Using Big Data Analytics in Information Technology (IT) Service Delivery

2013 ◽  
Vol 1 (1) ◽  
pp. 6 ◽  
Author(s):  
Fung Han Ping ◽  
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Service Delivery ◽  
Data Analytics ◽  
Big Data Analytics ◽  
It Service

scholarly journals SIDeKa: The Role of Information Technology for Knowledge Creation

2017 ◽  
Vol 2 (6) ◽  
pp. 570
Author(s):  
Cungki Kusdarjito
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Data Collection ◽  
Knowledge Creation ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Village Level ◽  
The Village

The advancement of big data analytics is paving the way for knowledge creation based on very huge and unstructured data. Currently, information is scattered and growth tremendously, containing many information but difficult to be interpreted. Consequently, traditional approaches are no longer suitable for unstructured data but very rich in information. This situation is different from the role of previous information technology in which information is based on structured data, stored in the local storage, and in more advanced form, information can be retrieved through internet. Meanwhile, in Indonesia data are collected by many institutions with different measurement standard. The nature of the data collection is top-down, carried out by survey which is expensive yet unreliable and stored exclusively by respective institution. SIDeKa (Sistem Informasi Desa dan Kawasan/Village and Regional Information System), which are connected nationally, is proposed as a system of data collection in the village level and prepared by local people. Using SIDeKa, data reliability and readiness can be improved at the local level. The goals of the SIDeKa is not only local people have information in their hand such as poverty level, production, commodity price, the area of cultivated land, and the outbreak of diseases in their village, but also they have information from the neighboring villages or event at the national level. For government, data reliability will improve the policy effectiveness. This paper discusses the implementation and role of SIDeKa for knowledge creation in the village level, especially for the agricultural activities which has been initiated in 2015.Keywords: big data analytics; SIDeKa;  unstructured data.

scholarly journals Information technology capability and firm performance in the era of Big Data analytics: Evidence from the United States

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Jahidur Rahman ◽  
Ning Zhao
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Firm Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The United States ◽  
Rank Test ◽  
Control Groups ◽  
It Capability ◽  
And Control

Research Question: This study investigates the relationship between information technology (IT) capability and firm performance in the 2010s, the era of big data analytics (BDA), in the context of US companies. Motivation: With the evolution of business intelligence and the proliferation of analytic tools that further improve IT capability, it is more important than ever to understand whether firms with stronger IT capabilities perform better. Idea: After categorizing firms into pairs of IT leaders and control groups, the performance of each pair of firms in each group was compared. Data: All data are publicly available from Compustat and InformationWeek (IW) 500. Tools: The Wilcoxon signed-rank test and regression analysis were used to examine how the performance of IT leaders and control groups changed during the 2010–2017 period. Findings: This results show no significant relationship between a firm’s IT capability and its performance in the sample of US companies during this period. Contribution: This study will help academicians and practitioners to better understand how the adoption and application of BDA derived from IT capability affects firm performance.

Prospects for Implementing Big Data Analytics into the Auditing Profession

Auditor ◽  
2021 ◽  
pp. 40-47
Author(s):  
Nataliya Kazakova ◽  
Margarita Mel'nik ◽  
E. Dudorova
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Digital Transformation ◽  
Competence Model ◽  
Technology Knowledge ◽  
Gradual Development

The article analyzes modern trends in the digital transformation of audit, problems and prospects for the use of big data analytics in audit and consulting. The providers of the development of the “roboaudit” direction are large audit companies that invest a lot of resources in the digital transformation of audit. To understand the benefits of digital transformation of auditing, a gradual development of the professional IT worldview among young auditors is required, which takes into account the new competence model of the qualification exam for auditors, which allows assessing information technology knowledge in different modules and at all stages of the exam.

scholarly journals IT in the workspace – The need for digital transformation

2018 ◽  
Vol 12 (1) ◽  
pp. 952-965
Author(s):  
Tiberiu Telegescu
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Digital Economy ◽  
Digital Transformation ◽  
Demand Model ◽  
Organizational Goals ◽  
It Service ◽  
Traditional Markets ◽  
Digital Environments

Abstract We live today in a world irreversibly and comprehensively impacted by the digital economy, either by its ability to transform traditional markets into digital ones or by creating new ones altogether. In such a digitized environment, agility, speed, adaptability and innovation through disruption are key attributes for organizations to maximize, in order to enable their workforce to be effective and efficient and achieve the organizational goals. The paper aims to outline that organizations have, as part of their digital transformation journey, opportunities to embrace the benefits that the new tools of the digital economy, such as cloud, mobility and big data analytics bring. The most immediate advantage of such a change is the ability to reduce the total IT spend, making the organization more frugal and allowing resources to be diverted into other ( potentially revenue generating ) parts of the business. As a method of proving that, we’ve taken several case study examples of enterprises both achieving savings by implementing new tools into their business. However, businesses often do not settle just for that but instead use part of the newly freed up resources to enhance their digital environments. Digital tools enable teams to work easily together and allow the workforce to access any and all of the organization’s resources from anywhere and at any time they wish. This “consumerized” demand model, where IT service is customized every step of the way is what lead enterprises to change their internal IT work frame and thus migrate from the legacy “one size fits all “ model, to the new, flexible, scalable and customizable model, through the usage of new capabilities like Cloud, Big Data, Analytics, Internet of Things (IoT), Mobility.

scholarly journals Development of Big Data Analytics Model

2019 ◽  
Vol 4 (1) ◽  
pp. 14-25
Author(s):  
Saiful Rizal
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Big Data ◽  
Data Storage ◽  
Data Structures ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Complex Data ◽  
Survey Paper

The development of information technology produces very large data sizes, with various variations in data and complex data structures. Traditional data storage techniques are not sufficient for storage and analysis with very large volumes of data. Many researchers conducted their research in analyzing big data with various analytics models in big data. Therefore, the purpose of the survey paper is to provide an understanding of analytics models in big data for various uses using algorithms in data mining. Preprocessing big data is the key to turning big data into big value.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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