Big data team process methodologies: A literature review and the identification of key factors for a project's success

Saltz, Jeffrey S.; Shamshurin, Ivan

doi:10.1109/bigdata.2016.7840936

Cited by 67 publications

(60 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. According to Janssen et al (2020), data governance contains mechanisms to encourage preferred behavior.…”

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

confidence: 99%

“…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. Proposition 3 proposes that organizations having an established data governance capability are more likely to be in a position to meet organizational conditions.…”

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

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: Proposition 2 Organizations With Established Data Governancmentioning

confidence: 99%

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

“…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

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

“…Although frameworks have been developed for data mining and data science systems, ADM systems have not been specifically handled yet [25,26]. Defining the process of ADM system development may allow the designers of these systems to better understand how biases are introduced in the development phase.…”

Section: Introductionmentioning

confidence: 99%

Preventing Algorithmic Bias in the Development of Algorithmic Decision-Making Systems: A Delphi Study

Aysolmaz

İren²,

Dau

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

In this digital era, we encounter automated decisions made about or on behalf of us by the so called Algorithmic Decision-Making (ADM) systems. While ADM systems can provide promising business opportunities, their implementation poses numerous challenges. Algorithmic bias that can enter these systems may result in systematical discrimination and unfair decisions by favoring certain individuals over others. Several approaches have been proposed to correct erroneous decision-making in the form of algorithmic bias. However, proposed remedies have mostly dealt with identifying algorithmic bias after the unfair decision has been made rather than preventing it. In this study, we use Delphi method to propose an ADM systems development process and identify sources of algorithmic bias at each step of this process together with remedies. Our outputs can pave the way to achieve ethics-by-design for fair and trustworthy ADM systems.

show abstract

Big data team process methodologies: A literature review and the identification of key factors for a project's success

Cited by 67 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

Data Governance as Success Factor for Data Science

Preventing Algorithmic Bias in the Development of Algorithmic Decision-Making Systems: A Delphi Study

Contact Info

Product

Resources

About