Organizations are looking for ways to harness the power of big data (BD) to improve their decision making. Despite its significance the effects of BD on decision-making quality has been given scant ...attention in the literature. In this paper factors influencing decision-making based on BD are identified using a case study. BD is collected from different sources that have various data qualities and are processed by various organizational entities resulting in the creation of a big data chain. The veracity (manipulation, noise), variety (heterogeneity of data) and velocity (constantly changing data sources) amplified by the size of big data calls for relational and contractual governance mechanisms to ensure BD quality and being able to contextualize data. The case study reveals that taking advantage of big data is an evolutionary process in which the gradually understanding of the potential of big data and the routinization of processes plays a crucial role.
Big Data Technologies: A Survey Oussous, Ahmed; Benjelloun, Fatima Zahra; Ait Lahcen, Ayoub ...
Journal of King Saud University. Computer and information sciences,
10/2018, Volume:
30, Issue:
4
Journal Article
Peer reviewed
Open access
Developing Big Data applications has become increasingly important in the last few years. In fact, several organizations from different sectors depend increasingly on knowledge extracted from huge ...volumes of data. However, in Big Data context, traditional data techniques and platforms are less efficient. They show a slow responsiveness and lack of scalability, performance and accuracy. To face the complex Big Data challenges, much work has been carried out. As a result, various types of distributions and technologies have been developed. This paper is a review that survey recent technologies developed for Big Data. It aims to help to select and adopt the right combination of different Big Data technologies according to their technological needs and specific applications’ requirements. It provides not only a global view of main Big Data technologies but also comparisons according to different system layers such as Data Storage Layer, Data Processing Layer, Data Querying Layer, Data Access Layer and Management Layer. It categorizes and discusses main technologies features, advantages, limits and usages.
With the upsurge of data traffic due to the change in customer behavior towards the use of telecommunications services, fostered by the current global health situation (mainly due to Covid-19), the ...telecommunications operators have a golden opportunity to create new sources of revenues using Big Data Analytics (BDA) solutions. Looking to setting up a BDA project, we faced several challenges, notably, in terms of choice of the technical solution from the plethora of the existing tools, and the choice of the governance methodologies for governing the project and the data. The majority of research documents related to the telecommunications industry have not addressed BDA project implementation from start to finish. The purpose of this study focuses on a BDA telecommunications project, namely, Project’s Governance, Architecture, Data Governance and the BDA Project’s Team. The last part of this study presents useful BDA use cases, in terms of applications enabling revenue creation and cost optimization. It appears that this work will facilitate the implementation of BDA projects, and enable telecommunications operators to have a better understanding about the fundamental aspects to be focused on. It is therefore, a study that will contribute positively toward such goal.
Data Feminism D'Ignazio, Catherine; Klein, Lauren F
03/2020
eBook
Open access
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.
The open access edition of this book was made possible by generous funding from the ...MIT Libraries.
Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.
Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”
Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.
•We define what is meant by big data.•We review analytics techniques for text, audio, video, and social media data.•We make the case for new statistical techniques for big data.•We highlight the ...expected future developments in big data analytics.
Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.
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