Marianne Hickey

Casebook: A Cloud-Based System of Engagement for Case Management

Casebook embraces social and collaboration technology, analytics, and intelligence to advance the state of the art in case management from systems of record to a system of engagement for knowledge workers. It addresses complex, inefficient work practices, information loss during hand offs between teams, and failure to learn from previous case experience. Intelligent agents help people adapt to changing work practices by tracking process evolution and providing updates and recommendations. Social collaboration surrounding cases integrates communication with information and supports collaborative roadmapping to enable people to work as they collaborate, thus accelerating how quickly and accurately they handle cases.

Speech-as-data technologies for personal information devices

Abstract For small, portable devices, speech input has the advantages of low-cost and small hardware, can be used on the move or whilst the eyes & hands are busy, and is natural and quick. Rather than rely on imperfect speech recognition we propose that information entered as speech is kept as speech and suitable tools are provided to allow quick and easy access to the speech-as-data records. This paper summarises our work on the technologies needed for these tools – for organising, browsing, searching and compressing the stored speech. These technologies go a long way towards giving stored speech the characteristics of text without the associated input problems.

A solution for identifying the root cause of problems in IT change management

The reuse of knowledge acquired by operators to diagnose failures in Information Technology (IT) infrastructures has potential to decrease the recurrence of failures and, consequently, reduce possible losses and maintenance costs. Nevertheless, existing solutions to support failure diagnosis lack of flexibility to adapt to a constantly changing IT environment. As a result, diagnostic is performed in an ad hoc and static fashion, which hampers the reuse of knowledge to solve similar failures affecting different elements of an IT infrastructure. To bridge this gap, in this paper we propose an extension of Common Information Model (CIM), supported by a conceptual solution for the identification of the root causes of problems, adaptable to changes in the target infrastructure and applicable to similar failures. Experiments carried out considering typical failures during the deployment of IT changes provide evidence about the efficacy of the proposed solution1.

A model for decision support in business-driven IT service portfolio management using SLA-dependent criteria and under uncertainty

This paper presents a model to support decision making for investments in IT services. As such it contributes to IT service portfolio management. Investment options are analyzed and ranked according to a utility index estimated from possible positive and negative business impact of IT services due to IT Service Level Agreement (SLA) compliance and violations. The Balanced Scorecard framework is used to infer the linkage between IT and business activities. The approach takes uncertainty into account by means of utility theory and interval arithmetic. Numerical illustrations encompassing tangible SLA-dependent criteria demonstrate how the approach may be of use.

Prioritizing Information Technology Service Investments under Uncertainty

We explore the challenge of selecting the best among a set of alternative IT investments. Solving this problem is important since the difference between alternative investment options may be drastic in terms of business results, both positive and negative. The resulting model takes as input a set of investment alternatives and a parameterized description of IT services, and provides as output a Preference Index for each alternative. The solution takes into account such characteristics as epistemic and aleatory uncertainty as well as the decision makers attitude toward risk. Through a case study and a sensitivity analysis, we conclude that the model is useful in practice and robust; we also describe its domain of validity.

Value-based IT decision support: Towards a formal business value model for steering IT-business alignment

The problem: Value creation and delivery are recurrently mentioned in the literature as important drivers for effective business and IT decision making. However, the knowledge concerning value creation is commonly offered in vague, informal and subjective terms and, for that reason, corporate executives and IT staff depend mostly on personal expertise, background and intuition to practice what theory recommends.

LearnIT: enhanced search and visualization of IT projects

Year over year, the majority of IT projects fail to deliver the required features on time and budget, costing billions of dollars. The task of portfolio managers is to make a selection of prospective projects, given only rough cost-benefit estimates for them. In this paper, we present LearnIT, a tool to aid portfolio managers in their portfolio selection job. LearnIT identifies past or current IT projects within a large database on the basis of similarity to a given IT project or proposal. Furthermore, LearnIT is capable of being trained with expert user feedback, via example. LearnIT also provides a means for visualizing and making evident any relationships between IT projects in order to help IT managers design templates for future projects thus increasing their success rate.

Leveraging IT project lifecycle data to predict support costs

There is an intuitive notion that the costs associated with project support actions, currently deemed too high and increasing, are directly related to the effort spent during their development and test phases. Despite the importance of systematically characterizing and understanding this relationship, little has been done in this realm mainly due to the lack of proper tooling for both sharing information between IT project phases and learning from past experiences. To tackle this issue, in this paper we propose a solution that, leveraging existing IT project lifecycle data, is able to predict support costs. The solution has been evaluated through a case study based on the ISBSG dataset, producing correct estimates for more than 80% of the assessed scenarios1.

IT Project Variables in the Balance: A Bayesian Approach to Prediction of Support Costs

In the context of Information Technology (IT) project management, it is commonly accepted that the costs associated with support actions are strongly influenced by the effort spent during their development and test phases. Despite the importance of systematically characterizing and understanding this relationship, little has been done in this realm mainly due to the lack of proper mechanisms for both sharing information between IT project phases and learning from past experiences. To tackle this issue, we present a Bayesian model to perform support cost predictions based on data from software development and test phases. In addition, we present a qualitative and quantitative analysis of the model, in order to demonstrate its effectiveness and efficiency, and also discuss its potentialities and limitations.

A framework to support investment decisions using multi-criteria and under uncertainty in IT service portfolio management

This paper presents a framework to support decision making for investments in IT services. Investment options are analyzed and ranked according to utility indices estimated from possible positive and negative business impact of IT services. The approach takes multiple criteria and uncertainty into account by means of multi-attribute utility theory and interval arithmetic. The business impact of IT services is estimated by the analysis of criteria in one of three groups: Purely financial, Service Level Agreement (SLA) dependent and Subjective. Numerical illustrations encompassing tangible and intangible criteria demonstrate how the approach may be of use.