An integrated data analytics process to optimize data governance of non-profit organization

Wang, Chen-Shu; Lin, Shiang-Lin; Chou, Tung‐Hsiang; Li, Boyi

doi:10.1016/j.chb.2018.10.015

Cited by 24 publications

(17 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There is often a lack of established mechanisms for data governance leading to the ad hoc handling of data (Janssen et al 2020). According to Wang et al (2019) it is necessary to develop data governance mechanisms beginning with policy development to define governance goals and strategies, followed by the establishment of organizational data governance structures. Top management support (Gao et al 2015), well-defined roles and responsibilities (Saltz and Shamshurin 2016), and the choice of the data governance approach (Koltay 2016) are considered critical.…”

Section: The Role Of Data Governance With Regards To Organizational Cmentioning

confidence: 99%

“…As the number of people with data science skills as well in-depth domain knowledge is limited (Waller and Fawcett 2013), these insights suggest that data science initiatives should be governed by people with in-depth domain knowledge. According to Wang et al (2019), organizations should develop comprehensive data governance mechanisms, beginning with policy development to define governance goals and strategies, followed by the establishment of organizational data governance structures.…”

Section: The Role Of Data Governance With Regards To Organizational Cmentioning

confidence: 99%

“…Proposition 3. Organizations with an Established Data Governance Capability Are More Likely to Ensure that Organizational Conditions of Data Science are Met Successful data science outcomes require data governance mechanisms beginning with policy development to define governance goals and strategies (Wang et al 2019), followed by the establishment of organizational data governance structures. Top management support (Gao et al 2015), well-defined roles and responsibilities (Saltz and Shamshurin 2016), and the choice of the data governance approach (Koltay 2016) are considered critical.…”

Section: Proposition 2 Organizations With Established Data Governancmentioning

confidence: 99%

See 2 more Smart Citations

Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

Brous¹,

Janssen

2020

Administrative Sciences

View full text Add to dashboard Cite

Organizations are increasingly introducing data science initiatives to support decision-making. However, the decision outcomes of data science initiatives are not always used or adopted by decision-makers, often due to uncertainty about the quality of data input. It is, therefore, not surprising that organizations are increasingly turning to data governance as a means to improve the acceptance of data science decision outcomes. In this paper, propositions will be developed to understand the role of data governance in creating trust in data science decision outcomes. Two explanatory case studies in the asset management domain are analyzed to derive boundary conditions. The first case study is a data science project designed to improve the efficiency of road management through predictive maintenance, and the second case study is a data science project designed to detect fraudulent usage of electricity in medium and low voltage electrical grids without infringing privacy regulations. The duality of technology is used as our theoretical lens to understand the interactions between the organization, decision-makers, and technology. The results show that data science decision outcomes are more likely to be accepted if the organization has an established data governance capability. Data governance is also needed to ensure that organizational conditions of data science are met, and that incurred organizational changes are managed efficiently. These results imply that a mature data governance capability is required before sufficient trust can be placed in data science decision outcomes for decision-making.

show abstract

Section: The Role Of Data Governance With Regards To Organizational Cmentioning

confidence: 99%

Section: The Role Of Data Governance With Regards To Organizational Cmentioning

confidence: 99%

Section: Proposition 2 Organizations With Established Data Governancmentioning

confidence: 99%

See 1 more Smart Citation

Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

Brous¹,

Janssen

2020

Administrative Sciences

View full text Add to dashboard Cite

show abstract

“…Little evidence has been produced so far indicating what actually has to be organized by data governance and what data governance processes may entail [20,21], and many organizations find data governance difficult to implement [22,23]. There appears to be no "one-size-fits-all" approach to data governance [24] and the nuances attached to various domains and organizational types have not yet been extensively described [25,26]. Furthermore, evidence is scant as to the role data governance plays in ensuring the successful outcomes of data science initiatives [18,19].…”

Section: Literature Backgroundmentioning

confidence: 99%

Data Governance as Success Factor for Data Science

Brous

Janssen

Krans

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

More and more, asset management organizations are introducing data science initiatives to support predictive maintenance and anomaly detection. Asset management organizations are by nature data intensive to manage their assets like bridges, dykes, railways and roads. For this, they often implement data lakes using a variety of architectures and technologies to store big data and facilitate data science initiatives. However, the decision-outcomes of data science models are often highly reliant on the quality of the data. The data in the data lake therefore has to be of sufficient quality to develop trust by decision-makers. Not surprisingly, organizations are increasingly adopting data governance as a means to ensure that the quality of data entering the data lake is and remains of sufficient quality, and to ensure the organization remains legally compliant. The objective of the case study is to understand the role of data governance as success factor for data science. For this, a case study regarding the governance of data in a data lake in the asset management domain is analyzed to test three propositions contributing to the success of using data science. The results show that unambiguous ownership of the data, monitoring the quality of the data entering the data lake, and a controlled overview of standard and specific compliance requirements are important factors for maintaining data quality and compliance and building trust in data science products.

show abstract

“…The confusion matrix is developed for the performance estimation. (Wang et al, 2019) developed a Knowledge Discovery in Databases (KDD) method for the data analysis of National Health Insurance Administration (NHIA) call center in Taiwan. The rapid decision helps to provide the public requirements in the finest quality.…”

Section: Data Mining Approachesmentioning

confidence: 99%

Review of Data Mining and Data Warehousing Implementation in E-Governance

Jha¹

2020

IJISRT

View full text Add to dashboard Cite

Nowadays, the role of information and communication technology plays an important part in the Indian government administration. Because of the government, it has to handle a large amount of citizen’s data and recent increases of online accessibility. However, due to the lack of unstructured knowledge, it develops complexity while analyzing the data. The issues in the e-governance can be solved using efficient data mining methods. In this review, various researches on the works in the data mining as well as data warehouse in e-governance are investigated and compared. The significances and issues in the e-governance are discussed for the future enhancement. Consequently, the illiteracy rate and literacy rate after the development of e-governance in India is measured. Moreover, the few performance evaluations of e-governance application are an investigation for the validation of the efficiency. The meta-analysis of execution has been done in this review.

show abstract

An integrated data analytics process to optimize data governance of non-profit organization

Cited by 24 publications

References 18 publications

Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

Data Governance as Success Factor for Data Science

Review of Data Mining and Data Warehousing Implementation in E-Governance

Contact Info

Product

Resources

About