A review and future direction of agile, business intelligence, analytics and data science

Larson, Deanne; Chang, Victor

doi:10.1016/j.ijinfomgt.2016.04.013

Cited by 332 publications

(243 citation statements)

References 17 publications

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“…However the small-scale experiment model is a low risk alternative with good potential for small quick wins. Though project process is sometimes based on CRISP-DM, sometimes on software development (Larson & Chang, 2016), AA Jumpstarts show sufficiently distinctive characteristics to warrant independent consideration. A final observation is that it is unclear in the literature what the step after Jumpstart might be; where should an organization on the AA journey go next?…”

Section: Discussionmentioning

confidence: 99%

“…This presents challenges both to conventional analytical processes (ask the IT department to prepare a report) and intuition-based decision-making. A method similar to CRISP-DM (Wirth & Hipp, 2000) is often adopted, though some researchers combine this kind of approach with system development methods such as agile development (Larson & Chang, 2016).…”

Section: The Jumpstart Projectmentioning

confidence: 99%

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The advanced analytics Jumpstart: definition, process model, best practices

Rose

Berndtsson

Mathiason

et al. 2017

JISTEM

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Companies are encouraged by the big data trend to experiment with advanced analytics and many turn to specialist consultancies to help them get started where they lack the necessary competences. We investigate the program of one such consultancy, Advectas -in particular the advanced analytics Jumpstart. Using qualitative techniques including semi structured interviews and content analysis we investigate the nature and value of the Jumpstart concept through five cases in different companies. We provide a definition, a process model and a set of thirteen best practices derived from these experiences, and discuss the distinctive qualities of this approach.

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Section: Discussionmentioning

confidence: 99%

Section: The Jumpstart Projectmentioning

confidence: 99%

The advanced analytics Jumpstart: definition, process model, best practices

Rose

Berndtsson

Mathiason

et al. 2017

JISTEM

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“…BI helps organizations to make real-time and quick decisions through data, and it prevents a great number of problems and errors [20], [21]. Thus, BI solutions offer the ability to extract, cleans and aggregate data from multiple operational systems in a single data warehouse [21].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, BI solutions offer the ability to extract, cleans and aggregate data from multiple operational systems in a single data warehouse [21]. These solutions enable organizations to improve decision-making and require processes, skills, technology and data [20]. BI systems combine data gathering and storage and knowledge management with analytical tools so that they offer complex and competitive information for decision-makers [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Importance of Data Factors of Data Quality Model in the Business Intelligence Area

Hejazi¹,

Abdolvand²,

Harandi³

2017

IJTEF

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Abstract-Business Intelligence (BI) is defined as the intelligent transfer of data from all variety of sources with a view to providing a real time quantification of business performance. Moreover, by utilizing BI senior managers are able to make better-informed decisions, guided by the integration of comprehensive information from all facets of an organization in real time. Further, the quality of corporate decision making is directly dependent upon the quality of data. Quality dimensions have proven to be of importance in evaluating how an organization understands and manages its data. Hence, in this study we employed the data quality model of Wang and Strong as our benchmark, and by questioning experts five additional factors (Usefulness, Efficiency, Availability, Navigation, and Usability) were added to the model to improve the reference model. Furthermore, the importance of these factors (and how they relate to each other, in terms of their dimensions) will be measured by the AHP method and an expert panel. Finally, we will reveal how the findings demonstrate that the intrinsic, contextual, representational and accessibility IQs in terms of their dimensions and reputation, accuracy, believability, and reliability, proved to be the most important factors.. Index Terms-Business intelligence, data, data quality, data quality model.

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“…Many other applications and business opportunities revolving around the concept of the proposed framework can be utilized. Another example is in analytics and data science (Hashem et al, 2016;Larson & Chang, 2016) based on mobile crowd sensing, in which the directory services can be improved by crediting the energy valuation in the directory for an individual device. Big-data applications (Newman, Chang, Walters, & Wills, 2016;Yaqoob et al, 2016) can employ a tailored version of the proposed agentoriented framework following the rules and incentive unit of the environment.…”

Section: Applications and Business Prospectsmentioning

confidence: 99%

Directory-based incentive management services for ad-hoc mobile clouds

Yousafzai

Chang

Gani

et al. 2016

International Journal of Information Management

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Mobile cloud computing is envisioned as a promising approach to augment the computational capabilities of mobile devices for emerging resource-intensive mobile applications. This augmentation is generally achieved through the capabilities of stationary resources in cloud data centers. However, these resources are mostly not free and sometimes not available. Mobile devices are becoming powerful day by day and can form a self-organizing mobile ad-hoc network of nearby devices and offer their resources as on-demand services to available nodes in the network. In the ad-hoc mobile cloud, devices can move after consuming or providing services to one another. During this process, the problem of incentives arises for a node to provide service to another device (or other devices) in the network, which ultimately decreases the motivation of the mobile device to form an ad-hoc mobile cloud. To solve this problem, we propose a directory-based architecture that keeps track of the retribution and reward valuations (in terms of energy saved and consumed) for devices even after they move from one ad-hoc environment to another. From simulation results, we infer that this framework increases the motivation for mobile devices to form a selforganizing proximate mobile cloud network and to share their resources in the network.

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A review and future direction of agile, business intelligence, analytics and data science

Cited by 332 publications

References 17 publications

The advanced analytics Jumpstart: definition, process model, best practices

The advanced analytics Jumpstart: definition, process model, best practices

Assessing the Importance of Data Factors of Data Quality Model in the Business Intelligence Area

Directory-based incentive management services for ad-hoc mobile clouds

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