Business intelligence maturity models: opportunities and recommendations for future investigation - a systematic literature review - part 1

La globalización de la economía representa grandes desafíos. Uno de ellos es la explotación de la información y el conocimiento de la empresa. Convertir datos en información y la información en conocimiento se denomina inteligencia de negocios- BI. Se han desarrollado varias herramientas de BI para apoyar el proceso de toma de decisiones. Los modelos de madurez son una de estas herramientas. Esta investigación tiene como objetivo mostrar en dos partes, lagunas y proponer oportunidades para el avance en este campo. En general, se reveló un predominio de características genéricas y descriptivas. Se detectaron algunas lagunas relacionadas con modelos que pueden adaptarse a segmentos industriales específicos. Este campo todavía ofrece amplias posibilidades para nuevos modelos de investigación y madurez.The economy globalization represents significant challenges. One of them is information exploitation and company knowledge. Converting data into information and information into knowledge is called Business Intelligence – BI. Several BI tools have been established to support the decision-making process. Maturity Models is one of these tools. This research aims to show in two parts, breaches and to propose prospects for the progression of this field. In general, the prevalence of generic and descriptive features was revealed. Some gaps related to models that can be modified to specific industrial sectors were detected. This field offers great promises for new investigations and maturity models

Does the Demographic Factor Impact Enterprise Business Intelligence Maturity Initiaves in Companies in Malaysia?

This chapter proposes an Enterprise Business Intelligence Maturity Model that involves thirteen key process areas (Strategic Management, Performance Measurement, Balanced Scorecard, Information Quality, Data Warehouse, Master Data Management, Metadata Management, Analytical, Infrastructure, Knowledge Management, People, Organization Culture and Change Management). This key objective of this chapter was to investigate impact on demographic factors such as age of BI initiave, organizational size, number of IT/BI employees, type of industry and revenue of the company towards the Enterprise Business Intelligence Initiave. A survey was conducted around 132 companies in this study. Results shows that age of BI initiatives, organizational size and number of IT/BI employees have relationship on BI maturity level while BI maturity level has strong relationship on the revenue of the company. Results above also show that the type of industry has no relationship on the BI maturity level

Factors influencing the quality of decision-making using business intelligence in Hulamin-KZN.

Master’s degree. University of KwaZulu-Natal, Durban.The current study sought to investigate the factors that affect decision-making by use of business intelligence (BI). Specifically, the study was focused on information quality, system quality and BI service quality. Business intelligence uses organisational data, performs analytical functions and provides decision makers with high quality information to support decision-making. This quantitative study, based on the researcher’s experience of BI, was carried out in a selected manufacturing organisation which recently implemented business intelligence in KwaZulu-Natal. The study used a self-administered survey sent out to participants who used business intelligence so as to gather data on their perception of these variables on the quality of decision-making. All the employees of the organisation with sufficient report runs made the population of the study. The collected data came from different levels of employees, namely managers (47%) and nonmanagers (53%) with varying levels of BI experience. The results were imported into SPSS for analysis. The data showed that information quality had a positive significant impact on the quality of decision-making; system quality had a positive significant impact on the quality of decisionmaking; and BI service had a positive significant impact on the quality of decision- making. Thereafter, a conducted multiple linear regression analysis to determine the strength of these variances in influencing decision-making revealed that the three variables explained 65.7% of the variance in the quality of decision-making. Overall, the study found that high quality information, coupled with a high-quality system and good BI service, leads to a higher quality of decisionmaking, and that the impact of BI on decision-making is positive. This finding concurs reviewed literature

