Reorganizing Business Analysis in an Information Technology Environment
Keyword
business analysisstrategic analysis
SWOT analysis
process analysis
Pareto diagram
business requirements
business systems analysis
Metadata
Show full item recordAbstract
This project was initiated to identify changes needed for the existing structure of the business analysis process and the organization of Business Analysts within the Information Technology (IT) department of a major financial institution. The organization currently experiences a large number of quality issues that are found after the products are implemented rather than during project Initiation, Planning or Execution phases. This results in re-work costs, shortage of resources for strategic initiatives and issues with both employee morale and customer satisfaction. Management has identified weak business analysis processes as a key driver in the high number of resource hours spent on day-to-day unplanned issues. Analysis of data collected from interviews conducted with a cross-section of the IT staff were used to identify areas to be considered for process improvement. The current state was researched using data obtained from the interview process and data analyzed and prioritized using Cause and Effect Analysis. Pareto and Tornado analysis provided further insights into the data. Using the results of the data analysis, some potential short-term and long-term solutions were selected to address identified weaknesses, and potentially reduce time spent on unanticipated non-discretionary tasks, thereby increasing the availability of resources to address the organization’s key initiatives.Description
Presented to the Faculty of the University of Alaska Anchorage in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCETable of Contents
Final Project Report / Final Project Report with research details / Interview Questions / Data with Cause and Effect and Pareto Analysis / SWOT and Matrix Analysis Graphics / Tornado Risk Analysis and Graphic / POPIT Impact Analysis and Graphic / Final PowerPoint Presentation / Project Lessons Learned / Narrative on Knowledge Areas / Project Management Plan / Risk Register /; Project Schedule and WBS / Requirements Traceability Matrix / Change Management Log / Stakeholder Management Plan / Project Charter / Sponsor Letter of Support / Digital Media Files of ProjectDate
2015-05-01Publisher
University of Alaska AnchorageType
ReportCollections
Related items
Showing items related by title, author, creator and subject.
-
A Note on Harmonic Analysis of Geophysical Data with Special Reference to the Analysis of Geomagnetic StormsSome geophysical characteristics tend to have a fixed distribution relative to the sun. An example is the distribution of air temperature on an ideal earth that is perfectly symmetrical (e.g., in its pattern of land and water) about its axis of rotation. In such a case the geophysical characteristic at any fixed station on the earth undergoes a daily variation that depends only on local time (and latitude and season). This simple pattern of daily change may be modified by intrinsic changes in the solar influences on the earth. The harmonic components of the daily variation at any station may in this case undergo phase changes, in some respects corresponding to Doppler shifts of frequency in optical or sonic phenomena. Care is then needed if the results of harmonic analysis are to be properly interpreted. Such interpretation is discussed with reference to the parts Dst and DS of the magnetic storm variations. Like caution must be observed in cases where the amplitude of a harmonic variation changes,with fixed phase.
-
Using rate transient analysis and bayesian algorithms for reservoir characterization in hydraulically fractured horizontal gas wells during linear flowMulti-stage hydraulically fractured horizontal wells (MFHWs) are currently a popular method of developing shale gas and oil reservoirs. The performance of MFHWs can be analyzed by an approach called Rate transient analysis (RTA). However, the predicted outcomes are often inaccurate and provide non-unique results. Therefore, the main objective of this thesis is to couple Bayesian Algorithms with a current production analysis method, that is, rate transient analysis, to generate probabilistic credible interval ranges for key reservoir and completion variables. To show the legitimacy of the RTA-Bayesian method, synthetic production data from a multistage hydraulically fractured horizontal completion in a reservoir modeled after Marcellus shale reservoir was generated using a reservoir (CMG) model. The synthetic production data was analyzed using a combination of rate transient analysis with Bayesian techniques. Firstly, the traditional log-log plot was produced to identify the linear flow production regime, which is usually the dominant regime in shale reservoirs. Using the linear flow production data and traditional rate transient analysis equations, Bayesian inversion was carried out using likelihood-based and likelihood-free Bayesian methods. The rjags and EasyABC packages in statistical software R were used for the likelihood-based and likelihood-free inversion respectively. Model priors were based (1) on information available about the Marcellus shale from technical literature and (2) hydraulic fracture design parameters. Posterior distributions and prediction intervals were developed for the fracture length, matrix permeability, and skin factor. These predicted credible intervals were then compared with actual synthetic reservoir and hydraulic fracture data. The methodology was also repeated for an actual case in the Barnett shale for a validation. The most substantial finding was that for all the investigated cases, including complicated scenarios (such as finite fracture conductivity, fracturing fluid flowback, heterogeneity of fracture length, and pressure-dependent reservoir), the combined RTA-Bayesian model provided a reasonable prediction interval that encompassed the actual/observed values of the reservoir/hydraulic fracture variables. The R-squared value of predicted values over true values was more than 0.5 in all cases. For the base case in this study, the choice of the prior distribution did not affect the posterior distribution/prediction interval in a significant manner in as much as the prior distribution was partially informative. However, the use of noninformative priors resulted in a loss of precision. Also, a comparison of the Approximate Bayesian Computation (ABC) and the traditional Bayesian algorithms showed that the ABC algorithm reduced computational time with minimal loss of accuracy by at least an order of magnitude by bypassing the complicated step of having to compute the likelihood function. In addition, the production time, number of iterations and tolerance of fitting had a minimal impact on the posterior distribution after an optimum point--which was at least one-year production, 10,000 iterations and 0.001 respectively. In summary, the RTA-Bayesian production analysis method implemented in relatively easy computational platforms, like R and Excel, provided good characterization of all key variables such as matrix permeability, fracture length and skin when compared to results obtained from analytical methods. This probabilistic characterization has the potential to enable better understanding of well performance, improved identification of optimization opportunities and ultimately improved ultimate recovery from shale gas resources.
-
Analysis of the effects of online homework on the achievement, persistence, and attitude of developmental mathematics studentsThis dissertation summarizes a study of the use of online homework with developmental mathematics students at the University of Alaska Fairbanks. To address the problem of high failure rates in developmental mathematics courses this study investigated the relationship between online homework and academic achievement, persistence, and attitude. Special focus was placed on non-traditional and Alaska Native students. A matched pair experimental design was employed. The independent variable was homework type and the dependent variables were achievement, persistence, and attitude. Nineteen sections of developmental mathematics, six instructors, and 423 student participants were involved. The main effect of homework type was not statistically significant to any of the dependent variables. However, the effect of the interaction between homework type and course level was significant (p = 0.005). Upon further analysis it was found that one of the four levels (beginning algebra) had significantly higher post-test scores when online homework was assigned. The interaction effects of homework type/ Native status and homework type/ non-traditional status were not statistically significant on any of the dependent variables. Also, results from homework questionnaires were compared. In general, students rated paper homework slightly higher than online homework. Instructors rated online homework higher than students did. Non-traditional students scored paper homework higher than online homework. The conclusion of this study is that while students have a slightly more favorable attitude toward paper homework, online homework in conjunction with graded paper quizzes and face-to-face instruction does not have a negative effect on achievement or persistence.
