ADDRESSING THE ELEPHANT IN THE UNDERGROUND: AN ARGUMENT FOR THE INTEGRATION OF HETEROGENEOUS DATA SOURCES FOR RECONCILIATION OF SUBSURFACE UTILITY DATA

In this paper we address the issue of unreliable subsurface utility information. Data on subsurface utilities are often positionally inaccurate, not up to date, and incomplete, leading to increased uncertainty, costs, and delays incurred in underground-related projects. Despite opportunities for improvement, the quality of legacy data remains unaddressed. We address the legacy data issue by making an argument for an approach towards subsurface utility data reconciliation that relies on the integration of heterogeneous data sources. These data sources can be collected at opportunities that occur throughout the life cycle of subsurface utilities and include as-built GIS records, GPR scans, and open excavation 3D scans. By integrating legacy data with newly captured data sources, it is possible to verify, (re)classify and update the data and improve it for future use. To demonstrate the potential of an integration-driven data reconciliation approach, we present real-world use cases from Denmark and Singapore. From these cases, challenges towards implementation of the approach were identified that include a lack of technological readiness, a lack of incentive to capture and share the data, increased cost, and data sharing concerns. Future research should investigate in detail how various data sources lead to improved data quality, develop a data model that brings together all necessary data sources for integration, and a framework for governance and master data management to ensure roles and responsibilities can be feasibly enacted.

Improving Data Quality and Data Governance Using Master Data Management: A Review

Master data management (MDM) is a method of maintaining, integrating, and harmonizing master data to ensure consistent system information. The primary function of MDM is to control master data to keep it consistent, accurate, current, relevant, and contextual to meet different business needs across applications and divisions. MDM also affects data governance, which is related to establishing organizational actors’ roles, functions, and responsibilities in maintaining data quality. Poor management of master data can lead to inaccurate and incomplete data, leading to lousy stakeholder decision-making. This article is a literature review that aims to determine how MDM improves the data quality and data governance and assess the success of MDM implementation. The review results show that MDM can overcome data quality problems through the MDM process caused by data originating from various scattered sources. MDM encourages organizations to improve data management by adjusting the roles and responsibilities of business actors and information technology (IT) staff documented through data governance. Assessment of the success of MDM implementation can be carried out by organizations to improve data quality and data governance by following the existing framework.

Strategi Pengembangan Sistem Informasi Berbasis E-KTP di Kantor Kelurahan Galur (Studi Kasus Kelurahan Galur Kota Jakarta Pusat)

Intisari— Sistem Informasi adalah suatu sistem yang berada dalam suatu organisasi yang mengelola kebutuhan pengolahan transaksi harian yang mendukung operasi dan juga dalam kegiatan strategi dalam suatu organisasi dengan menyediakan laporan-laporan yang diperlukan. Seiring dengan perkembangan teknologi informasi dan komunikasi yang semakin pesat saat ini sangatlah memungkinkan masyarakat untuk bisa mengakses informasi apa saja yang mereka butuhkan dalam kehidupan sehari-hari, seolah-olah tidak ada batasannya. Salah satu program yang mulai diterapkan sekarang ini oleh Kementrian Dalam Negeri Republik Indonesia adalah dengan memanfaatkan teknologi informasi dalam melakukan pelayanan kepada masyarakat adalah berupa penerapan program Kartu Tanda Penduduk Elektronik atau disebut juga e-KTP. KTP Elektronik adalah dokumen kependudukan yang memuat sistem keamanan / pengendalian baik dari sisi administrasi ataupun teknologi informasi pada data base ke pendudukan nasional. Untuk mendapatkan kelengkapan data dan informasi yang sesuai dengan fokus penelitian maka yang dijadikan teknik pengumpulan data dalam penelitian ini menggunakan kuesioner. Penulis simpulkan bahwa dengan menggunakan pola matriks IFAS dan EFAS yang telah dilakukan maka dapat diketahui kondisi Kelurahan Galur dalam melakukan pengembangan terhadap sistem informasi berbasis E-KTP yaitu dengan kemajuan teknologi dan sudah diterapkannya pemberdayaan pegawai dalam bidang teknologi maka dapat meningkatkan lagi visi dan misi yang mendukung kualitas pelayanan Kelurahan. Kata Kunci— Sistem Informasi, E-KTP, Kelurahan Galur, SWOT. Referensi : [1] M. S. Putra, “Faktor-Faktor Pengembangan Sistem Informasi Akademik Berbasis Web Pada Perguruan Tinggi Swasta Palembang,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), pp. 295-300, 2018. [2] P. P. R. N. 9. T, “Peraturan Presiden Republik Indonesia No 95 Tahun 2018 Tentang Sistem Pemerintahan Berbasis Elektronik,” p. 199, 2018. [3] I. D. a. R. M. M. Z. Syafnel, “Analisis dan Perancangan TataKelola Data sistem Pemerintahan Berbasis Elektronik Domain Master Data Management (MDM) pada Dama Dmbok V2 di Diskominfotik Kbb,” p. 7775–7786, 2019. [4] T. R. V. RONI EKHA PUTERA, “Implementasi Program KTP Elektronik (e-KTP) di Daerah Percontohan,” MIMBAR, pp. 193-201,2011. [5] M. P. Febriharini, “Pelaksanaan Program e KTP Dalam Rangka Tertib Administrasi Kependudukan,” Jurnal Ilmiah UNTAG Semarang, pp. 2302-2752, 2016. [6] I. Irawan, “Pengembangan Sistem Informasi Akademik Universitas Pahlawan Tuanku Tambusai Riau,” J. Teknol. Dan Open Source, pp. 55-66, 2018. [7] S. a. D. S. Paryanta, “Sistem Informasi Administrasi Kependudukan Berbasis Web Desa Sawahan,” IJSE - Indones. J. Softw. Eng, pp. 1-8, 2017. [8] N. W. UTAMI, “Jurnal Enterpreneur,” 26 February 2019. [Online]. Available: https://www.jurnal.id/id/blog/2017-manfaat-faktor-yang-memengaruhi-dan-contoh-analisis-swot/. [Accessed 22 10 2020]. [9] M. A. a. R. Sanjaya, “Strategi Perencanaan dan Pengembangan Program Studi Menggunakan Analisis SWOT (Studi Kasus Program Studi Sistem Informasi ARS University),” pp. 1-8, 2020. [10] O. A. B. D. H. S. a. L. P. P. R. Veriyadna, “Penerapan Analisis SWOT : Studi Kasus Usaha Mahasiswa Creative Puzzle Glass,” pp. 1-6, 2019. [11] A. W. a. Suyudi, “Penerapan Analisis SWOT Dalam Menentukan Strategi Pengembangan Sistem Informasi STIKOM Yos SudarsoPurwokerto,” J. Chem. Inf.Model., pp. 1689-1699, 2018. [12] S. Noor, “Penerapan Analisis Swot dalam Menentukan Strategi Pemasaran Daihatsu Luxio di Malang,” J. INTEKNA, p. 102–209, 2014. [13] G. Pendidikan, “Analisis SWOT : Pengertian, Unsur, Manfaat, Faktor, Contoh, Kelebihan & Kekurangannya Lengkap,” seputar ilmu.com, 2019. [14] S. E. Masri Singarimbun, Metode Penelitian Survai, Jakarta: Pusat LP3ES Indonesia, 1987. [15] W. Surakhmad, Pengantar Penelitian Ilmiah Dasar Metode Teknik, Bandung: Tarsito, 1994. [16] Riduwan, Skala Pengukuran Variabel-Variabel Penelitian, Bandung: Alfabeta, 2009.

