support (as opposed of automation) and its application to IT service management processes. Within these research dimensions, we advance the state of the art by 1) contributing a decision theoretical framework for BDIM and 2) presenting two novel BDIM solutions in the IT service management space. First we present a simpler BDIM solution for prioritizing incidents, which can be used as a template for creating BDIM solutions in other IT service management processes. Then, we present a more comprehensive solution for optimizing the business-related performance of an IT support organization in dealing with incidents. Our decision theoretical framework and models for BDIM bring the concepts of business impact and risk to the fore, and are able to cope with both monetizable and intangible aspects of business impact. We start from a constructive and quantitative re-definition of some terms that are widely used in IT service management but for which was never given a rigorous decision: business impact, cost, benefit, risk and urgency. On top of that, we build a coherent methodology for linking IT-level metrics with business level metrics and make progress toward solving the business-IT alignment problem. Our methodology uses a constructive and quantitative definition of alignment with business objectives, taken as the likelihood – to the best of one’s knowledge – that such objectives will be met. That is used as the basis for building an engine for business impact calculation that is in fact an alignment computation engine. We show a sample BDIM solution for incident prioritization that is built using the decision theoretical framework, the methodology and the tools developed. We show how the sample BDIM solution could be used as a blueprint to build BDIM solutions for decision support in other IT service management processes, such as change management for example. However, the full power of BDIM can be best understood by studying the second fully fledged BDIM application that we present in this thesis. While incident management is used as a scenario for this second application as well, the main contribution that it brings about is really to provide a solution for business-driven organizational redesign to optimize the performance of an IT support organization. The solution is quite rich, and features components that orchestrate together advanced techniques in visualization, simulation, data mining and operations research. We show that the techniques we use - in particular the simulation of an IT organization enacting the incident management process – bring considerable benefits both when the performance is measured in terms of traditional IT metrics (mean time to resolution of incidents), and even more so when business impact metrics are brought into the picture, thereby providing a justification for investing time and effort in creating BDIM solutions. In terms of impact, the work presented in this thesis produced about twenty conference and journal publications, and resulted so far in three patent applications. Moreover this work has greatly influenced the design and implementation of Business Impact Optimization module of HP DecisionCenter™: a leading commercial software product for IT optimization, whose core has been re-designed to work as described here

Managing Technical Debt in Agile Software Development Projects

One of the key reasons that agile software development methods have gained popularity in recent years is because they enable organizations to produce software quickly to meet the needs of various stakeholders. However, this focus on delivering software quickly often encourages practitioners to incur technical debt – design or implementation constructs that are expedient in the short term but set up a technical context that can make future changes more costly or impossible. Worldwide, technical debt is estimated to be a trillion-dollar problem. This has prompted significant interest from both researchers and practitioners. In this dissertation, I present two essays that advance our knowledge of the causes of technical debt in agile software development projects and that offer potential solutions to manage the most important of these causes of technical debt. In my first essay, I conduct a ranking-type Delphi study of information technology (IT) project managers and software developers to identify and prioritize the most important causes of technical debt in agile software development projects. The findings from this study provide a verified list of 55 causes of technical debt in agile software development projects and offer 13 potential techniques to manage the causes of technical debt that were most important to the IT project managers and software developers in this study. In my second essay, I conduct a randomized experiment to examine the impact of software developers’ construal level, a cognitive process, on the unintentional accumulation of technical debt in software development projects. The findings from this experiment suggest that software developers at a high construal level are more likely to focus on developing the architecture or design than software developers at a low construal level. Collectively, the findings from these two essays deepen our understanding of the intentional and unintentional causes of technical debt in agile software development projects. Further, the findings offer potential techniques to manage the most important causes of technical debt for IT project managers and software developers

High and dry: institutional impediments to effective drought management and relief in the 1995-96 and 1998 Oklahoma droughts

Thesis (M.R.C.P.)--University of Oklahoma, 2001.Includes bibliographical references (leaves 162-178).This thesis examines the abilities of government entities and non-governmental organizations (NGOs) in Oklahoma to work in concert using institutional and organizational learning precepts to effectively plan for and mitigate the effects of drought. Drawing from academic literature in the areas of organizational learning, drought prediction, planning and mitigation and Oklahoma agricultural production, specifically in the areas of wheat and cattle operations, and newspaper articles in the areas of Oklahoma drought prediction, planning and mitigation, Oklahoma agricultural production, heat wave and wildfire it paints a more complete portrait of drought mitigation in Oklahoma. It develops this portrait with interviews of key individuals. Finally, survey results from a University of Oklahoma drought study provide insight into the details of mitigating drought in Oklahoma from the perspective of fire, emergency management and agricultural staffs. The droughts of 1995-96 and 1998 forced intricate inter-agency planning and mitigation between, for example, the Oklahoma Department of Civil Emergency Management, the Oklahoma Water Resources Board, the Oklahoma Department of Agriculture, and non-governmental organizations such as Feed the Children. The state government failed to experience significant learning on the double loop level, although some individual agencies, did experience single loop learning. One attempt at learning occurred during the year between the two droughts, 1997, when the state of Oklahoma authored a drought plan which this study finds seriously lacking. The plan only suggests responses; does not address responses for consistently recurring problems such as water shortages; does not contain specific, detailed instruction for implementing responses and suggests collaborations between agencies and entities that no longer exist. It provides no one agency with directorial powers. As far as one can determine from a comparison of historical data from both droughts, the plan made little to no difference in actual mitigation

n-Dimensional Prediction of RT-SOA QoS

Service-Orientation has long provided an effective mechanism to integrate heterogeneous systems in a loosely coupled fashion as services. However, with the emergence of Internet of Things (IoT) there is a growing need to facilitate the integration of real-time services executing in non-controlled, non-real-time, environments such as the Cloud. As such there has been a drive in recent years to develop mechanisms for deriving reliable Quality of Service (QoS) definitions based on the observed performance of services, specifically in order to facilitate a Real-Time Quality of Service (RT-QoS) definition. Due to the overriding challenge in achieving this is the lack of control over the hosting Cloud system many approaches either look at alternative methods that ignore the underlying infrastructure or assume some level of control over interference such as the provision of a Real-Time Operating System (RTOS). There is therefore a major research challenge to find methods that facilitate RT-QoS in environments that do not provide the level of control over interference that is traditionally required for real-time systems. This thesis presents a comprehensive review and analysis of existing QoS and RT-QoS techniques. The techniques are classified into seven categories and the most significant approaches are tested for their ability to provide QoS definitions that are not susceptible to dynamic changing levels of interference. This work then proposes a new n-dimensional framework that models the relationship between resource utilisation, resource availability on host servers, and the response-times of services. The framework is combined with real-time schedulability tests to dynamically provide guarantees on response-times for ranges of resource availabilities and identifies when those conditions are no longer suitable. The proposed framework is compared against the existing techniques using simulation and then evaluated in the domain of Cloud computing where the approach demonstrates an average overallocation of 12%, and provides alerts across 94% of QoS violations within the first 14% of execution progress