Sustainability Reporting Process Model using Business Intelligence

Sustainability including the reporting requirements is one of the most relevant topics for companies. In recent years, many software providers have launched new software tools targeting companies committed to implementing sustainability reporting. But it’s not only companies willing to use their Business Intelligence (BI) solution, there are also basic principles such as the single source of truth and tendencies to combine sustainability reporting with the financial reporting (Integrated Reporting) The IT integration of sustainability reporting has received limited attention by scientific research and can be facilitated using BI systems. This has to be done both to anticipate the economic demand for integrated reporting from an IT perspective as well as for ensuring the reporting of revisable data. Through the adaption of BI systems, necessary environmental and social changes can be addressed rather than merely displaying sustainability data from additional, detached systems or generic spreadsheet applications. This thesis presents research in the two domains sustainability reporting and Business Intelligence and provides a method to support companies willing to implement sustainability reporting with BI. SureBI presented within this thesis is developed to address experts from both sustainability and BI. At first BI is researched from a IT and project perspective and a novel BI reporting process is developed. Then, sustainability reporting is researched focusing on the reporting content and a sustainability reporting process is derived. Based on these two reporting processes SureBI is developed, a step-by-step process method, aiming to guide companies through the process of implementing sustainability reporting using their BI environment. Concluding, an evaluation and implementation assesses the suitability and correctness of the process model and exemplarily implements crucial IT tasks of the process. The novel combination of these two topics indicates challenges from both fields. In case of BI, users face problems regarding historically grown systems and lacking implementation strategies. In case of sustainability, the mostly voluntary manner of this reporting leads to an uncertainty as to which indicators have to be reported. The resulting SureBI addresses and highlights these challenges and provides methods for the addressing and prioritization of new stakeholders, the prioritization of the reporting content and describes possibilities to integrate the high amount of estimation figures using BI. Results prove that sustainability reporting could and should be implemented using existing BI solutions

Business Intelligence Maturity Model voor ziekenhuizen

In de loop der tijd zijn meerdere Business Intelligence Maturity Models (BIMMs) ontwikkeld. Deze scriptie onderzoekt welke goed toepasbaar zijn voor non-profitorganisaties, waaronder ziekenhuizen. Eerst werd onderzocht welke BIMMs bestaan en uit welke facetten zij zijn opgebouwd. Vervolgens zijn de gevonden BIMMs beoordeeld op hun theoretische basis en op de volledigheid en duidelijkheid van het model zelf. Daarna is onderzocht aan welke uitgangspunten BIMMs voor met name ziekenhuizen moeten voldoen. Verder zijn er eisen geformuleerd waaraan huidige BIMMs niet voldoen, maar waaraan ze wel zouden moeten voldoen. Deze uitgangspunten werden afgezet tegen de overgebleven BIMMs, wat resulteerde in een selectie. Op basis van deze modellen is een samengesteld conceptueel BIMM voor ziekenhuizen ontwikkeld, een model dat alle gevonden uitgangspunten zou moeten afdekken. Dit conceptueel BIMM is vervolgens getoetst bij BI-deskundigen van vijf ziekenhuizen. Hieruit is gebleken dat enkele facetten als overbodig beschouwd kunnen worden. Enkele punten waarvan de empirie leert dat ze meegenomen dienen te worden in het BIMM, zijn in het nieuwe conceptuele BIMM voor ziekenhuizen gealloceerd

Avaliação de modelos de maturidade de sistemas de Business Intelligence: caso de estudo TAP Portugal

