The More, the Merrier?

Presthus, Wanda; Ghinea, Gheorghiţă; Utvik, Ken-Robin

doi:10.4018/jbir.2012040103

Cited by 4 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Olbrich et al (2012) study made no distinction between the types of BI projects, attempted to include a wide as possible spectrum of industries, and limited the number of participants per industry. Other researchers (Presthus et al 2012;Yeoh & Koronios 2010) identified similar factors that contributed to BI implementation success in different industries.…”

Section: Hypothesis 2 Sub : Alignment Of Project Specific Critical Success Factors Between Projectsmentioning

confidence: 92%

“…Over the last five years, there have been several empirical studies on CSFs in BI (Yeoh et al 2007;Hawking & Sellitto 2010;Yeoh & Koronios 2010;Olbrich et al 2012;Presthus et al 2012). In these studies, committed top manager support, source system data quality and user involvement emerged consistently as the most important CSFs.…”

Section: Critical Success Factors For Business Intelligencementioning

confidence: 99%

“…Several recent studies have investigated the outcomes of organisational BI projects by using the CSF approach (Yeoh, Gao & Koronios 2007;Hawking & Sellitto 2010;Yeoh & Koronios 2010;Olbrich, Pöppelbuß & Niehaves 2012;Presthus, Ghinea & Utvik 2012).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Critical success factors for business intelligence in the South African financial services sector

Dawson

Belle

2013

S. Afr. j. inf. manag.

View full text Add to dashboard Cite

Background: Business intelligence (BI) has become an important part of the solution to providing businesses with the vital decision-making information they need to ensure sustainability and to build shareholder value. Critical success factors (CSFs) provide insight into those factors that organisations need to address to improve new BI projects’ chances of success.

Objectives: This research aimed to determine which CSFs are the most important in the financial services sector of South Africa.

Method: The authors used a Delphi-technique approach with key project stakeholders in three BI projects in different business units of a leading South African financial services group.

Results: Authors regarded CSF categories of ‘committed management support and champion’,‘business vision’, ‘user involvement’ and ‘data quality’ as the most critical for BI success.

Conclusions: Researchers in the BI field should note that the ranking of CSFs in this study only correlate partially with those a European study uncovered. However, the five factors the authors postulated in their theoretical framework ranked in the seven highest CSFs. Therefore, they provide a very strong validation of the framework. Research in other industries and other emerging economies may discover similar differences and partial similarities. Of special interest would be the degree of correlation between this study and future, and similar emerging market studies. Practitioners, especially BI project managers, would do well to check that they address the CSFs the authors uncovered before undertaking BI projects.

show abstract

Section: Hypothesis 2 Sub : Alignment Of Project Specific Critical Success Factors Between Projectsmentioning

confidence: 92%

Section: Critical Success Factors For Business Intelligencementioning

confidence: 99%

See 1 more Smart Citation

Critical success factors for business intelligence in the South African financial services sector

Dawson

Belle

2013

S. Afr. j. inf. manag.

View full text Add to dashboard Cite

Objectives: This research aimed to determine which CSFs are the most important in the financial services sector of South Africa.

Method: The authors used a Delphi-technique approach with key project stakeholders in three BI projects in different business units of a leading South African financial services group.

show abstract

“…Further, the information given by the traditional BI is commonly delivered in static reports with rows and columns which in the past is sufficient and acceptable for support the decision making within organization. Nevertheless, since rapid development of data science has continued immensely, particularly on the frontiers of machine learning and deep learning, nowadays companies seek a new solution to explore raw data combined with powerful and sophisticated analytic tools to gain insights for better decision making (Presthus & Canales, 2015). Their decisions are no longer have to be made in the row or column but more into visual based on evident experiments and more accurate forecasts.…”

Section: Introductionmentioning

confidence: 99%

Data Storytelling with Dashboard: Accelerating Understanding Through Data Visualization in Financial Technology Company Case Study

Suprata

2021

Metris

View full text Add to dashboard Cite

In the rapid development many organisation rely on context data to support as well as to assist its decision making process. Consequently, Business Intelligence (BI), Dashboard, and Data Visualization emerged as primary tools in early 1990s as a way to help practitioners, data analyst, and data scientist to present context data into an actionable information for decision making process. However, despite its robust and powerful tools, recent study done by Kaggle’s survey in 2017 resulted that in the last five years, many companies were not able to create effective data-driven dashboard due to complex dataset, poor dashboard design, and insufficient storytelling. Hence, understanding of who is going to use dashboard, choosing which data and metrics to visualize in the right context, knowing how to convey information, driving engagement, and persuading audiences are essential in current business practices. This study is aimed to help practitioners to understand the impact of effective dashboard can have on decision making process, to design leveraging dashboard, and to present the dashboard in storytelling. A literature study is performed to gather all relevant information resulted in guidelines for dashboard creator. Case study in financial technology company is applied to experiment and to test the guidelines for assisting dashboard creator to present data-driven insight to the stakeholder.

show abstract

Political Sentiment Mining

Bele

Panigrahi

Srivastava

2020

Cognitive Analytics

View full text Add to dashboard Cite

Investigations on sentiment mining are mostly ensued in the English language. Due to the characteristics of the Indian languages tools and techniques used for sentiment mining in the English language cannot be applied directly to text in Hindi languages. The objective of this paper is to extract the political sentiment at the document-level from Hindi blogs. The authors could not find any literature about extracting sentiments at the document-level from Hindi blogs. They extracted opinion about one of India's very famous leaders who was a prominent face in the national election of 2014. They prepared the datasets from Hindi blogs reviews. They purposed the lexicon and machine learning technique to classify the sentiment. Their purposed method used four steps: (1) Crawling and preprocessing the blog reviews; (2) Extracting reviews relevant to the query using the Vector Space Model (VSM); (3) Identifying sentiment at the document level using the Lexicon method, and (4) Measuring the result using the Machine learning technique. Their experimental result demonstrates the effectiveness of our algorithms.

show abstract

The More, the Merrier?

Cited by 4 publications

References 10 publications

Critical success factors for business intelligence in the South African financial services sector

Critical success factors for business intelligence in the South African financial services sector

Data Storytelling with Dashboard: Accelerating Understanding Through Data Visualization in Financial Technology Company Case Study

Political Sentiment Mining

Contact Info

Product

Resources

About