Big Data: New Opportunities and New Challenges [Guest editors' introduction]

Michael, Katina; Miller, Keith

doi:10.1109/mc.2013.196

Cited by 253 publications

(126 citation statements)

References 10 publications

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“…Other studies have used Twitter to reliably detect and track Lyme Disease, H1N1, depression, and other illnesses (Chew & Eysenback, 2010;De Choudhury, Counts, & Horvitz, 2013;Salathé, Freifeld, Mekaru, Tomasulo, & Brownstein, 2013;St Louis & Zorlu, 2012). In the private sector, corporations are increasingly using big data to inform decision making in the domains of supply chains, workforce management, and consumer marketing (Michael & Miller, 2013).…”

Section: Beyond Survey Methodsmentioning

confidence: 99%

Using wellbeing for public policy: Theory, measurement, and recommendations

Adler¹,

Seligman²

2016

Intnl. J. Wellbeing

183

115

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Indicators of social progress are the primary drivers of public policy. If existing economic measures of prosperity are complemented with wellbeing metrics that better capture changes in individuals' quality of life, decision makers will be better informed to assess and design policy. The science of wellbeing has yielded extensive knowledge and measurement instruments during more than three decades. We review the existing wellbeing literature and answer three questions: (1) What is wellbeing? (2) How do we measure wellbeing? And, importantly, distinguishing this review from previous ones, (3) How do we use wellbeing metrics to assess and design policy? We suggest that the science of wellbeing is empirically mature enough to complement economic assessments of national progress. We build on existing work to provide recommendations on metrics and new, specific policies for societal wellbeing.

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Section: Beyond Survey Methodsmentioning

confidence: 99%

Using wellbeing for public policy: Theory, measurement, and recommendations

Adler¹,

Seligman²

2016

Intnl. J. Wellbeing

183

115

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“…Hence, an organization's potential to analyze these different forms of voluminous data is paramount and readiness for these analytics is now at centre stage. Michael and Miller (2013) note that the dramatic growth of data within organizations continues to overpower the traditional analytic tools and requires software vendors to offer new data analytical solutions to handle the massive volumes of data, which are also known as Big Data. The Big Data concept may be looked at as the formation of datasets that continuously expand so much that it becomes chal-lenging to manage using existing database management concepts and tools (Singh & Singh, 2012).…”

mentioning

confidence: 99%

“…However, he argues that even though these traditional environments continue to advance, many new more complex types of data have now emerged into what businesses ought to use in order to analyze and build on what they already know. Additionally, Michael & Miller (2013), Wielki (2013) observed that due to increasing use of technology in all business units, new data are being created and generated at high speed in organizations and end up outclassing the traditional analytics techniques and tools. This implies that to leverage on these Big Data, organizations need to improvize means that will lead to change of skills, leadership, structures, technologies and architectures, hence, calling for readiness to change.…”

mentioning

confidence: 99%

“…This implies that to leverage on these Big Data, organizations need to improvize means that will lead to change of skills, leadership, structures, technologies and architectures, hence, calling for readiness to change. Michael & Miller (2013), Wielki (2013) have also noted that despite all the buzz surrounding Big Data, organizations continue to generate data from variety of sources. Singh and Singh (2012) also noted that in organizations that leverage IT, data are now coming from various sources and in different formats making such organizations to be data rich, but knowledge poor.…”

mentioning

confidence: 99%

“…However, like any new technological innovations, heterogeneous challenges may impede organizations from realizing the full benefits of Big Data (Michael & Miller, 2013). Using and analyzing Big Data may involve many new techniques and technologies needed for working with unstructured data, streaming data, social data, machine data that may not be available in organizations.…”

mentioning

confidence: 99%

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Developing countries organizations’ readiness for Big Data analytics

Kalema

Mokgadi

2017

Problems and Perspectives in Management

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Regardless of the nature, size, or business sector, organizations are now collecting burgeoning various volumes of data in different formats. As much as voluminous data are necessary for organizations to draw good insights needed for making informed decisions, traditional architectures and existing infrastructures are limited in delivering fast analytical processing needed for these Big Data. For success organizations need to apply technologies and methods that could empower them to cost effectively analyze these Big Data. However, many organizations in developing countries are constrained with limited access to technology, finances, infrastructure and skilled manpower. Yet, for productive use of these technologies and methods needed for Big Data analytics, both the organizations and their workforce need to be prepared. The major objective for this study was to investigate developing countries organizations' readiness for Big Data analytics. Data for the study were collected from a public sector in South Africa and analyzed quantitatively. Results indicated that scalability, ICT infrastructure, top management support, organization size, financial resources, culture, employees' e-skills, organization's customers' and vendors are significant factors for organizations' readiness for Big Data analytics. Likewise strategies, security and competitive pressure were found not to be significant. This study contributes to the scanty literature of Big Data analytics by providing empirical evidence of the factors that need attention when organizations are preparing for Big Data analytics.

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