Classificação ACM: H.4.2 Types of Systems - Decision SupportBusiness Intelligence (BI) representa atualmente uma das áreas de maior investimento por parte das organizações. O interesse demonstrado pelos gestores executivos tem sido crescente e a tomada de decisão suportada por sistemas de informação analíticos tem-se revelado um fator decisivo para elevar a competitividade das empresas. Contudo, não se assiste ainda ao sucesso generalizado das iniciativas de BI pois o grau de satisfação de utilizadores e profissionais de BI fica aquém do potencial que um sistema de BI/Data Warehouse proporciona. Os modelos de avaliação de maturidade podem desempenhar um papel fundamental no sucesso da implementação de um programa de BI. Nesta dissertação é feita uma revisão bibliográfica dos modelos de maturidade de BI e uma análise comparativa, em particular do ponto de vista das dimensões de avaliação de maturidade. A avaliação dos modelos de maturidade consistiu em verificar a sua aplicabilidade num contexto real pelo que se recorre ao caso de estudo como método de validação. Do inquérito realizado na TAP Portugal retiram-se conclusões importantes como a confirmação de que os aspetos associados à organização são considerados mais relevantes para a maturidade do BI do que tecnologia, processos ou pessoas. De acordo com o ensaio realizado para definição de um conjunto de dimensões transversais aos modelos, as mais relevantes para avaliar a maturidade são Valor para a organização e Cultura analítica. Adicionalmente, no caso de estudo verificou-se que a aplicabilidade de um modelo de maturidade de BI (TDWI) contribuiu para que hoje em dia a arquitetura de Data Warehouse seja eficiente e escalável.Business Intelligence (BI) is now one of the areas where companies invest the most. Business executives’ awareness is increasing and decisions supported by analytic information systems are becoming crucial in improving organizations competitiveness. However, the success of BI initiatives is not yet widespread since the satisfaction of both BI users and practitioners is still below the level BI/Data Warehouse systems can provide. Assessment maturity models may play an important role regarding a successful BI program implementation. This dissertation includes a state of the art of BI maturity models and a comparative study, particularly in the maturity model dimensions perspective. The evaluation of the maturity models consisted on the verification of their applicability to a real context, using case study as the validation method. Valuable conclusions were drawn from the survey and interview led at TAP Portugal such as the confirmation that organizational aspects are considered more relevant for BI maturity than technology, processes or people. In accordance with the essay on defining generic dimensions that cover all models, Value to the organization and Analytic culture were the most relevant when assessing BI maturity. Additionally, the case study revealed that the use of a BI maturity model (TDWI) contributed on establishing a more efficient and scalable Data Warehouse architecture

Business intelligence information systems success : a South African study.

Doctor of Philosophy in Information Systems and Technology. University of KwaZulu-Natal. Durban, 2016.Business Intelligence (BI) systems hold promise for improving organisational decision making in South Africa. Additionally, BI systems have become increasingly important over the past few decades and are one of the top spending priority areas of most organisations. Yet till now, the factors influencing the success of BI systems in South Africa have not been fully investigated. The study found no scholarly research for managers and other practitioners to assess post implementation success of BI systems in South Africa. This lack of research may directly affect managers’ not knowing how best to implement BI systems and could thereby delay the successful implementation of BI systems in South African organisations. The study extends that of DeLone and McLean (2003), conducted in developed economies by applying it to a developing economy context, namely South Africa. The DeLone and McLean (2003) model has been widely utilised to study factors that influence information systems (IS) success. This study extends the DeLone and McLean (2003) by adding a user quality factor and suggests a theoretical model consisting of six factors, which are: (1) system quality, (2) service quality, (3) information quality, (4) user satisfaction, (5) individual impact, (6) and user quality. The theoretical model was formulated from the literature review. It was then validated and enhanced through a qualitative study of three interviews with end users of BI systems based in South Africa. The theoretical model was then presented to a panel of experts for verification. A questionnaire survey method was employed as the main method to collect data and to answer the main research question. Statistical analysis methods and Structural Equation Modelling (SEM) with SPSS was used to analyse the data. The results of the hypotheses were mixed. Three suggested that relationships were statistically significant, while the other four did not. The study finds that information quality is significantly and positively related to user satisfaction in a BI system. The results also indicate that user quality is positively related to user satisfaction in a BI system and system quality is positively related to individual impact in a BI system. The results have both managerial and research implications. The results of this study will add value to IS and specifically BI literature. Organisations, which have adopted BI or are planning to adopt BI, can use the important variables of the study to undertake an internal check to find out how they compare in terms of these variables. The unique contribution of this study is the identification of post implementation success factors of BI systems in a South African context. The factors identified also served in providing a set of management guidelines for the BI environment in South Africa