An investigation into the implementation factors affecting the success of big data systems

Cato, Patrick; Gölzer, Philipp; Demmelhuber, Walter

doi:10.1109/innovations.2015.7381528

Cited by 17 publications

(12 citation statements)

References 24 publications

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“…As well as establishing data management processes that manage data quality, data governance should also ensure that the organization's data management processes are compliant with laws, directives, policies, and procedures (Wilbanks and Lehman 2012). According to Cato et al (2015), policies and principles should be aligned with business strategies in an enterprise data strategy. Panian (2010) states that establishing and enforcing policies and processes around the management of data should be the foundation of effective data governance practice as using big data for data science often raises ethical concerns.…”

Section: The Role Of Data Governance With Regards To the Organizationmentioning

confidence: 99%

“…Organizations with an Established Data Governance Capability Are More Likely to Be Able to Manage Organizational and Process Changes Introduced by Data Science Outcomes Data governance establishes data management processes which manage data quality (Passi and Jackson 2018) and compliance with relevant laws, directives, and policies (Cato et al 2015). Data governance aligns policies and principles with business strategies in an enterprise data strategy (Cato et al 2015). Proposition 4 proposes that organizations with mature data governance are more likely to be able to manage changes introduced by data science decision outcomes.…”

Section: Propositionmentioning

confidence: 99%

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

Brous¹,

Janssen

2020

Administrative Sciences

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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.

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Section: The Role Of Data Governance With Regards To the Organizationmentioning

confidence: 99%

Section: Propositionmentioning

confidence: 99%

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

Brous¹,

Janssen

2020

Administrative Sciences

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“…For successful implementation for the BDA investments, the organization should be clear on what they want to achieve with the alignment to the overall objectives of the organization (Eybers & Hattingh 2017). The characteristics of the organization affect the successful implementation of Big Data systems within the organization including the subcategory related to close collaboration between IT and Business as well asorganizational structure (Cato, Golzer, & Demmelhuber, 2016).…”

Section: Organizationmentioning

confidence: 99%

“…As this phenomenon is occurring in various organizations at the moment, the concept has attracted the attention of managersand executives toward implementingand investing in Big Data projects. Due to the large volume of data generated, the current infrastructure in several areas such as technology capabilities, organizational structure, and processing capacity often fails to deal with the requirements in capturing, storing and even processing Big Dataefficiently ( where the implementation of such projectssometimes requiresnew technical and organizational approaches (Cato, Golzer, & Demmelhuber, 2016). This often requires organizations to be ready for additional requirements in those areas that should be aligned with the increased complexity of Big Data characteristics (namely Volume, Velocity, and Variety) to ensure the achievement of a successful implementation, and the ability to gain a suitable value from the Big Data investments(Eybers & Hattingh 2017; Alexandros Labrinidis & H. V. Jagadish 2012).…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Systematic Performance on Critical Success Factors Categories for Big Data Analytics

Al-Sai,

Abdullah,

Husin

et al. 2019

IJEAT

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Big Data could be used in any industry to make effective data-driven decisions. The successful implementation of Big Data projects requires a combination of innovative technological, organizational, and processing approaches. Over the last decade, the research on Critical Success Factors (CSFs) within Big Data has developed rapidly but the number of available publications is still at a low level. Developing an understandingof the Critical Success Factors (CSFs) and their categoriesare essential to support management in making effective data-driven decisions which could increase their returns on investments.There islimited research conducted on the Critical Success Factors (CSFs) of Big DataAnalytics (BDA) development and implementation.This paper aims to provide more understanding about the availableCritical Success Factors (CSFs) categoriesfor Big Data Analytics implementation and answer the research question (RQ) “What are the existing categories of Critical Success Factors for Big Data Analytics”.Based on a preliminary Systematic Literature Review (SLR) for the available publications related to Big Data CSFs and their categories in the last twelve years (2007-2019),this paper identifiesfive categoriesfor Big Data AnalyticsCritical Success Factors(CSFs), namelyOrganization, People, Technology, Data Management, and Governance categories.

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“…A lack of trust in data science projects can often be attributed to the lack of data quality, and the success of data science projects is often highly reliant on the quality of the data being used [8][9][10]. There is no single factor defining the successful outcomes of a data science project [11,12], but recently data governance has gained traction by many organizations as being important for ensuring quality and compliance in data science outcomes [11,13]. However, it remains unclear how data governance contributes to the success of data science outcomes, leading to calls for more research in this area [11,14,15].…”

Section: Introductionmentioning

confidence: 99%

Data Governance as Success Factor for Data Science

Brous

Janssen

Krans

2020

Lecture Notes in Computer Science

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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.

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An investigation into the implementation factors affecting the success of big data systems

Cited by 17 publications

References 24 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

A Preliminary Systematic Performance on Critical Success Factors Categories for Big Data Analytics

Data Governance as Success Factor for Data Science

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