The Challenges of Big Data Analytics in the Mobile Communications Sector

Saldžiūnas, Kęstutis; Skyrius, Rimvydas

doi:10.15388/ekon.2017.2.11004

Cited by 3 publications

(15 citation statements)

References 7 publications

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“…Big data analytics can yield beneficial information, but some challenges exist, such as (a) managing large sets of data, (b) data silos, (c) data centralization, (d) data protection and privacy, (e) management training, (f) having the right skills to conduct analysis such as data scientists, (g) cost of implementation, and (h) organizational cultural change (Saldžiūnas & Skyrius, 2017). Michael and Miller (2013) argued that leaders would face challenges using big data analytics when trying to understand:…”

Section: Challenges To Implementing Big Data Analyticsmentioning

confidence: 99%

“…As the volume of data increases, challenges may arise, with leaders discovering ways to manage the continued growth in data collected to yield more benefits (Saldžiūnas & Skyrius, 2017). For example, law enforcement agencies and other organizations rely on video data generated from closed-circuit television (CCTV) from surveillance used in criminal investigations (Michael & Miller, 2013).…”

Section: Managing Large Datasetsmentioning

confidence: 99%

“…The term data silos describe the isolation or segregation of data collection (Leonard et al, 2018). The term centralized data represents a typical nonsilo environment that consists of multiple integrated systems with a complete dataset of unstructured, semistructured, and structured data (Saldžiūnas & Skyrius, 2017). The term professional silos describe the process by which barriers can exist within an organization between departments, leading to isolation amongst individuals who are supposed to be working collaboratively in the same team rather than working individually (Kitchens et al, 2018).…”

Section: Data Silosmentioning

confidence: 99%

“…Data silos are a frequent occurrence in the telecommunication sector, especially in mobile network operators (MNO) (Saldžiūnas & Skyrius, 2017). The data from a mobile network operator originates from many disparate systems and sources such as (a) device data, (b) cell site data, (c) network data, and (d) back-office data.…”

Section: Data Silosmentioning

confidence: 99%

“…The downside of having a predominantly silo data environment could result in the existence of data duplication in multiple data locations (Saldžiūnas & Skyrius, 2017). Data could be of less value without cleansing, transforming, integrating, and aggregating the data before analyzing it to discover insights (Saggi & Jain, 2018).…”

Section: Data Silosmentioning

confidence: 99%

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Strategies to Implement Big Data Analytics in Telecommunication Organizations

Aneato¹,

Castellanos²

2021

IJCATR

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Information technology (IT) leaders who do not invest in big data projects may struggle to gain a competitive advantage and business insights to improve performance. Grounded in Kotter’s change and Six Sigma models, the purpose of this qualitative multiple case study was to explore strategies IT leaders use to implement big data analytics successfully. The participants comprised 4 IT leaders from 2 telecommunication organizations in the United States of America, who expertly used big data analytics strategies to promote and maximize competitive advantage. Data were collected from semistructured interviews, company documents, and project-related documents. The collected information was examined by utilizing a thematic analysis approach. Four themes emerged from the data analysis process communication, training, employee involvement in decisions, and teamwork strategy. A key recommendation from these findings is for IT leaders to use successful communication strategies to convey the vision and objectives to all organizational levels. The successful communication-strategy can help evaluate business trends, forecasts, improve overall organizational performance and competitive advantage. The implications for positive social change include the potential for job creation, thus catalysing economic growth within communities.

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Section: Challenges To Implementing Big Data Analyticsmentioning

confidence: 99%

Section: Managing Large Datasetsmentioning

confidence: 99%

Section: Data Silosmentioning

confidence: 99%

Section: Data Silosmentioning

confidence: 99%

Section: Data Silosmentioning

confidence: 99%

See 3 more Smart Citations

Strategies to Implement Big Data Analytics in Telecommunication Organizations

Aneato¹,

Castellanos²

2021

IJCATR

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A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

Reddy

Bhattacharjee

Mishra

et al. 2022

Inf Syst E-Bus Manage

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While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted ‘Data Science Strategy’ (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of ‘Data Science’ in discrete for various industry sectors and domains. There is dearth of studies on comprehensive ‘Data Science’ adoption as an umbrella constituting all of its components. The study conducts a “Systematic Literature Review (SLR)” on enablers and barriers affecting the implementation and success of DSS in enterprises. The SLR comprised of 113 published articles during the period 1998 and 2021. In this SLR, we address the gap by synthesizing and proposing a novel framework of ‘Enablers and Barriers’ influencing the success of DSS in enterprises. The proposed framework of ‘Data Science Strategy’ can help organizations taking the right steps towards successful implementation of ‘Data Science’ projects. Supplementary Information The online version contains supplementary material available at 10.1007/s10257-022-00550-x.

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Drivers and Loci of Big Data Innovation: A Review, Synthesis, and Future Research Directions

Radojević,

Heumüller

2023

jim

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Despite a drastic increase in the amount of data collected and stored, the exact role that big data plays in innovation remains a subject of a diverging debate. This paper hence employs a systematic literature review and qualitative analysis techniques to identify, analyze and synthesize extant conceptual contributions on how big data drives the innovation process, where this process takes place, and which theoretical perspectives have shaped the research on big data innovation so far. Our findings reveal that big data innovation primarily takes place cooperatively, in the form of open innovation or within innovation networks, and that it is simultaneously driven by a variety of drivers. However, purposeful applications of big data and analytics for innovating in response to existing but unsatisfied market needs ("market pull") remain comparatively underresearched. Concomitantly, the theoretical lenses of “traditional” innovation management have largely framed the research on big data innovation so far. We coherently integrate these findings within an overarching framework and suggest an extensive future research agenda.

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The Challenges of Big Data Analytics in the Mobile Communications Sector

Abstract: Abstract. The activities of the MNO (Mobile NetworkOperator

Cited by 3 publications

References 7 publications

Strategies to Implement Big Data Analytics in Telecommunication Organizations

Strategies to Implement Big Data Analytics in Telecommunication Organizations

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

Drivers and Loci of Big Data Innovation: A Review, Synthesis, and Future Research Directions

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