Master data management in an iterative approach

Master Data Management (MDM) is a young area of business informatics that concerns consolidation and centralized control of highly important business data distributed over different information systems. Leading IT companies such as IBM, Oracle, Informatica and others offer a wide range of ready-made products for master data management (MDM products). MDM product deployment involves many technical and organizational complications: it is necessary to adapt these products for the specifics of the business, modify business processes, create new data policies, solve se-curity questions, etc. A popular approach to this task is the iterative strategy of MDM deployment, which supposes a step-by-step implementation of master data management based on the real needs of the business organization. In this paper, the notion of an MDM solution is introduced, which is the result of the deployment of MDM in an organization. It includes a specifically adapted MDM product, new regulations for working with data, trained employees, and an up-and-running process of master data management. The main result of the paper is a functional model of master data management intended for the early stages of the development of an MDM solution within the iterative deployment strategy. The purpose of this model is the representation of real business needs in terms of MDM. It is important to un-derstand which MDM components should be implemented first. The paper describes a detailed description of the mod-el components, as well as a portfolio of six real MDM projects analyzed from the viewpoint of the proposed model.

Erfolgsfaktor Data Governance

Von Master Data Management (MDM) versprechen sich Unternehmen Effizienz, Transparenz und Risikominimierung im Umgang mit ihren Stammdaten. MDM soll dazu beitragen, Stammdaten als „Asset“ im Unternehmen zu bewirtschaften. Der vorliegende Beitrag liefert praktische Tipps, wie MDM-Implementierungen nachhaltig gestaltet werden können, damit die Daten einen Beitrag zum Unternehmenserfolg leisten. Er stellt das qualitätsorientierte Data Governance Framework vor. Das Framework stellt sicher, dass bei einer Implementierung alle Aspekte von MDM adressiert werden inkl. strategischer und organisatorischer Fragestellungen. Die konsequente Ausrichtung an der Datenqualität sorgt dafür, dass alle Unternehmensbereiche Stammdaten nutzenstiftend einsetzen können.

Problems and Challenges Related to Advanced Data Analysis in Multi-Site Enterprises

The new generation of industry, i.e. Industry 4.0, pertains to the processing of immense amounts of data, resulting, among other things, from the large-scale use of microcontrollers to control machines, an increase in the scale of automation, the use of the Internet of Things technology — e.g. in sensors installed at different stages of the production process, the implementation of the digital twin concept, and many other technologies designed to collect data (e.g. GPS or RFID). These data are collected in the enterprise’s variety of resources and databases. These data can be a valuable source of information and knowledge if the right approach to advanced data analysis is adopted, which depends, among other things, on the enterprise’s existing IT infrastructure. This paper sets out to present conclusions formulated on the basis of research consisting in the analysis of multinational manufacturing companies’ existing IT infrastructures. Three basic model solutions of IT architecture occurring in multi-site enterprises were identified, which made it possible to identify the main problems stemming from the IT architecture in place and concerning the analysis of data for the needs of company management. Additionally, this paper discusses the challenges faced by multi-site manufacturing companies. One such activity is the modification and expansion of the company’s IT infrastructure, including the implementation of Big Data and Master Data Management (MDM) solutions. The contribution provided by this paper consists in the analysis of the IT infrastructure in large, multi-site enterprises, which enabled the identification of problems and challenges related to advanced data analysis in this type of companies.